EFFECTS OF LAND-APPLICATION OF BIOENERGY RESIDUALS ON ECOSYSTEM SERVICES AND SOIL-WATER-PLANT RELATIONS IN FLORIDA

AGROECOSYSTEMS

By

JOEL EDUARDO REYES CABRERA

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2017

© 2017 Joel Eduardo Reyes Cabrera

To Rafaela J. Cabrera and Pedro P. Reyes

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ACKNOWLEDGMENTS

I thank Dr. Ramon G. Leon and Dr. John E. Erickson for all their guidance,

patience, and friendship throughout this three year journey. I am grateful to Dr. Maria

Silveira, Dr. Diane Rowland, and Dr. Kelly Morgan for serving on my committee. They

always provided me with excellent advice and references that made my experiments

more scientifically sound.

This project was funded by the USDA-NIFA competitive grant number 2012-

67009-19596, for which I am grateful.

I am deeply thankful to Andrew Schreffler for field and lab assistance in the

PSREU and Dr. Erickson laboratory, for helping me out to sort, pick, and scan roots. I

also thank Mike Dozier, Robert Murrell, and Sharon Howell for their support in field

activities at the WFREC in Jay, Florida. I am thankful to Neeta Soni for her

encouragement to pursue this doctorate and her great friendship over the years.

I am grateful to all the people that collaborated along the years with my

experiments in the field either at Citra or Jay. Special thanks to Kayla Thomason,

Jeffrey Fedenko, Rezzy Manning, Victor Guerra, Jose Lopez, Rocio van der Laat,

Washington Bravo, Wenwen Liu, and Ignacio Leon.

I thank my family and friends for their support during the last five years of

graduate school.

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TABLE OF CONTENTS page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................ 7

LIST OF FIGURES .......................................................................................................... 9

LIST OF ABBREVIATIONS ........................................................................................... 10

ABSTRACT ................................................................................................................... 11

CHAPTER

1 INTRODUCTION .................................................................................................... 14

2 BIOCHAR CHANGES SHOOT GROWTH AND ROOT DISTRIBUTION OF SOYBEAN DURING EARLY VEGETATIVE STAGES ............................................ 19

Background ............................................................................................................. 19 Materials and Methods............................................................................................ 20

Experimental Design ........................................................................................ 21 Soil and Root Analysis ..................................................................................... 23

Data Analysis ................................................................................................... 24 Results .................................................................................................................... 25

Soil Characteristics and Water Dynamics......................................................... 25 Aboveground Plant Characteristics .................................................................. 26 Root Characteristics ......................................................................................... 27

Discussion .............................................................................................................. 27 Biochar Effects on Soil Water Retention and Plant Growth .............................. 27

Biochar Effects on Root Growth and Distribution ............................................. 30 Summary ................................................................................................................ 31

3 CONVERSION FROM BAHIAGRASS TO ELEPHANTGRASS INCREASES YIELD, WATER USE EFFICIENCY, AND REDUCES NO3-N LEACHING IN WARM-SEASON PERENNIAL GRASS FIELDS FOR BIOMASS PRODUCTION . 39

Background ............................................................................................................. 39

Materials and Methods............................................................................................ 43

Study Site ......................................................................................................... 43 Experimental Design ........................................................................................ 43 Data Collection ................................................................................................. 44 Statistical Analysis ............................................................................................ 47

Results .................................................................................................................... 48 Environmental Conditions ................................................................................. 48 Biomass Yield ................................................................................................... 48

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Soil Moisture Dynamics .................................................................................... 49

Evapotranspiration and Water Use Efficiency .................................................. 49 Drainage ........................................................................................................... 50

Nitrate Leaching ............................................................................................... 51 Discussion .............................................................................................................. 51

Yield, Evapotranspiration, and Water Use Efficiency ....................................... 51 Soil Moisture Dynamics .................................................................................... 53 Drainage and Nitrate Leaching ......................................................................... 55

Summary ................................................................................................................ 56

4 COMPARISON OF BIOMASS PRODUCTION AND WATER AND NITRATE DYNAMICS BETWEEN SWEET SORGHUM AND A COTTON-PEANUT ROTATION IN THE SOUTHEASTERN UNITED STATES ..................................... 66

Background ............................................................................................................. 66 Materials and Methods............................................................................................ 69

Site Description and Experimental Design ....................................................... 69 Cultural Practices ............................................................................................. 70

Data Analysis ................................................................................................... 73 Results .................................................................................................................... 74

Biomass Accumulation ..................................................................................... 74

Soil Moisture Dynamics .................................................................................... 75 NO3-N Concentration in Leachates .................................................................. 76

Discussion .............................................................................................................. 77 Biochar Effects on Soil Moisture Retention ...................................................... 78 Nitrate Concentration in Leachates .................................................................. 80

Summary ................................................................................................................ 81

5 CONCLUSIONS ..................................................................................................... 87

LIST OF REFERENCES ............................................................................................... 89

BIOGRAPHICAL SKETCH ............................................................................................ 99

7

LIST OF TABLES

Table page 2-1 Chemical analysis of biochar used in the study. ................................................. 33

2-2 Summary of ANOVA for root length density (RLD), root surface area (RSA), soil pH, soil Eh, infiltration .................................................................................. 34

2-3 Summary of analysis of variance results for dry weight of leaves and stems, leaf area, and leaf number as affected by biochar rate ....................................... 33

2-4 Infiltration, cumulative irrigation depth, calculated ETc, and cumulative drainage affected by biochar application ............................................................ 35

2-5 Soil pH and Eh (mean ± standard error) at 0-0.05 m and 0.05-0.15 m soil depths in the lysimeter ........................................................................................ 36

2-6 Leaf number, leaf area, dry weight of soybean plants grown in lysimeters under four biochar rates and two application methods. ...................................... 37

2-7 Root length density and root surface area of soybean collected at 0-0.05 m soil depth in lysimeter columns. .......................................................................... 38

2-8 Root length density and root surface area of soybean collected at 0.05-0.15 m soil depth in lysimeter columns ....................................................................... 38

3-1 Initial soil physical and chemical characteristics of the experimental site a. ........ 58

3-2 Chemical and physical characteristics of the biochar residual and fermentation residual used in the study .............................................................. 58

3-3 Interactive effect of year and treatment on mean (n=4) annual total aboveground dry matter yield. ............................................................................ 59

3-4 Daily average of soil volumetric water content to 1 m soil depth of bahiagrass with 50 kg fertilizer N ha-1 (BHG). ...................................................................... 59

3-5 Effect of treatment on average daily crop evapotranspiration (ETc) for each of three growing seasons ................................................................................... 60

3-6 Effect of treatment on average daily drainage for each of three growing seasons (GS; May - Oct) .................................................................................... 61

3-7 Effect of treatment on average daily drainage for each of three growing seasons (GS; May - Oct) and two dormant seasons. ......................................... 62

4-1 Chemical and physical characteristics of the biochar residual and vinasse residual used in the study. .................................................................................. 82

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4-2 Minimum, maximum, average air temperature measured at 2 m, precipitation, and solar radiation during May-October ............................................................. 83

4-3 Aboveground dry biomass accumulation of cotton + 150 kg N ha-1 (COT), peanut +30 kg N ha-1 (PEA), and sweet sorghum .............................................. 84

4-4 Soil volumetric water content at six different depths in the soil profile of a conventional cotton-peanut rotation and sweet sorghum ................................... 85

4-5 Average NO3-N concentration in leachates collected at 1m depth in cotton (COT), peanut (PEA), and sweet sorghum. ........................................................ 86

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LIST OF FIGURES

Figure page 3-1 Minimum, average, maximum daily air (°C) at 2 m, daily and monthly solar

radiation (MJ m-2 d-1), and rainfall ....................................................................... 63

3-2 Water use efficiency of bahiagrass fertilized with 50 kg N ha-1 (BHG) and elephantgrass during three years in north FL. .................................................... 64

3-3 Rainfall and volumetric water content at 10 cm (a-c), 20 cm (d-f), 30 cm (g-i), 40 cm (j-l), 60 cm (m-o), and 100 cm (p-r) depth in bahiagrass .......................... 65

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LIST OF ABBREVIATIONS

COT Cotton + 150 kg N ha-1

ETc Evapotranspiration

HA Hectares

PEA Peanut + 30 kg N ha-1

RLD Root Length Density

RSA Root Surface Area

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

EFFECTS OF LAND-APPLICATION OF BIOENERGY RESIDUALS ON ECOSYSTEM

SERVICES AND SOIL-WATER-PLANT RELATIONS IN FLORIDA AGROECOSYSTEMS

By

Joel Eduardo Reyes Cabrera

May 2017

Chair: Ramon G. Leon Cochair: John E. Erickson Major: Agronomy

There is a need to understand the impacts of converting existing cropping

systems in the southeastern U. S. to bioenergy cropping systems, especially regarding

ecosystem services. Moreover, conversion of feedstock to biofuels produce nutrient-rich

biochar and vinasse residual materials that could be recycled as soil amendments, but

limited information is available regarding their effects as nutrient sources on dedicated

bioenergy cropping systems. Therefore, this study assessed the effects of replacing

bahiagrass (Paspalum notatum Flugge) pastures + 50 kg N ha-1 (BHG) with

elephantgrass (Pennisetum purpureum L. Schum.) receiving one of the following

treatments: 50 kg N ha-1 (E50), 50 kg N ha-1 + fermentation residual (E50FR), 50 kg N

ha-1 + biochar residual (E50BC), and elephantgrass + 250 kg N ha-1 (E250) in Citra,

Florida. Similarly, the effects of converting a cotton (Gossypium hirsutum L.)-cotton-

peanut (Arachis hypogaea L.) rotation to a sweet sorghum [Sorghum bicolor (L.)

Moench] receiving one of the following + 30 kg N ha-1 (S30); iv) sweet sorghum + 30 kg

N ha-1 + 8 Mg vinasse ha-1 (S30V); v) sweet sorghum + 30 kg N ha-1 + 5 Mg biochar ha-

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1 (S30B); and vi) sweet sorghum + 150 kg N ha-1 (S150) were evaluated in Jay, Florida.

Comparisons were evaluated for aboveground biomass accumulation, crop water

dynamics, and nitrogen leaching dynamics.

Additionally, a greenhouse study was conducted to assess effects of land-

application of biochar residual on soil water holding capacity of coarse-texture soil and

root distribution of soybean (Glycine max L.) during early vegetative stages.

Results showed that elephantgrass produced 88% more dry matter compared to

bahiagrass. Elephantgrass treated with both bioenergy residuals produced on average

70 and 85% lower daily water drainage compared with bahiagrass in 2013 and 2014

growing seasons, respectively. Furthermore, application of vinasse residual reduced

drainage 83% and allowed 141% higher ETc compared to bahiagrass. Elephantgrass,

with or without residuals, reduced on average 95% the amount of NO3-N mass lost

through drainage compared to bahiagrass. Elephantgrass amended with bioenergy

residuals exhibited increased ETc compared to bahiagrass and similar to elephantgrass

treated with low (30 kg N ha-1) and high (250 kg N ha-1) N rate. Therefore, replacing

bahiagrass with elephantgrass will slightly increase cropping system water, but produce

substantially more biomass compared with bahiagrass.

In the annual cropping system study, similar aboveground dry matter yields were

obtained in 2013 and 2015 for cotton and all sweet sorghum treatments (average of 20

Mg ha-1 for both years). Biochar increased 36, 29, and 24% soil moisture retention in

the 0-0.2 m soil depth compared to the other treatments in 2013, 2014, and 2015,

respectively. Biochar application to sweet sorghum reduced NO3-N concentration in

leachates by 74, 44, and 73% compared to S150 in 2013, 2014, and 2015, respectively.

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In the greenhouse study, incorporation of 25 and 50 Mg biochar ha-1 in the top

0.15 m of soil increased leaf area 29 and 31% compared to topdressing 50 Mg biochar

ha-1 and to the control, respectively. Biochar increased soybean root length density

(RLD), root surface area (RSA), and leaf area, especially when incorporated into the soil

compared to nontreated control. Biochar application and placement within the soil

affects plant-rooting distribution, which represents an important management practice to

increase RLD and RSA.

Overall, our findings highlight that changing from existing cropping systems to

bioenergy cropping systems maintains, in the case of sweet sorghum, or increases, in

the case of elephantgrass, aboveground biomass productivity. Additionally, the

application of bioenergy residuals could improve elephantgrass water uptake and

increase ETc. Particularly, the use of fermentation residual as a soil amendment favored

greater elephantgrass ETc. Moreover, incorporation of biochar residual increases soil

moisture retention in sweet sorghum grown for feedstock production. The lack of

biomass response to the application of bioenergy residuals as soil amendments could

discourage farmers to adopt their use. However, residuals rates used in this study did

not appear to produce detrimental effects on ecosystem services measured, thereby

further research should evaluate the capacity of vinasse and biochar to support biomass

production combined with lower rates of synthetic fertilizer.

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CHAPTER 1 INTRODUCTION

There is an active search for sustainable alternatives to fossil-fuel as the main

source for energy (Solomon, 2010). Plant biomass represents a source of renewable

energy to the dwindling source of fossil fuels worldwide (Perlack and Stokes, 2011).

Biofuel production from agricultural feedstock relies on the biomass produced by

dedicated plant species, especially C4 grasses because of their high ratio of carbon

fixation per unit of water transpired (Woodard and Prine, 1993). Initially, corn (Zea mays

L.) was the primary biomass crop for biofuels (i.e. ethanol). However, concerns about

whether a food crop should be used for fuel and the environmental implications of

expanding corn production prompted the search for other non-edible biomass crops

(Fargione et al., 2008). The development of lignocellulosic fermentation procedures

allowed the production of what are known as second-generation biofuels, which have

been pinpointed as a more sustainable bioenergy choice because of their high biomass

productivity, and their primary use is not for human food or animal feed (Sims et al.,

2010). Among those lignocellulosic feedstocks, elephantgrass (Pennisetum purpureum

Schum.) and sweet sorghum (Sorghum bicolor L. Moench) are reported to be suited

bioenergy crops with desirable agronomic traits such as high energy yield and potential

for mechanization (Strezov et al., 2008; Regassa and Wortmann, 2014).

Elephantgrass is a perennial grass that produces abundant biomass and is well

adapted to Florida conditions. Therefore, it has been proposed as the main candidate

for feedstock in the southeastern United States (Woodard and Sollenberger, 2012).

Sweet sorghum is an annual crop with potential for bioenergy purposes due to its high

dry matter yield and drought tolerance (i.e. recovery ability), which makes it attractive to

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grow because it can overcome temporary water deficit conditions (Singh and Singh,

1995; Knoll et al., 2012). This tolerance to dry conditions is a desirable trait in the

context of climate change and less irrigation water available for agriculture.

Current efforts are focused on reducing the overall energetic cost and nutrient

inputs to grow lignocellulosic bioenergy crops. Hence, there is a need to minimize

dependence on external inputs while increasing the quantity and quality of biomass

harvested for conversion to liquid biofuel (Tilman et al., 2006).

Previous reports have found detrimental impacts on groundwater recharge from

growing bioenergy crops, particularly due to the extent of land conversion necessary to

grow them and the associated soil erosion, high evaporative demand, offsite nutrient

movement, which will impact hydrological and nutrient cycles in ways that are poorly

understood (Fargione et al., 2008; Vanloocke et al., 2010). Therefore, there is a need to

assess the potential impacts land conversion to bioenergy crops will have on

ecosystems services such as water dynamics, nitrate loading, and the carbon cycle.

Although weather conditions in Florida favor the high biomass productivity of

potential bioenergy crops, low soil water holding capacity of sandy soils in this state

combined with their low fertility are factors that can severely reduce the expected yield

of these bioenergy crops. Management practices aimed to improve the efficiency of

these bioenergy cropping systems must be explored and tested in order to meet the

goals set for renewable production of biomass for energy purposes.

Management practices aimed to increase the levels of soil water availability could

significantly improve the productivity of bioenergy crops as well as food crops. In Florida

coarse-textured soils, ensuring available soil moisture for crop use throughout the

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growing season is a challenge that requires a combination of alternatives that increase

water retention in the rootzone and minimize percolation losses. The possible adverse

effects of climate change could mean less available precipitation to grow energy crops,

so there is a need to extent the time the water remains in the rootzone as well as

reduce potential nitrate downward movement that may pollute groundwater (Tuck et al.,

2006).

Therefore, growing bioenergy crops in extensive agricultural settings will require

a large amount of water to support evapotranspiration demands and produce

harvestable biomass that can be used as feedstock for bioenergy. Thus, the production

of renewable fuel will place greater pressure on available water resources already

limited in many agricultural settings.

The use of soil amendments that improve soil water holding capacity and

increase soil surface area can ameliorate expected precipitation deficit and minimize the

use of costly irrigation and fertilization inputs necessary to support the higher crop

evapotranspiration of bioenergy crops associated with high biomass productivity. In this

regard, it is known that industrial transformation of biomass to biofuels produces

nutrient-rich residuals that can be used to improve soil fertility and maximize nutrient

availability for plant uptake in bioenergy crops. For instance, Agyin-Birikorang et al.

(2013) observed that the applications of residuals from the bioenergy industry to the soil

generated agronomic benefits such as greater soil cation exchange capacity and higher

sweet sorghum dry matter yield.

Biochar is a carbonaceous residual produced by pyrolysis of biomass that is

applied to the soil in a controlled manner to enhance soil properties (Lehmann and

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Joseph, 2009). The use of biochar as soil amendment in agricultural production systems

has been studied in the past years. Various experiments have evaluated the effects of

biochar application across multiple cropping systems, and the majority reported biochar

to positively affect the aggregation of soil particles, increase cation exchange capacity,

and turned agricultural systems into a net carbon sink due to biochar’s stability and

potential to sequester carbon by slowing down the release of soil carbon to the

atmosphere (Lehmann, 2007; Lehmann and Joseph, 2009; Jeffery et al., 2011).

Biochar application could potentially increase nutrient retention and water holding

capacity of Florida sandy soils (Major et al., 2009). The small specific surface area of

sand particles (0.01 to 0.1 m2 g−1) offers limited water storage for plant uptake that could

be improved through biochar addition (Troeh and Thompson, 2005). Therefore, biochar

additions are likely to be more beneficial for increasing water holding capacity in sandy

soils than clay soils (Atkinson et al., 2010; Chan et al., 2007; Woolf, 2008).

Previous reports suggest that biochar changes particle size distribution of the soil

profile within the rooting depth. By increasing soil surface area biochar creates an

enhanced filtration pattern, which also positively impacts water and nutrient (especially

N and P) retention time (Atkinson et al., 2010; Piccolo et al., 1996).

The development and adoption of novel ways to increase water retention in

sandy soils will positively impact nutrient availability within the rooting zone. A vast

majority of food and fiber production in Florida takes place in soils with high sand

content, thus an increase in the ability of these soils to hold and supply moisture and

nutrients will potentially promote higher crop productivity (Atkinson et al., 2010).

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Addition of biochar to agricultural fields has been documented to increase soil

health and fertility; however, controversy is found in the literature stating both positive

and negative effects from biochar application due to inconsistent results related mostly

to complex soil-water-plant interactions (Atkinson et al., 2010; Spokas et al., 2012). The

determination of optimum application rate, proper timing, and placement of the biochar

are commonly identified as the biggest challenges in order to maximize the benefits of

using this carbon-rich material. Although biochar represents an alternative method to

improve soil properties in agricultural land where fertility and water holding capacity are

poor, its heterogeneous characteristics present a complex issue that requires thorough

evaluation of rate and application strategy to effectively use biochar depending on soil

type and crop (Enders et al., 2012).

This study aims to assess the effects of two land-use conversion 1) conventional

perennial bahiagrass pasture to elephantgrass and 2) from a conventional cotton-

peanut rotation to sweet sorghum. Treatment will be subjected to different soil fertility

regimens including the use of biochar, vinasse, and commercial inorganic fertilizer.

Additionally, studies were conducted to gain deeper knowledge about biochar use as

soil amendment on soil pH, Eh, water infiltration and its effects on plant root growth and

biomass accumulation. The null hypothesis (Ho) was that bioenergy residuals do not

provide significant ecosystem or agronomic benefits when applied in field or

greenhouse conditions.

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CHAPTER 2 BIOCHAR CHANGES SHOOT GROWTH AND ROOT DISTRIBUTION OF SOYBEAN

DURING EARLY VEGETATIVE STAGES

Background

Biochar is a carbon rich material obtained as a secondary product of pyrolysis

and can be applied to the soil to alleviate existing soil constraints, thereby promoting

greater crop productivity (Woolf et al., 2010; Jeffery et al., 2011; Lehmann et al., 2015).

The poor water and nutrient holding capacity of sandy soils in the southeastern United

States (Carlisle et al., 1988) creates challenging conditions for root resource uptake and

adequate plant growth, which negatively impacts crop productivity. Application of

biochar to coarse-textured sandy soils favors accumulation of soil organic matter and

increases soil water and nutrient adsorption capacity (Uzoma et al., 2011).

Several studies under controlled and field conditions have found that addition of

biochar seems to have the potential to increase soil water-holding capacity (WHC),

which could have beneficial impacts on crop production under water-limited conditions

and contribute to agricultural sustainability (Atkinson et al., 2010; Jeffery et al., 2011;

Karhu et al., 2011; Basso et al., 2013). Although increases in WHC are reported with

biochar addition to the soil, limited information exists about biochar effects on water

infiltration and root distribution patterns. Biochar could potentially produce negative

effects on water infiltration and root growth due to modifications to the physicochemical

interface where soil, roots, and biochar converge, thus directly affecting root growth and

distribution patterns (Lehmann et al., 2009, 2015). For instance, Bruun et al. (2014)

conducted a lysimeter study and reported a significant increase in barley (Hordeum

vulgare L. cv. ‘Anakin’) root density in the 40- to 80-cm soil depth range after

incorporating 2% w/w biochar in a coarse sandy soil. Conversely, previous studies

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reported adverse effects of applying high biochar rates (>100 Mg ha-1), as they may

inhibit initial root growth of some crop species due to its alkalinity (Solaiman et al.,

2012). Thus, it is necessary to assess the effects of biochar application and rates not

only on soil WHC but also on water infiltration and root distribution.

Soil-root-water interactions are expected to vary depending on biochar rate and

placement. From a practical standpoint, biochar placement will be largely influenced by

cropping system (e.g., annual versus perennial species), tillage method (conservation

vs. conventional tillage), and environmental and topographic conditions. For instance, in

perennial and no-till cropping systems biochar will be applied and maintained on the soil

surface, whereas in annual cropping systems that use conventional tillage, biochar will

be incorporated into the soil to minimize wind erosion risk. Additionally, similar to

fertilizer and manure application, the effectiveness of biochar and its agricultural

benefits will depend on whether biochar is surface applied or incorporated into the crop

root zone (Blackwell et al., 2009; Major, 2010). Therefore, assessment of how biochar

placement in the soil profile affects soil WHC, infiltration, and plant shoot and root

responses will help elucidate effective strategies for biochar utilization to maximize its

agronomic and environmental benefits (i.e. notably carbon sequestration).

The objective of this study was to evaluate the effects of topdressing or

incorporating four biochar rates on soybean [Glycine max (L.) Merr.] root distribution,

shoot growth, soil WHC, and water infiltration during soybean early vegetative stages.

Materials and Methods

A greenhouse study was conducted in late spring and early summer of 2014 at

the West Florida Research and Education Center of the University of Florida near Jay,

FL (30° 46’ N, 87° 08’ W). A sandy loam soil (Red bay, fine-loamy, kaolinitic, thermic

21

Rhodic Kandiudult) with 69% sand, 16% silt, 15% clay, 2.3% organic matter, and a pH

5.7 was obtained from the research center, where peanut (Arachis hypogaea L.) and

soybean had been grown in previous years. To reduce variability in texture and

structure associated with different soil horizons, a uniform section of soil was collected

from the upper 0- to 0.10-m soil layer, sieved through a 6.4-mm mesh to remove debris,

air-dried for 15 days inside the greenhouse, homogenized, and weighed, to fill lysimeter

columns (0.15-m diameter, 0.35-m height, 183.85-cm2 area). A headspace of 5 cm

above the soil surface was left for water addition. The soil was uniformly packed across

all lysimeter columns to achieve a mean bulk density of 1.34 ± 0.05 and 1.36 ± 0.06 g

cm-3 in the first and second repetitions of the experiment, respectively. The bottom of

each lysimeter had a drainage system that transported the leachates to an individual

collection unit for storage and subsequent volume measurement, which normally

occurred 24 h after irrigation, and evaporation losses were considered negligible.

Experimental Design

The experiment was a factorial arranged in a completely randomized design with

eight replications and was conducted twice. The first factor was application rate of a fine

powdery biochar produced at 760 °C using wood as feedstock (Standard Purification,

Dunnellon, FL) (Table 2-1). Biochar was applied at four rates: 0, 18.4, 46, and 92 g

lysimeter-1, which are equivalent to 0, 10, 25, and 50 Mg ha-1. The second factor was

depth of biochar placement in the soil, including (i) topdressed (surface applied) or (ii)

uniformly incorporated in the top 0.15 m of the soil profile by removing this volume of

soil and thoroughly mixing it with biochar and then placing it back into the lysimeter. The

experiment had a total of 64 experimental units (i.e., lysimeters).

22

After biochar application, a known volume of water was added to each lysimeter

and infiltration time (i.e., time required for water to enter the soil surface) was recorded.

The rate at which water entered the soil was calculated using the formula in Equation 2-

1:

𝑖 = 𝑄

𝐴 ∗ 𝑡

(2-1)

Where i is infiltration, Q is the volume of water (m3) infiltrating, A is the area of the

soil surface (m2) where water infiltrates, and t is the time (s) it takes Q to infiltrate in the

soil profile. The initial soil moisture content was considered to be negligible in both

experiments since the soil was air dried before initiation of the experiment.

Water addition was repeated several times until the soil in the lysimeter was

saturated (i.e., water draining to the collection units). Lysimeters were allowed to drain

for 24 h, after which drainage was collected and measured. Next, the lysimeters were

weighed to quantify water content at field capacity. Five soybean (‘AsGrow 7733’

maturity group VII) seeds were sown in each lysimeter at a depth of 0.05 m. Soybean

was chosen because its morphology and physiology has been well characterized and its

plant size was appropriate to study root responses to biochar based on the size of the

lysimeters. Plants were randomly thinned 1 wk after emergence to leave a single plant

per lysimeter. Soil moisture content loss due to plant transpiration and soil evaporation

was measured gravimetrically (i.e., lysimeters were weighed) twice a week to determine

water losses. Irrigation maintained soil moisture content at 80 ± 10% field capacity. For

every watering event, irrigation and drainage volumes were recorded in order to

determine water balance. For a given time interval, crop evapotranspiration was

23

calculated by subtracting drainage volume from the change in irrigation volume between

two consecutive irrigations.

Lysimeter location inside the greenhouse was randomly rearranged weekly to

reduce site effects. Average air humidity in both experiments was 62%; temperature

was controlled throughout the duration of the experiment with 32 ± 5 °C daytime and 24

± 3 °C nighttime temperatures. Solar radiation availability was lower in the first

experimental repetition, with a greater number of cloudy days compared with the

second repetition, which slightly reduced soybean growth rate. No supplemental light

was provided.

Plant growth stage was monitored daily and determined using the stage

description provided by Fehr et al. (1971). Soybean shoots were harvested when 50%

of the plants had at least one single visible flower and thus considered to reach the R1

stage (beginning of flowering; Pedersen, 2009) at 40 and 32 d after planting for the first

and second trials, respectively. This harvest timing was chosen because a number of

studies have shown that the lysimeter size in this study could have constrained rooting

depth after the R1 stage (Kaspar et al., 1978; Torrion et al., 2012).

At R1, fully developed leaves were counted, aboveground plant material was

clipped from lysimeters, and leaves and stems were separated. Leaves were scanned

with a leaf area meter (LI-3100C, LICOR Biosciences, Lincoln, NE). Subsequently,

samples were dried at 65 °C in an oven for 72 h until constant weight was attained.

Soil and Root Analysis

After the aboveground plant tissue was clipped, the soil column, including roots,

was split and removed from the lysimeters by depth (i.e., 0-0.05, 0.05-0.15, and 0.15-

0.30 m), washed, placed in plastic bags and stored at 8 °C for further analysis (Qian et

24

al., 1997; Adiku et al., 2001). A soil subsample (20 g) was taken to evaluate treatment

effects on soil pH and electrical conductivity (Eh) using 1:2 soil weight/volume of

deionized water with a glass electrode (Mylavarapu, 2012). A soil subsample equivalent

to half of the original soil volume in each depth was used to measure root distribution.

Roots were separated from each soil section using a 0.84 mm sieve to remove soil.

Roots were scanned using the WinRhizo 8.0 software package (Regent Instruments

Inc., Quebec, Canada) to quantify length and area of roots in the different soil depths.

Root samples were then oven dried at 65 °C until constant weight was reached for

subsequent dry weight determination.

Several root variables are reported in the literature as insightful means to

quantify changes in root distribution and morphology. However, RLD and root surface

area (RSA) are reported as the most sensitive to change when soil physical and

chemical composition are altered by the addition of amendments (Smika and Klute

1982; Himmelbauer et al. 2004), and were thus used in the present study. Root length

density was calculated as the ratio of root length (cm) to volume of a given depth

section of soil (cm3).

Data Analysis

PROC UNIVARIATE of SAS software (SAS Institute, 2013) was used to assess

normality of data distribution and homoscedasticity. Shoot data, root parameters, soil

pH, and soil Eh were analyzed with ANOVA using PROC GLIMMIX in SAS software.

Biochar rate and application method and experiment repetition were initially treated as

fixed effects. Significant interactions between biochar rate and application method

(P<0.05) were detected only for soybean shoot parameters (Table 2-2). In contrast, no

interactions between experimental repetition and any of the other variables evaluated in

25

the present study (i.e., soybean, soil, irrigation, and drainage measurements) were

significant (P>0.05) (Tables 2-2 and 2-3). For this reason, data were subsequently

analyzed and presented by pooling the two experiment repetitions, and biochar rate and

application method were considered fixed effects and experiment repetition as a

random effect. Tukey-Kramer honest significant difference test (α=0.05) was used for

mean separation.

Results

Soil Characteristics and Water Dynamics

Water infiltration under field conditions was higher (0.5 ± 0.1 mm s-1) than the

one observed in the control treatment indicating that our experimental system had more

compacted conditions. However, we were still able to detect reductions in infiltration of

at least 45% after incorporating or topdressing biochar compared to the control (Table

2-4). The reduction was more pronounced when 50 Mg biochar ha-1 was incorporated

producing a 70% decrease in infiltration.

A significant interaction between biochar rate and application method affected

irrigation volume necessary to maintain moisture content within the established ranges

(P<0.05). However, the only significant difference was found when incorporating 25 Mg

biochar ha-1 reduced irrigation water 18% compared with topdressing 50 Mg ha-1 (Table

2-4). Topdressing 10, 25, and 50 Mg biochar ha-1 increased evapotranspiration by 14,

17, and 19%, respectively, compared with the control. Incorporation of 25 Mg biochar

ha-1 significantly reduced the volume of water lost through drainage by 81% compared

with the control (Table 2-4). Soil pH was affected by interactions among biochar rate,

application method, and soil depth within the lysimeter (P<0.001). In the upper soil layer

(0-0.05 m), there was a positive relation between biochar rate and pH for both

26

application methods (Table 2-5). An increase of up to 2 pH units was observed in the

top 0.05 m soil after biochar was added, compared with the slightly acidic conditions of

the control (pH ~ 5.5). In the 0.05- to 0.15-m soil layer, biochar also increased soil pH,

but this increase was less pronounced compared with the 0- to 0.05-m layer. In

contrast, soil Eh decreased as biochar rate increased at both soil depths in the

lysimeter, and the decrease was steeper in the 0- to 0.05-m section (Table 2-5). The

fact that pH and Eh changes occurred in the top 0 to 0.05 m of soil for both application

methods suggested that higher aeration and/or temperature close to the soil surface

favored redox reactions in the aromatic components of the biochar matrix (Joseph et al.,

2010), while these processes were delayed or reduced at deeper layers.

Aboveground Plant Characteristics

There were significant interactions between biochar rate and application method

for all aboveground growth parameters (P<0.05) except for leaf and total dry weight

(Table 2-2). Biochar incorporated at 25 and 50 Mg ha-1 produced the greatest leaf

number representing a 29 and 24% increase compared with plants in the control and

topdressed treatments, respectively (Table 2-6). There was a significant interaction

between biochar rate and application method for leaf area (P<0.05; Table 2-2). Plants

growing in lysimeters with biochar incorporated to the soil produced at least 28%

greater leaf area than plants in the control. Incorporation of 25 and 50 Mg biochar ha-1

produced 18 to 26% more leaf area compared to topdressing treatments. Total dry

weight was higher than the control only when biochar was incorporated at 50 Mg ha-1.

The incorporation of either 25 or 50 Mg ha-1 increased total dry weight 22% compared

with topdressing 50 Mg biochar ha-1.

27

Root Characteristics

Root length density and RSA were affected by soil depth in the lysimeter (P <

0.001). No differences in RLD or RSA (P > 0.05) were found among biochar treatments

in the lower soil layer (0.15–0.35 m), but biochar modified root growth in the upper and

middle soil layers in the lysimeter (Tables 2-7 and 2-8). In the upper soil layer (0–0.05

m), biochar at 10 and 25 Mg ha−1 increased RLD 51 to 57% and RSA 50 to 53%

compared with the control (Table 2-7). At 0.05–0.15 m, only incorporation of 10 Mg

biochar ha−1 increased RLD (38%) (Table 2-8). Additionally, incorporation of 10 Mg

biochar ha−1 exhibited 40 and 44% higher RLD compared with topdressing 10 and 50

Mg ha−1, respectively. The incorporation of 10 and 25 Mg biochar ha−1 increased RSA

35 to 43% compared with the control.

Discussion

Biochar Effects on Soil Water Retention and Plant Growth

Biochar application, whether incorporated or topdressed, decreased water

infiltration as reported by Novak et al. (2016) indicating a rapid decline in soil water

conductivity. It was expected that the high rates of topdressed biochar would maintain a

significant proportion of the irrigated water (i.e., soil moisture) in the upper 0.05 m of the

soil profile, supplying water to the plant and potentially increasing water uptake (Olmo et

al., 2014). However, results from this study showed that, regardless of the biochar rate

and application method, similar volumes of water were added between biochar-treated

soil compared to the nontreated control (Table 2-4).

Interestingly, the incorporation of 25 Mg ha-1 of biochar produced a reduction of

drainage, thereby suggesting an effect on the soil matrix with this specific rate-soil

combination that was not observed under any of the other treatments. A similar

28

response was noted by Barnes et al. (2014), where addition of biochar to sandy soil

produced a soil with greater porosity compared with untreated sand, restricting water

flow through the soil matrix and therefore decreasing drainage.

It has been reported that biochar can increase soil water availability (Jeffery et

al., 2011; Liu et al., 2013), but this likely depends on factors such as soil moisture

content and biochar age. For instance, Hardie et al. (2014) described a water

characteristic curve and reported no biochar effect on soil water retention of a sandy

loam at low water content, but observed greater water content held at near-saturated

conditions. The lack of water retention at low soil water content could limit the ability of

biochar-amended soils to mitigate drought stress in plants. In contrast to findings in

other studies in which biochar improved soil WHC (Karhu et al., 2011; Uzoma et al.,

2011), this study demonstrated that, regardless of the rate of biochar added to a sandy

loam soil, there was not an increase in soil WHC. These results could be explained by

the single application of biochar and the short period of interaction between biochar and

soil particles during the experiment. Thus, it is possible that over time this type of

biochar may increase soil WHC after greater interaction with the soil medium. Because

the interval between biochar application and planting was not evaluated in the present

greenhouse study, application of biochar to coarse-textured soil needs to be further

analyzed to understand the complex relations between biochar and soil particles

governing long-term effects of biochar application on soil water retention.

In the present study, the availability of soil moisture was sufficient to fulfill

soybean water needs during early vegetative growth, as no plant showed visual signs of

drought stress. However, the addition of different rates of biochar elicited different

29

soybean above and belowground responses depending on the application method,

which underscores the importance of identifying optimum rates of biochar under field

conditions to maximize biochar benefits on plant growth. The increase in soybean

growth observed in treatments when biochar was incorporated but not when it was

topdressed indicates that biochar should be added in the root zone to maximize its

benefits on crop growth. Furthermore, our results suggest that biochar might have toxic

effects (i.e., Solaiman et al., 2012) beyond its optimum application rate (e. g. 25 Mg ha-1

in the present study), which needs to be identified for various crop species. This may

make the economics of biochar application more favorable, but it might also limit the

total amount of biochar that can be returned to the field in a given period of time. The

increase in plant growth observed in treatments with biochar incorporation may be due

in part to increases in soil porosity, and thus the pool of nutrients and water that the

plant can utilize to support leaf development. Additionally, previous studies indicated

nutrient availability and plant uptake were high when biochar is mixed with a large soil

volume where roots can forage (Laird et al. 2010a; Basso et al., 2013; Bruun et al.,

2014). Thus, in the current study, the larger leaf number and area observed with

incorporated biochar as compared to the topdressed treatments may be reflective of

improved soil nutrient conditions overall (Chan et al., 2008; Atkinson et al., 2010;

Prendergast-Miller et al., 2014).

Improved nutrient availability may be linked to the soil pH changes noted in the

present study in the biochar treatments. It has been proposed that biochar improves soil

fertility indirectly through increases in soil pH (Major et al., 2010), alleviating possible

issues with acidic conditions, like those measured in the control soil (pH ~ 5.5), and

30

potential aluminum toxicity (van Zwieten et al., 2009; Major et al., 2010) For instance,

van Zwieten et al. (2010) reported that, on average, biochar could have 30% of the

liming effect of CaCO3, thereby increasing the availability of some nutrients in the soil

solution for plant uptake. Wright et al. (2011) reported that, under Florida conditions,

soybean grows well in soils with pH in the range of 5.8 to 6.5, and in the present study,

a significant increase in pH was observed when biochar was added in the upper and

intermediate soil sections in the lysimeter compared with the control. This increase in

soil pH suggested an improvement of soil fertility with biochar addition to which soybean

responded positively. Mengel and Kamprath (1978) found that high soil pH improved

rhizobium activity and alleviated growth-limiting factors associated with low pH

conditions. Moreover, Rogovska et al. (2007) reported a positive relation between soil

pH in the 5.6 to 7.5 range with high soybean growth and yield.

Biochar Effects on Root Growth and Distribution

The application of biochar changed soybean root growth and distribution, which

highlights the potential value of this amendment to stimulate root growth in soil layers

where it is present (Bruun et al., 2014). Topdressing biochar in the soil surface could

increase fertilizer uptake efficiency through stimulation of greater root length density and

greater potential for soil exploration in the upper 0-0.05 m where fertilizer is broadcast.

A similar root growth response to biochar application was documented by Abiven et al.

(2015). Their findings demonstrated a strong association among biochar application, a

bigger maize (Zea mays L.) root system, and greater uptake of nutrients compared to a

nontreated control.

Thus, the use of biochar may be a tool that growers can utilize to elicit specific

changes in root growth to enhance root proliferation and more water uptake and

31

exploitation of mobile and non-mobile nutrients in the soil. In the present study, root

distribution was concentrated in the biochar-amended soil sections in the lysimeter,

suggesting more favorable conditions for root growth. Moreover, the fact that RLD and

RSA differences were observed only in the specific soil sections where biochar was

present suggests that biochar influences the root and soil interface benefiting the plant

and encouraging root exploration. Based on the results of this study, addition of biochar

to the soil produced changes in soil physical and chemical properties that resulted in

changes in root growth. Although the present research did not allow for identifying the

physiological mechanisms by which roots proliferated in the soil treated with biochar, it

is possible that biochar either behaves as a source of nutrients or creates favorable

conditions for nutrient uptake (Prendergast-Miller et al., 2014) providing stimulatory

signals for root growth (Bonser et al., 1996; Lopez-Bucio et al., 2003; Malamy, 2005).

Although clear correlation between increased RLD and increased water uptake was not

observed in the present study, greater RLD in deep soil layers has been reported to be

advantageous to improve water uptake and minimize drought stress by increasing root

access to water percolating through the soil profile (Ho et al., 2004). A study conducted

by Bruun et al. (2014) using soil columns also indicated an increase of root density at

soil depths under 0.4 m when biochar was added alleviating existing subsoil constraints.

Summary

Biochar significantly decreased water infiltration in the soil compared with the

control, and increased soybean RLD and RSA during early vegetative stages, which

was observed in the specific soil layer where biochar was topdressed or incorporated.

Additionally, topdressed biochar drastically increased surface (0-0.05 m) soil pH,

alleviating acidic conditions. Addition of biochar at the desired depth could be a

32

management practice aimed to guide root growth and distribution to increase foraging

and uptake of water and nutrients. However, it is important that future research should

further assess the effects of biochar application on soybean root distribution in the field

during the entire growing season to determine the application rate and incorporation

method that best fit the needs and characteristics of the cropping system.

33

Table 2-1. Chemical analysis of biochar used in the study a.

Parameter Unit Value

Nitrogen b g kg-1 5.3 Phosphorus g kg-1 1.8 Potassium g kg-1 1.6 Sulfur g kg-1 1.2 Zinc g kg-1 2.4 Manganese g kg-1 0.2 Calcium g kg-1 19.6 Boron mg kg-1 80 Aluminum mg kg-1 3659 Particle size μm 325 Bulk density g cm-3 0.4-0.6 pH na 9.4 a Biochar mineral content determined by laboratory analysis. Particle size and bulk density data

was obtained from biochar manufacturer. Same biochar was used in both experiment repetitions. b Nitrogen added to treatments was 0, 53, 133, and 265 kg ha-1 for 0, 10, 25, and 50 Mg biochar

ha-1, respectively. c Phosphorus added to treatments was 0, 18, 45, and 90 kg ha-1 corresponding to 0, 10, 25, and

50 Mg biochar ha-1, respectively. Table 2-2. Summary of ANOVA results for dry weight of leaves and stems, leaf area,

and leaf number as affected by biochar rate, biochar application method, experiment repetition, and treatments interactions.

Main effect

D.F. Leaf area

Leaf number

Dry weight

leaves stems total Biochar

rate (B) 3 ***

*** * ns *

Application

(A) 1 **

*** * ns *

Experiment

(E) 1 ns

*** ns ns ns

B x A 3 * ** ns * ns

A x E 1 ns ns ns ns ns

B x E 3 ns ns ns ns ns

B x A x E 3 ns ns ns ns ns

* Significant at P<0.05; ** significant at P<0.01; *** significant at P< 0.001. ns: not significant; --: not applicable; D.F.: degrees of freedom.

34

Table 2-3. Summary of ANOVA for root length density (RLD), root surface area (RSA), soil pH, soil electrical conductivity (Eh), infiltration, irrigation, drainage, and soybean crop evapotranspiration (ETc) during early vegetative growth in a lysimeter study.

Main effect D.F. RLD RSA pH Eh Infiltration Irrigation Drainage ETc

Biochar rate (B)

3 ** * *** *** ** * ns ns

Application (A)

1 ns ns * ns * ns ns ns

Experiment (E)

1 ns ns ns ns ns ns ns *

Depth (D) 2 *** *** *** *** -- -- -- -- A x B 3 ns ns ns ns ns ns ns ns A x E 1 ns ns ns ns ns ns ns ns A x D 2 ** * *** *** -- -- -- -- B x E 3 ns ns ns ns ns ns ns ns B x D 6 ns ns *** *** -- -- -- -- D x E 2 ns ns ns ns ns ns ns ns A x D x E 2 ns ns ns ns -- -- -- -- A x B x E 3 ns ns ns ns ns ns ns ns B x D x E 6 ns ns ns ns -- ns ns ns

* Significant at P<0.05; ** significant at P<0.01; *** significant at P< 0.001. ns: not significant; --:

not applicable; D.F.: degrees of freedom.

35

Table 2-4. Infiltration, cumulative irrigation depth, calculated crop evapotranspiration (ETc), and cumulative drainage affected by biochar application method and biochar rate for early vegetative soybean grown in lysimeters.

Application method

Biochar rate (Mg ha-1)

Infiltration (mm s-1)

Cumulative irrigation depth (mm)

ETc (mm) Cumulative drainage (mm)

Control 0.18 ± 0.10 a a 100.2 ± 7.3 ab 87.6 ± 4.4 b 12.6 ± 4.6 a Incorporated 10 0.12 ± 0.07 b 101.9 ± 6.7 ab 94.3 ± 4.8 ab 7.5 ± 2.5 ab 25 0.09 ± 0.06 bc 94.4 ± 5.5 b 91.9 ± 5.5 ab 2.4 ± 1.2 b 50 0.06 ± 0.05 c 102.3 ± 5.0 ab 95.6 ± 3.4 ab 6.7 ± 2.9 ab Topdressed 10 0.10 ± 0.05 b 111.3 ± 7.8 ab 99.4 ± 4.6 a 11.8 ± 3.9 ab 25 0.11 ± 0.06 b 111.2 ± 4.6 ab 102.2 ± 4.3 a 9.1 ± 3.2 ab 50 0.10 ± 0.10 b 115.8 ± 6.1 a 104.0 ± 3.3 a 11.8 ± 4.2 ab a Mean ± standard error. Values within columns and method of application followed by the same

lowercase letter indicate that means are not significantly different at P<0.05. Data represent the results of the combined analysis of two experimental repetitions.

36

Table 2-5. Soil pH and electrical conductivity (Eh) (mean ± standard error) at 0-0.05 m and 0.05-0.15 m soil depths in the lysimeter affected by the interaction of biochar application method and biochar rate.

Soil depth (m)

Application method

Biochar rate (Mg ha-1)

pH Eh (siemens)

0-0.05 control 5.47±0.21 f a 0.102 ±0.007 a Incorporated 10 5.76 ±0.22 e 0.067 ±0.007 b 25 6.18 ±0.22 cd 0.039 ±0.004 c 50 6.53 ±0.27 bc 0.013 ±0.007 d Topdressed 10 5.99 ±0.17 de 0.054 ±0.011 bc 25 6.68 ±0.28 ab 0.012 ±0.017 d 50 7.00 ±0.35 a 0.000 ±0.022 e 0.05- 0.15 control 5.26 ±0.16 a 0.098 ±0.005 a Incorporated 10 5.35 ±0.40 a 0.083 ±0.010 bc 25 5.58 ±0.18 a 0.075 ±0.009 c 50 5.50 ±0.42 a 0.079 ±0.023 c Topdressed 10 5.31 ±0.16 a 0.094 ±0.005 ab 25 5.46 ±0.16 a 0.087±0.006 abc 50 5.39 ±0.21 a 0.087±0.007 abc a Values within columns followed by the same lowercase letter indicate that means are not

significantly different at P<0.05 within the same soil depth. Data represent the results of the combined analysis of two experimental repetitions.

37

Table 2-6. Leaf number, leaf area, dry weight of soybean plants grown in lysimeters under four biochar rates and two application methods.

a Values within columns followed by the same lowercase letter indicate that means are not significantly different at P<0.05 between

application method within the same biochar rate. Data represent the results of the combined analysis of two experimental repetitions.

Application method Biochar rate (Mg ha-1)

Leaf number

Leaf area (cm2)

Dry weight (g plant-1)

leaves stems total Control 0 9 b a 374 c 1.7 bc 1.0 a 2.7 bc Incorporated 10 11 ab 499 ab 1.9 abc 1.0 a 3.0 abc 25 13 a 543 a 2.1 a 1.1 a 3.18 ab 50 13a 536 a 2.0 ab 1.2 a 3.20 a Topdressed 10 10 b 412 bc 1.7 bc 0.9 a 2.7 abc 25 11b 473 abc 1.9 abc 1.1 a 3.0 abc 50 9 b 384 c 1.6 c 0.9 a 2.5 c

38

Table 2-7. Root length density (RLD) and root surface area (RSA) of soybean collected at 0-0.05 m soil depth in lysimeter columns treated with four biochar rates and two application methods.

Main effect RLD (cm cm-3)

RSA (cm2)

Biochar rate 0 0.20 b a 41.1 b 10 0.32 a 62.9 a 25 0.31 a 61.9 a 50 0.27 ab 56.1 ab Application method Control 0.19 b 39.7 b Incorporated 0.23 ab 49.2 ab Topdressed 0.32 a 63.6 a

a Means within columns followed by the same lowercase letter are not significantly different

(P<0.05) according to Tukey-Kramer HSD for main effects. Data represent the results of the combined analysis of two experimental repetitions. Table 2-8. Root length density (RLD) and root surface area (RSA) of soybean collected

at 0.05-0.15 m soil depth in lysimeter columns treated with four biochar rates and two application methods.

Application method

Biochar rate (Mg ha-1)

RLD (cm cm-3)

RSA (cm2)

Control 0 0.31 b a 101.9 c Incorporated 10 0.48 a 168.3 a 25 0.42 ab 160.5 ab 50 0.38 ab 128.4 abc Topdressed 10 0.29 b 92.6 c 25 0.32 ab 111.5 abc 50 0.27 b 89.2 c

a Means within columns followed by the same lowercase letter are not significantly different

(P<0.05) according to Tukey-Kramer HSD. Data represent the results of the combined analysis of two experimental repetitions.

39

CHAPTER 3 CONVERSION FROM BAHIAGRASS TO ELEPHANTGRASS INCREASES YIELD,

WATER USE EFFICIENCY, AND REDUCES NO3-N LEACHING IN WARM-SEASON PERENNIAL GRASS FIELDS FOR BIOMASS PRODUCTION

Background

The increasing demand for renewable fuels and chemicals from agricultural

crops and residues will stimulate more land planted with non-food bioenergy crops

(Perlack et al., 2005). Elephantgrass or Napiergrass [Pennisetum purpureum (L.)

Schum.] is a perennial C4 tall grass adapted to sub-tropical conditions that has been

identified as a potential candidate bioenergy crop based on high biomass production for

the southeastern United States (Woodard and Prine, 1993; Fedenko et al., 2013).

Elephantgrass dry biomass yields in excess of 30 Mg ha-1 are commonly reported in the

southeastern U.S. region depending on management practices (Bouton, 2002; Knoll et

al., 2012; Fedenko et al., 2013; Na et al., 2015). Bioenergy crops like elephantgrass are

likely to be grown on large areas, but not where food crops are already grown (Perlack

et al., 2005). Bahiagrass (Paspalum notatum Flugge) perennial grasslands used for

animal feed are a major land use in the southeastern U.S. with over 2 million hectares in

the state of Florida alone (Muchovej and Mullahey, 2000). Bahiagrass pastures on

marginal lands represent a likely candidate cropping system to be converted to

elephantgrass for bioenergy. Although elephantgrass has demonstrated relatively high

yield potential on similar marginal lands, the impacts on ecosystem services like water

quantity and quality of converting conventional low intensity pasture land to

elephantgrass bioenergy cropping systems are not well understood.

Bahiagrass pastures in the region are usually managed under low fertilization

and minimum irrigation. The typical fertilizer rate for low input bahiagrass ranges

40

between 50 to 60 kg N ha-1 (Mylavarapu et al., 2016) and a study evaluating the effects

of phosphorus fertilization rates on bahiagrass (Rechcigl et al. 1992), also indicated

bahiagrass high N uptake capacity, which could reduce potential NO3-N leaching that

degrades water quality. Additionally, the dense root system of bahiagrass (Ibrikci et al.,

1999) could facilitate nutrient and water extraction from deep soil layers thus reducing

the need for supplemental irrigation. Therefore, replacing bahiagrass with elephantgrass

would ideally result in similar or even enhanced ecosystem services to minimize

negative environmental impacts. However, high biomass productivity, which is common

and desired for bioenergy cropping systems, is often associated with high nutrient

removal and high water transpiration demand (Kering et al., 2012). Thus, high fertility

requirements and an elevated water use of bioenergy crops could reduce water quality

and quantity available to recharge aquifers. For example, McIsaac et al. (2010)

measured water consumptive use of bioenergy crops Miscanthus x giganteus and

switchgrass (Panicum virgatum) compared with a conventional corn (Zea mays L.)-

soybean (Glycine max L.) grown in Illinois, U.S. and reported greater soil water uptake

and crop evapotranspiration (ETc) for bioenergy crops, which reduced 32% of the

drainage water available for natural waterways.

Additionally, dedicated bioenergy crops that produce high amounts of

aboveground biomass that is removed from the field at harvest also tend to remove high

amounts of nutrients in that biomass (Singh et al., 2015; Na et al., 2015). The high

biomass production of bioenergy feedstock crops such as elephantgrass will then

require moderate to high fertilizer inputs to sustain yields over time, especially on

marginal soils (Knoll et al., 2012). For instance, Woodard and Prine (1993) reported

41

elephantgrass yielded up to 47 Mg dry matter ha-1 in response to high fertilizer inputs

(200 kg N ha-1), which could encourage application of high rates of synthetic N to

increase yields and profits, while also increasing the risk of NO3-N movement through

leaching. Furthermore, the coarse nature of most common soils in the southeastern

U.S. (Carlisle et al., 1988) could aggravate the risk of NO3-N leaching. However,

alternative production practices that increase nutrient use efficiency within the field

could achieve both reductions in fertilizer application and high biomass productivity

(Knoll et al., 2012; Singh et al., 2015).

By-products from the bioenergy processing industry contain nutrients that could

be returned to the field to support more sustainable production of feedstocks for biofuel

purposes. The use of these bioenergy residuals could potentially minimize reliance on

fertilizers and irrigation while preventing environmental contamination associated with

uncontrolled disposal in water bodies (Agyin-Birikorang et al. 2013). Biochar and

vinasse are two important bioenergy residuals. Biochar is a carbon dense residual

produced via pyrolysis of biomass that has been reported to correct specific soil

constraints such as acidic soil pH, low fertility, and low water holding capacity (Liu et al.,

2013). Novak et al. (2009) found significant increase in soil moisture content of a loamy

sand soil when biochar was applied. Similarly, Uzoma et al. (2011) reported that

application of 20 Mg biochar ha-1 to a sandy soil increased water holding capacity

(WHC) by 97%.

Conversion of feedstock to simple sugars and the subsequent fermentation of

these sugars produces ethanol and a fermentation residual high in lignin and minerals

also known as vinasse. Application of this fermentation residual has been known to

42

increase the percentage of large soil aggregates improving water and nutrient holding

capacity (Jiang et al., 2012). Additionally, Agyin-Birikorang et al. (2013) found that N-

based application of processed bioenergy residual provided adequate amounts of P and

K to support sweet sorghum (Sorghum bicolor L. Moench) productivity. The processed

bioenergy residual increased sorghum dry matter yield ~45% compared with inorganic

fertilization. Soil constraints in coarse-textured soils could be alleviated by the

application of bioenergy residuals. Land application of biochar and fermentation residual

to bioenergy crops could function as a nutrient recovery practice aimed to partially

replace inorganic fertilization as well as a sustainable alternative to mitigate

environmental impacts from biomass to energy conversion facilities (Hoekman, 2009).

Sustainable bioenergy cropping systems should provide similar or even

additional ecosystem services beyond high biomass production, such as improving

groundwater quantity and quality, compared to the current land use they replace

(Ferchaud and Mary, 2016). However, there is currently limited information on the

ecosystem services of bioenergy cropping systems in comparison to current agricultural

systems. Therefore, there is a need to better understand how the conversion of current

bahiagrass pasture land to an elephantgrass bioenergy cropping system will impact

water quantity and quality, and how land application of bioenergy residuals might affect

the provisioning of these services by the bioenergy cropping system. The objectives of

the present study were to 1) evaluate the effects on the soil-water plant dynamics from

converting bahiagrass pastures to an elephantgrass perennial crop for biomass

production, 2) quantify NO3-N concentration in drainage below the rootzone in

bahiagrass and elephantgrass treated with and without bioenergy residuals; and 3)

43

evaluate the effects of land-application of biochar and fermentation residuals to support

elephantgrass biomass production. We hypothesized that elephantgrass grown

conventionally with synthetic fertilizer would negatively affect water quantity and quality

draining downwards to the aquifer compared to bahiagrass, but land application of

residuals to elephantgrass could reduce detrimental effects on groundwater quality from

fertilizer leaching.

Materials and Methods

Study Site

A field experiment was conducted at the University of Florida Plant Science

Research and Education Unit located near Citra, Florida, U.S. during three consecutive

years (2013 – 2015). The soil was classified as Kanapaha fine sand (loamy, siliceous,

semi-active, hyperthermic Grossarenic Paleaquult) (Table 3-1). The study site was

under fallow conditions for about 6 months prior to establishment of the experiment.

Experimental Design

Stem cuttings of elephantgrass breeding line ‘UF-1’ were obtained from a nearby

nursery and sown on November 11th, 2012, by placing overlapping canes in opposite

orientation into furrows. Plugs of bahiagrass (cv. ‘Pensacola’) were planted on 18 July

2013. Plots were 90 m2 (10 x 9 m), planted with bahiagrass or 8-rows of elephantgrass

spaced every 1 m. The experiment was arranged in a randomized complete block

design with four replications. The treatments were: 1) bahiagrass pasture fertilized with

50 kg N ha-1 yr-1 (BHG); 2) elephantgrass fertilized with 50 kg N ha-1 yr-1 (E50); 3)

elephantgrass fertilized with 50 kg N ha-1 yr-1 plus 10 Mg ha-1 yr-1 lignocellulosic

fermentation residual (E50FR); 4) elephantgrass fertilized with 50 kg N ha-1 yr-1 plus 5

Mg pyrolysis biochar residual ha-1 yr-1 (E50BC); and 5) elephantgrass fertilized with 250

44

kg N ha-1 yr-1 (E250). Fermentation residual was received from the University of Florida

Stan Mayfield Biorefinery in Perry, Florida, following the fermentation of the structural

sugars contained in commercial sugarcane bagasse, which is similar to elephantgrass

in terms of fiber composition (Fedenko et al., 2013). Both bioenergy residuals were

analyzed for chemical composition in a commercial laboratory (Waters Agricultural

Laboratories, Inc. Camilla, Georgia, U.S.). The fermentation residual was air-dried to

evaporate the liquid phase and remaining solid material was surface applied.

Fermentation residual had a C concentration of 486 g kg-1 and N concentration of 40 g

kg-1. Biochar (Standard Purification, Dunnellon, Florida) C concentration was 625 g kg-1

and N concentration was 5 g kg-1 (Table 3-2).

Biochar and fermentation residual application rates were 8 and 10 Mg ha-1,

respectively. Estimation of these bioenergy residuals rates were based on current

biomass conversion efficiency of fermentation and pyrolysis technologies and residual

yields proportional to typical elephantgrass biomass yields in the southeastern U.S.

region (Knoll et al., 2012; Singh et al., 2015). Granulated fertilizer was sidedressed in all

plots at a basal rate of 16, 5, and 11 kg ha-1 of N, P2O5, and K2O, respectively, in April

2013 to favor crop establishment. In May of each year, ammonium nitrate (50 kg N ha-1)

and muriate of potash (60 kg K2O ha-1) were applied. Bioenergy residuals were applied

in May of each year after fertilization. Elephantgrass plots receiving the high fertilizer N

rate treatment received an additional 200 kg N ha-1 applied as ammonium nitrate (34-0-

0) 40 days after the first N application.

Data Collection

Lysimeters with 1.47 m height, 0.25 m diameter, and 0.057 m2 collection area

(Drain Gauge G3, Decagon Devices, Pullman, Washington, U.S.) were installed in the

45

center of each plot on 6 March 2013 to measure water movement below the root zone.

During lysimeter installation, soil was extracted in buckets by depth and filled back in by

depth to minimize soil profile disturbance and maintain soil profile texture

characteristics. The depth from soil surface to the lysimeter collection point was 1 m,

which was below the 0.6 m where >90% of roots are found for bahiagrass and

elephantgrass in this type of soil texture (Ibrikci et al., 1999; Erickson et al., 2012a;

Trenholm et al., 2015). Every two weeks, a portable vacuum pump was used to collect

drainage (D) from the lysimeter reservoir. The volume of drainage was measured and

three replicate subsamples were collected in 20 mL scintillation vials, immediately

frozen at -20 °C, and subsequently analyzed for NO3-N concentration at the Analytical

Research Laboratory (University of Florida, Gainesville, Florida, U.S.). The NO3-N

leached below the root zone was determined by multiplying the volume of water

collected in the lysimeters by the NO3-N concentration measured in the leachates.

In order to measure the change in soil water storage (ΔS) between leachate

collection events, soil volumetric water content (VWC) was measured in each plot 1 m

from the lysimeter access ports. Soil VWC was measured at six depths (0.1, 0.2, 0.3,

0.4, 0.6 and 1 m) using a multi-sensor soil moisture probe (Profile Probe PR2, Delta-T

Devices Ltd, Cambridge, U.K.). Water stored in the soil to a depth of 1 m was estimated

by multiplying the sensor reading by the respective volume of each layer. Readings

were collected every two weeks at the same time as NO3-N leachate collection.

Two calibrated tipping bucket rain gauges (WatchDog® model 3665R; Spectrum

Technologies, Inc., Aurora, Illinois, U.S.) were installed at the site to measure rainfall

(R) and irrigation (I) inputs for the plots. A datalogger (WatchDog® Model 450,

46

Spectrum Technologies, Aurora, Illinois, U.S.) was coupled with each bucket. Limited

irrigation was supplied only when conditions were extremely dry and signs of drought

stress such as leaf rolling were present. Crop evapotranspiration (ETc) for each plot was

then calculated using the data collected in the field according to Equation 3-1 adapted

from Allen et al. (1998)

ETc = R+I-D± ΔS (3-1)

Where ETc in the crop evapotranspiration (mm), R is rainfall (mm), I corresponds

to irrigation (mm), D is drainage (mm) collected below the root zone, ΔS is the change

in soil water storage (mm). Runoff was negligible because the experimental area was

flat and infiltration rates were high due to the coarse textured soil. Therefore, runoff was

not included in the water balance. Additionally, water use efficiency (WUE) was

calculated using Equation 3-2

WUE=AB/WI (3-2)

Where AB is aboveground dry matter (g m-2) and WI is water consumed through

evapotranspiration (kg m-2) during the growing season and expressed as g of dry matter

per kg of water evapotranspired. Meteorological data such as air temperature, radiation,

and rainfall were retrieved from the Florida Automated Weather Network (FAWN,

<fawn.ifas.ufl.edu>) station located < 1 km from the experimental site.

Bahiagrass pastures exhibit fast shoot growth during warm months (April-Oct) in

Florida. During this period, it is a common practice to harvest pasture shoots at 30-45

days intervals (Ibrikci et al., 1999; Obour et al., 2011) when grass reaches maturity and

produces seed heads plots. Consequently, bahiagrass plots were harvested every 45

days during the growing season, which resulted in 3 to 4 harvests annually. During each

47

harvest, one forage strip (1.5 x 10 m) was harvested from each plot to a 0.075 m

stubble height with a forage harvester for further analysis. The rest of the pasture was

harvested to the same height and removed from the experimental site. Forage samples

were weighed immediately and subsamples were oven dried at 65 °C for at least 72 h to

determine dry matter weight. The cumulative annual bahiagrass yield was a sum of all

individual harvests.

In the case of elephantgrass, a single harvest until physiological maturity

occurred annually (Na et al., 2015), thereby plots were harvested on 26, 19, and 10

November in 2013, 2014, and 2015, respectively. A representative 4 m section was

selected from the inner rows of the plots, stalks were cut to a 0.075 m stubble height,

and weighed immediately in the field to calculate total fresh mass. Afterwards, a sub-

sample of 3 representative stalks was chopped and fresh weight was recorded. The

samples were placed in an oven at 65 °C until constant weight was attained to

determine moisture content, which was used to estimate dry weight yield based on the

harvested area.

Statistical Analysis

Data were analyzed using analysis of variance in the PROC GLIMMIX (SAS,

2013) to determine treatment effect on biomass yield, soil VWC, and crop water use

efficiency (WUE). Treatments and seasons (divided into growing [Apr-Nov] and dormant

[Dec-Mar] seasons) were considered as fixed effects. Block was treated as a random

effect.

Treatment effects on ETc, drainage, and NO3-N leaching during growing and

dormant seasons was assessed using an autoregressive covariance structure in order

to account for the correlation between samples due to repeated measures. Drainage

48

data was log transformed, analyzed, and back-transformed means reported.

Additionally, a contrast effect analysis was conducted between crop species. PROC

UNIVARIATE was used to determine whether the data met ANOVA assumptions of

homoscedasticity and normality. Treatment effects were considered significant at α =

0.05 and mean separation was done with the Tukey-Kramer Honest Significant

Difference test.

Results

Environmental Conditions

Similar weather conditions occurred in all years during the period of maximum

plant growth (May-October). Crops received rainfall equal to 800, 626, and 808 mm

during the growing season of 2013, 2014, and 2015, respectively. Precipitation received

during this growing period was equivalent to approximately 68% of the total rainfall for

each year. Average daily air temperature was 21.5, 21.5 and 22.6 °C and solar radiation

was 17.3, 18.3, and 17.3 MJ m-2 d-1 for 2013, 2014, and 2015 years, respectively

(Figure 3-1). Environmental conditions were adequate for optimum growth and

development of both crop species. However, the site experienced a drier growing

season during 2014 with 178 mm less rainfall during this period compared to 2013 and

2015 growing seasons, and thus crops were irrigated when symptoms of drought stress

were observed. Irrigation volumes applied to the experiment were 331, 151, and 124

mm during the growing season of 2013, 2014, and 2015 respectively.

Biomass Yield

Although elephantgrass produced 98, 89, and 78% more biomass during 2013,

2014, and 2015 respectively, compared to BHG, an interaction (P<0.001) between

treatment and year affected crop yield. This was due largely to an increase in dry

49

biomass yield for bahiagrass with each growing season, whereas elephantgrass yield

decreased after the first growing season (Table 3-3). For example, elephantgrass yields

averaged 42.8 Mg ha-1 in 2013 and only 24.5 Mg ha-1 in 2014, and were not affected by

fertilization or residual application in either of the first two growing seasons. During

2015, E50BC biomass yield was less than other elephantgrass treatments and less than

E50BC biomass yield in 2014.

Soil Moisture Dynamics

There was a significant interaction between treatment and year (P<0.001) for soil

moisture stored to 1m depth. Similar soil SMC was observed among BHG, E50, and

E250 in the first year, with an average VWC of 6.8 mm d-1. Also, the application of

residuals produced on average 16% lower soil VWC in this year (Table 3-4). Moreover,

E50BC reduced soil VWC 18, 23, 24, 25% compared to E50FR, E250, E50, and BHG

respectively (Figure 3-3). SMC in the E250 was higher, particularly at 1 m soil depth.

During the second and third year, E250 exhibited on average 18% greater soil VWC

compared to BHG (Figure 3-3). Furthermore, soil VWC in the E250 was 20 and 16%

higher compared to the E50BC and E50FR, respectively. In the third year, both E50 and

E250 showed on average 15% greater SMC compared to BHG. Application of biochar

consistently produced low SMC compared the other treatments, and it was more

evident in 2013 when E50BC decreased SMC by 23% (Table 3-4).

Evapotranspiration and Water Use Efficiency

An interaction between treatment and season (growing and dormant) was

observed (P<0.001). Comparisons between the two crops species indicated that

elephantgrass exhibited higher ETc compared to BHG in all growing seasons, but ETc

did not differ among treatments during dormant seasons (Table 3-5). For example,

50

application of fermentation residual increased elephantgrass ETc by 146, 134, and

142% compared to BHG in the 2013, 2014, and 2015 growing seasons, respectively

(Table 3-5). Biochar application in the E50BC produced an average 130% greater ETc

in two of the growing seasons compared to BHG. During the 2014 and 2015 growing

seasons ETc in the E250 was on average 127% higher than BHG.

Comparing elephantgrass treatments, E50FR was among the highest ETc in all

growing seasons (Table 3-5). For example E50FR increased elephantgrass ETc 121

and 136% compared to E50 in 2014 and 2015 growing seasons, respectively. E50FR

and E50BC exhibited similar ETc values, except in the 2015 growing season when ETc

was 127% higher in the E50FR treatment compared to E50BC. No significant difference

was found in the first dormant season (DS1) among elephantgrass treatments, whereas

in the second dormant season (DS2), E50FR increased elephantgrass ETc 125 and

166% compared to E250 and E50, respectively.

Although elephantgrass exhibited higher ETc compared to BHG, the former used

water more efficiently to produce biomass (Figure 3-2). Elephantgrass WUE averaged

across all treatments was 4.0, 1.7, and 1.9 kg m-3 more than BHG in 2013, 2014, and

2015, respectively. Among elephantgrass treatments, WUE was generally greater for

the inorganic fertilization treatments compared to the amended treatments, except for

2014.

Drainage

Drainage below the crop root zone was affected by treatment, year, and their

interaction (P<0.001). Comparison between crop species demonstrated that

elephantgrass significantly reduced drainage compared to BHG in two of the growing

seasons (Table 3-6). For instance, the E50FR reduced drainage by 84, 95, and 71%

51

compared to BHG in the first, second, and third growing seasons, respectively. The

application of biochar reduced drainage 55 and 74% compared to BHG in the first and

second growing seasons, respectively. Among elephantgrass treatments, the E50FR

consistently produced less drainage compared to the other treatments (Table 3-6). In

the first growing season, E50FR reduced drainage 77% compared to E250. Additionally,

E50FR reduced drainage 92 and 68% compared to the E50 in the second and third

growing seasons, respectively (Table 3-6).

Fewer differences were detected in the dormant seasons compared to the

growing seasons. Most noteworthy, E50FR significantly reduced drainage compared to

BHG and E50. The E50FR reduced drainage 41 and 68% compared to E50

respectively, whereas a 51% drainage reduction occurred for BHG in the second

dormant season (Table 3-6).

Nitrate Leaching

Averaged across elephantgrass treatments, NO3-N leaching was reduced in all

growing seasons compared to BHG (Table 3-7). For instance, elephantgrass reduced

NO3-N leaching by 96 and 93% compared to BHG in the first and third growing season,

respectively, where rainfall was more abundant during growing seasons. No significant

differences in NO3-N leaching were observed between elephantgrass and BHG or

among elephantgrass treatments during the dormant seasons. No difference in NO3-N

leaching was observed among elephantgrass treatments during the growing seasons.

Discussion

Yield, Evapotranspiration, and Water Use Efficiency

In our study, changing a low N input bahiagrass pasture to elephantgrass for

feedstock production obtained the desired goal of high biomass productivity. With an

52

87% increase in aboveground biomass production for elephantgrass. Elephantgrass

yield was superior to bahiagrass in all years, producing more aboveground biomass per

unit of land cultivated and unit of water evapotranspired, which was attributed to the tall-

stemmed growth habit of the crop and canopy structure. Although bahiagrass exhibited

91% increase in aboveground biomass production from beginning to end of the study,

yield was consistently lower compared to elephantgrass, reflecting the high biomass

accumulation and potential of elephantgrass as bioenergy crop in the southeastern US

region. Even though yield was large, there was a decreasing yield trend across years

for elephantgrass treatments; similar yield was obtained in the first two years but not in

the last one, when E50BC had a significant lower biomass yield.

Decline in elephantgrass yield over the years was attributed to the natural

senescence of the ratoon crop that was less pronounced in the E250, indicating that

elephantgrass responds to the high availability of N in the soil but still accumulates

lower biomass compared to the first and second years. Our results were similar to

others that reported elephantgrass yield in the range of 30-47 Mg ha-1 in the

southeastern US (Fedenko et al., 2013; Woodard and Prine, 1993). The high biomass

production of elephantgrass could also mean that less land may be converted in order

to achieve bioenergy goals set by the U.S. Department of Energy (Perlack and Stokes,

2011).

The conversion of land from bahiagrass to elephantgrass does not appear to

affect the amount of water evapotranspired during the dormant season as both

perennial species showed either similar ETc or lower than BHG in the case of E50FR

and E250 during this period. Overall, the application of biochar reduced crop water

53

uptake with further reduction of biomass accumulation compared to the other

elephantgrass treatments. Similar WUE values among elephantgrass treatments

demonstrate low variability from year to year and the ability of bioenergy residuals to

maintain WUE consistently. The increase in WUE associated with bioenergy crop

systems has been reported previously (Ferchaud and Mary, 2016) and incorporating

residuals in the system may improve these beneficial WUE rates.

Soil Moisture Dynamics

Land use change from bahiagrass to elephantgrass produced alteration in the

soil water dynamics. Seasonal soil moisture content showed that during the three years,

soil VWC in the E50FR was slightly higher compared to the other treatment in the 0-10

cm soil depth, which increased water availability for elephantgrass uptake (Figure 3-3).

Our results support the findings of Wang et al. (2014), a study in which applied

fermentation residuals from bioethanol and increased water retention by 150% in sandy

soils. Biochar application in the soil consistently reduced soil VWC compared to the

other treatments. An average 17% lower soil VWC was found in E50BC compared to

E250 and E50 throughout the study. Previous studies have reported an improvement of

soil water storage capacity when biochar was applied (Glaser et al., 2002; Novak et al.,

2012). However, we found an overall reduction of soil VWC when biochar was present

in the soil, but effects on yield were measurable only in the third year. Although soil

moisture was lower under E50BC, this reduction was linked to a higher elephantgrass

ETc, which indicated that biochar favored greater crop water uptake over water stored in

the soil. Seasonal rainfall was sufficient during our study, but because of typical

irregular precipitation patterns in the U.S., it is important to maintain adequate moisture

54

stored in the soil to meet crop water demand throughout the season, which in the

E50FR and E50BC may require supplemental irrigation.

Although E250 maintained similar or higher soil VWC to the other elephantgrass

treatments in all years, it did not differ in ETc compared to the other treatments.

Erickson et al. (2012a) reported that elephantgrass root biomass appears to be

concentrated in the upper 0.1 m in coarse-textured soils, which allows water to be

temporarily stored below this soil depth. We observed high soil VWC was predominantly

in the 0.04-1.0 m soil layers below the rootzone, where water was not extracted due to

the shallow root system and high N conditions present (data not shown).

Along with increases in soil moisture in some treatments, ETc was also shown to

increase, particularly for the E50FR treatment. This may have been linked to the greater

soil moisture availability for plant uptake when the fermentation residual was applied to

the upper soil layers leading to changes in soil physical properties that increased water

retention. Other studies have found this to be the case, such as Jiang et al. (2012) that

reported that vinasse decreased soil bulk density, and increased soil porosity in

sugarcane fields. Additionally, Johnson et al. (2007) found that application of

fermentation residuals from corn stover improved soil aggregates and significantly

decreased soil bulk density. From a production perspective, higher soil VWC represents

more water stored in the soil profile available to support ETc. However, if ETc rates are

too high and dry conditions occur, there could be detrimental effects on other processes

sensitive to low SMC such as microbial activity involved in nitrification and/or

mineralization (Skopp et al., 1990). However, it is important to note that more

information is needed to determine whether E50FR equally increased the evaporation

55

or transpiration fraction of ETc, as we did not observe greater biomass accumulation

often associated with high rates of plant transpiration.

A decrease in groundwater recharge when traditional cropping systems are

converted to bioenergy crops has been reported previously (Ferchaud and Mary, 2016).

The addition of fermentation residual and biochar contributed to reduce drainage and

favored plant water use but was not associated with greater productivity. Low drainage

in the elephantgrass treated with residuals was similar to drainage quantified in the

E250 treatment indicating elephantgrass high ability to extract soil water, thereby less

water moving downwards within the soil profile. Conversely, low N input elephantgrass

(i.e. E50) could be a management option in areas with low rainfall or stressed water

resources where a sustainable trade-off between water for evapotranspiration and

groundwater recharge could be attained.

Drainage and Nitrate Leaching

Excessive application of N-based fertilizer to sustain high biomass production of

bioenergy crops increases the potential for NO3-N losses to environment with

detrimental effects (Crutzen et al., 2008). However, in this study there was no difference

between E50 and E250, which demonstrated elephantgrass high N uptake and positive

impact on water quality by reducing NO3-N content in drainage moving belowground.

Furthermore, elephantgrass greatly reduced NO3-N leaching compared to bahiagrass,

and the reduction was more evident in the first growing season.

Although both crop species evaluated in this study are perennial, elephantgrass

exhibited greater water and N (i.e. inorganic fertilizer or bioenergy residual) uptake

efficiency regardless of the source, which was reflected in lower drainage and NO3-N

losses compared to bahiagrass.

56

Environmental concerns arise from bioenergy crops replacing grasslands and

native ecosystems. However, proper nutrient management of elephantgrass can reduce

the potential groundwater pollution associated with N fertilization. It was demonstrated

that elephantgrass receiving only 50 kg N ha-1 could attained high biomass yield and the

addition of bioenergy residuals did not influence the result.

Nonetheless, it is important to note that within elephantgrass, NO3-N leaching did

not differ between plots treated with bioenergy residuals versus conventionally fertilized

elephantgrass, which indicated elephantgrass high N uptake capacity. Reduction of

NO3-N leaching is an important ecosystem service of elephantgrass cropping systems,

which was not observed in other bioenergy crops grown in northern regions of the U.S.

or to the reported detrimental effects of growing corn (Zea mays L.) for bioenergy

purposes (Hoekman, 2009). Therefore, potential increase of land dedicated to

cultivation of elephantgrass in the southeastern U.S. does not seem to degrade the

quality of the water recharging the aquifer, even if high N rates are applied (~250 kg ha-

1). Additionally, land application of fermentation and biochar residuals to elephantgrass

did not appear to increase NO3-N loss susceptibility to groundwater. Thus, these

materials could be incorporated in the soil as a strategy to increase carbon

sequestration and minimize their disposal on surface waterbodies and landfills.

Summary

The conversion of low N input bahiagrass pastures to elephantgrass for biomass

production will have a significant impact on crop water dynamics. Quantification of

average daily water consumption between these two crop species demonstrated

elephantgrass greater ETc during growing seasons and similar ETc during dormant

seasons, thereby elephantgrass significantly reduced drainage below the root zone.

57

Well managed elephantgrass can reduce NO3-N leaching compared to bahiagrass.

Land application of bioenergy residuals could improve elephantgrass water uptake and

increase ETc, thereby reducing water available for other ecosystem functions.

Particularly, the use of fermentation residual as soil amendment favored greater

elephantgrass ETc. Conversion of grassland in the southeastern U.S. to elephantgrass

increases the productive use of evapotranspiration without increasing agricultural water

appropriation. Combination of low N fertilization (i.e. 50 kg ha-1) with bioenergy residuals

could be a suitable agronomic practice that maintains elephantgrass biomass

productivity without affecting underground water quality.

58

Table 3-1. Soil physical characteristics and Mehlich-1 soil test results (0-20 cm) for the experimental site at Citra, Florida.

Soil property Value

Sand (%) 98.4 Clay (%) 0.4 Silt (%) 1.2 Bulk density (g cm-3) 1.61 pH (water) 6.8 P (mg kg-1) 64.7 K (mg kg-1) 12.2 Ca (mg kg-1) 20.9 Mg (mg kg-1) 925.2 CEC a (cmolc kg-1) 7.7 a CEC: Cation Exchange Capacity

Table 3-2. Chemical and physical characteristics of the biochar residual and fermentation residual used in the study a

Units

Biochar

Fermentation

Residual

Nitrogen g kg-1 5.3 40.2

Phosphorus g kg-1 1.8 0.36

Potassium g kg-1 1.6 0.07

Sulfur g kg-1 1.2 3.4

Zinc g kg-1 2.4 0.03

Manganese g kg-1 0.2 0.03

Calcium g kg-1 19.6 16.5

Boron mg kg-1 80 0.01

Aluminum mg kg-1 3659 0.14

Particle size μm 325 ----

Bulk density g cm-3 0.4-0.6 ----

pH ---- 9.4 5.1 a Biochar residual and fermentation residual mineral content determined by laboratory analysis

prior to land-application in the experiment. Particle size and bulk density data was obtained from biochar manufacturer.

59

Table 3-3. Interactive effect of year and treatment on mean (n=4) annual total aboveground dry matter yield. Treatments include bahiagrass with 50 kg N ha-1 (BHG), elephantgrass with 50 kg N ha-1 (E50), elephantgrass with 50 kg N ha-1 plus fermentation residual (E50FR), elephantgrass with 50 kg N ha-1 plus biochar residual (E50BC), elephantgrass with 250 kg N ha-1 (E250). Fertilizer and residuals were applied at the beginning of each growing season.

Treatment 2013 2014 2015

Mg ha-1

BHG 0.5 b C a 3.2 b B 5.4 c A E50 48.1 a A 22.0 a B 23.0 ab B E50FR 41.0 a A 26.9 a B 23.0 ab B E50BC 39.3 a A 25.9 a B 16.6 b C E250 42.7 a A 23.2 a B 32.7 a AB a The values within columns followed by a different small letter represent a treatment difference

at P < 0.05. Values within rows followed by a different capital letter represent a year difference at P < 0.05.

Table 3-4. Effect of treatment on daily average of soil volumetric water content to 1 m soil depth. Treatments were: bahiagrass with 50 kg N ha-1 (BHG), elephantgrass with 50 kg N ha-1 (E50), elephantgrass with 50 kg N ha-1 plus fermentation residual (E50FR), elephantgrass with 50 kg N ha-1 plus biochar residual (E50BC), elephantgrass with 250 kg N ha-1 (E250) grown in Citra, Florida during 2013, 2014, and 2015 a.

Treatment Year

2013 2014 2015

Soil moisture content (mm d-1)

BHG 6.9 a b 9.7 bc 10.0 c E50 6.8 a 9.8 b 10.9 b E50FR 6.3 b 9.6 bc 10.4 bc E50BC 5.2 c 9.2 c 9.9 c E250 6.7 ab 11.3 a 12.6 a a Data analysis for 2013, 2014, and 2015 corresponds to each calendar year. b Mean values within a column sorted by year followed by the same letter are not significantly

different using Fisher’s LSD procedure at P=0.05 level of significance.

60

Table 3-5. Effect of treatment on average daily crop evapotranspiration (ETc) for each of three growing seasons (GS; May - Oct) and two dormant seasons (DS; Nov - April) from 2013 to 2015 for the study. Treatments include bahiagrass with 50 kg N ha-1 (BHG), elephantgrass with 50 kg N ha-1 (E50), elephantgrass with 50 kg N ha-1 plus fermentation residual (E50FR), elephantgrass with 50 kg N ha-1 plus biochar residual (E50BC), elephantgrass with 250 kg N ha-1 (E250). Fertilizer and residuals were applied at the beginning of each growing season.

Daily ETc by season 2013-2014 2014-2015 2015 Treatment GS1 DS1 GS2 DS2 GS3

mm d-1

BHG 4.12 c 2.79 a 3.32 c 2.06 ab 3.14 c E50 5.73 ab 2.47 a 3.69 bc 1.46 c 3.28 c E50FR 6.02 a 3.18 a 4.46 a 2.42 a 4.45 a E50BC 5.45 ab 2.53 a 4.26 ab 2.10 ab 3.51 bc E250 4.90 bc 2.47 a 4.17 ab 1.93 b 4.01 ab P-value <0.001 0.038 <0.001 <0.001 <0.001 BHG vs EG P-value <0.001 0.541 <0.001 0.496 0.001 a Mean values within a column followed by the same letter are not significantly different at p =

0.05 level of significance using Tukey-Kramer Honest Significant Difference. b Comparison between cropping systems within growing and dormant seasons.

61

Table 3-6. Effect of treatment on average daily drainage for each of three growing seasons (GS; May - Oct) and two dormant seasons (DS; Nov - April) from 2013 to 2015 for the study. Treatments include bahiagrass with 50 kg N ha-1 (BHG), elephantgrass with 50 kg N ha-1 (E50), elephantgrass with 50 kg N ha-

1 plus fermentation residual (E50FR), elephantgrass with 50 kg N ha-1 plus biochar residual (E50BC), elephantgrass with 250 kg N ha-1 (E250). Fertilizer and residuals were applied at the beginning of each growing season.

Treatment Daily drainage by season 2013-14 2014-15 2015

GS1 DS1 GS2 DS2 GS3 mm d-1

BHG 1.97 a 1.64 ab 0.97 a 0.95 ab 1.81 a

E50 0.55 bc 1.88 a 0.65 ab 1.45 b 1.64 a

E50FR 0.32 c 1.11 b 0.05 c 0.47 c 0.52 b

E50BC 0.88 bc 1.74 ab 0.25 bc 0.83 bc 1.40 ab

E250 1.37 ab 1.76 ab 0.33 bc 0.88 bc 0.94 ab

P-value <0.001 0.057 <0.001 <0.001 <0.009

BHG vs EG P-value <0.001 0.952 <0.001 0.773 0.021 a Mean values within a column followed by the same letter are not significantly different at p =

0.05 level of significance using Tukey-Kramer adjustment.

62

Table 3-7. Effect of treatment on average daily drainage for each of three growing seasons (GS; May - Oct) and two dormant seasons (DS; Nov - April) from 2013 to 2015 for the study. Treatments include bahiagrass with 50 kg N ha-1 (BHG), elephantgrass with 50 kg N ha-1 (E50), elephantgrass with 50 kg N ha-

1 plus fermentation residual (E50FR), elephantgrass with 50 kg N ha-1 plus biochar residual (E50BC), elephantgrass with 250 kg N ha-1 (E250). Fertilizer and residuals were applied at the beginning of each growing season.

Treatment Daily NO3-N Leaching by Season 2013-14 2014-15 2015

GS1 DS1 GS2 DS2 GS3 g ha-1 d-1

BHG 112.2 a 3.98 a 0.99 a 1.74 a 5.68 a E50 3.87 b 1.70 a 0.37 a 0.76 a 0.51 b E50FR 2.68 b 1.10 a 0.24 a 0.35 a 0.41 b E50BC 4.52 b 0.74 a 0.21 a 0.89 a 0.34 b E250 6.07 b 1.82 a 0.30 a 2.01 a 0.32 b P-value <0.001 0.073 0.299 0.125 0.002 BHG vs E P-value <0.001 0.011 0.040 0.197 <0.001 a Mean values within a column followed by the same letter are not significantly different at p =

0.05 level of significance using Tukey-Kramer adjustment.

63

Figure 3-1. Minimum, average, maximum daily air (°C) at 2 m, daily and monthly solar radiation (MJ m-2 d-1), and rainfall

(mm) during 2013, 2014, and 2015 in Citra, FL.

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Figure 3-2. Water use efficiency of bahiagrass fertilized with 50 kg N ha-1 (BHG) and elephantgrass during 2013, 2014, and 2015 growing seasons [May-October] in Citra, Florida. Elephantgrass received one of the following treatments: 50 kg N ha-1 (E50); 50 kg N ha-1 + fermentation residual (E50FR); 50 kg N ha-1 + biochar residual (E50BC); 250 kg N ha-1 (E250). Treatments with the same letters within year were not different based on Tukey-Kramer honest significant difference (P<0.05).

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Figure 3-3. Rainfall and volumetric water content at 10 cm (a-c), 20 cm (d-f), 30 cm (g-i),

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1 (E50), 50 kg N ha-1 + fermentation residual (E50FR), 50 kg N ha-1 + biochar residual (E50BC), and elephantgrass + 250 kg N ha-1 (E250) grown during 2013-2015 in Citra, Florida, U.S.

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66

CHAPTER 4 COMPARISON OF BIOMASS PRODUCTION AND WATER AND NITRATE

DYNAMICS BETWEEN SWEET SORGHUM AND A COTTON-PEANUT ROTATION IN THE SOUTHEASTERN UNITED STATES

Background

Cotton (Gossypium hirsutum L.) and peanut (Arachis hypogaea L.) are two major

row crops generally grown in rotation in the southeastern United States (U.S.). Total

area in 2016 dedicated to production of these crops was about 1.07 million ha in the

combined states of Alabama, Florida, and Georgia (NASS, 2016). The adoption of

alternative crops that offer additional ecosystem and economic services to this limited

rotation has been suggested previously. For instance, Katsvairo et al. (2007) found that

when bahiagrass (Paspalum notatum Flugge) was introduced in a conventional

peanut−cotton rotation, yield was 39% greater when peanut followed bahiagrass than

when cotton was the preceding crop. Additionally, they reported similar cotton lint yields

between the conventional and bahiagrass rotation. Therefore, the inclusion of other

crops in the rotation not only increases diversity, but also can potentially benefit yield of

crops in the rotation and increase farmer competitiveness.

Other annual grass species also have agronomic characteristics that make them

potential candidates to rotate with peanut and cotton. Among them, sweet sorghum

[Sorghum bicolor (L.) Moench], an annual crop, is a promising candidate because of its

low input requirements and high adaptability to environmental conditions typical of the

southeastern U.S. (Erickson et al., 2011). Additionally, sweet sorghum has

demonstrated potential as dedicated bioenergy crop due to its versatility to fit thermal

and chemical biomass conversion platforms for biofuels and energy generation (Rooney

et al., 2007).

67

Motivated by the interest to produce energy from plant biomass and an

expanding market for biofuels, farmers may divert current land used for cotton-peanut

rotation to sweet sorghum for bioenergy production. The change of cropping systems

could potentially alter ecosystem services, particularly water, carbon, and crop N

dynamics in ways that are poorly understood. Sweet sorghum has been reported to

produce up to 70 Mg fresh biomass ha-1 annually in the southeastern U.S. (Erickson et

al., 2011). Moreover, sweet sorghum has been characterized as highly adaptable to

adverse environmental conditions with minimum yield loss and one of few crops fit for

different conversion methods for biofuel production (Regassa and Wortmann, 2014).

Evidence suggests that sweet sorghum has physiological mechanisms to regulate its

metabolism to withstand soil salinity, drought, and flooding for longer periods than other

biofuel crops such as sugarcane (Saccharum spp.) and corn (Zea mays L)

(Vasilakoglou et al., 2011). Significant research has been carried out on sweet sorghum

management practices in the southeastern U.S. assessing its performance under low N

and K inputs (Adams et al., 2015a), different planting dates (Erickson et al., 2011), and

planting arrangements and densities to maximize biomass yield and make production

compatible with equipment and practices of existing cropping systems such as

sugarcane (Adams et al., 2015b). These studies consistently demonstrated the high

versatility of sweet sorghum to a myriad of management practices and stress conditions

with minimum yield loss. Consequently, sweet sorghum could be grown as a dedicated

bioenergy crop for lignocellulosic biofuel production (Zegada-Lizarazu and Monti, 2012)

as a new crop phase to diversify the current cotton-peanut rotation.

68

The increasing demand for renewable energy from lignocellulosic crops to

accomplish government energy goals (US-DOE, 2009) is also accompanied by

increasing production of mineral-rich residuals (i.e. byproducts) from the conversion of

biomass to fuels either by biochemical or thermochemical processes. Bioenergy

facilities processing feedstocks to constituent sugars that are subsequently fermented to

ethanol also produce a fermentation residual commonly referred as vinasse (Wilkie et

al., 2000). Additionally, production of biogas from plant biomass generates a solid

residue named biochar (Lehmann and Joseph, 2009). Both of these materials are

nutrient-rich but if not properly disposed may deteriorate ecosystems health.

Limited information exists regarding the effects of land-applying vinasse from

lignocellulosic feedstock on productivity of dedicated bioenergy crops and ecosystems

services such as soil moisture and water quality (Gell et al., 2011). Conversely, biochar

literature has grown exponentially in the last decade with a strong focus on carbon

sequestration and agronomic benefits attributed to amelioration of specific soil

constraints that depress crop productivity (Liu et al., 2013). Some of the benefits

reported for biochar application to soil include compaction reduction (Hardie et al.,

2014), increase in soil moisture content (Glaser et al., 2002) and stimulation of larger

root length density in the soil layers where it is applied (Reyes-Cabrera et al., 2017).

Therefore, biochar could increase sweet sorghum productivity and resilience to abiotic

factors such as drought spells. Consequently, biochar and vinasse residuals could be

used to supplement sweet sorghum nutritional requirements and increase the

productivity of the bioenergy cropping system through nutrient recovery potentially

reducing reliance on synthetic fertilizers. However, limited information exists on how

69

returning bioenergy residuals can affect biomass production and ecosystem services of

bioenergy cropping systems. Therefore, there is a clear need to assess and describe

ecosystem services offered by bioenergy crops beyond biomass production, especially

when vinasse and biochar residuals are land-applied.

The large removal of biomass in bioenergy crops poses detrimental effects on

soil and water resources due to minimum amount of crop residue left in the field after

harvest, whereas in conventional cropping systems only the grain is harvested and plant

biomass is either left on the surface or incorporated to the soil, which reduces soil

evaporative losses and recycle nutrients back to the soil (Laird, 2008).

Therefore, the objectives of this study were to compare aboveground biomass

production between a traditional cotton-peanut rotation and sweet sorghum grown for

feedstock. Furthermore, we evaluated the effects of land application of vinasse and

biochar residuals to sweet sorghum on aboveground biomass accumulation, soil

moisture dynamics, and NO3-N leaching. The hypotheses tested were i) sweet sorghum

exhibits higher biomass accumulation compared to cotton and peanut, ii) application of

bioenergy residuals combined with minimum N fertilization rate could suffice sweet

sorghum high biomass productivity and iii) sweet sorghum treated with vinasse and

biochar residual may exhibit lower NO3-N leaching compared to cotton, peanut, and

sweet sorghum fertilized with commercial N sources.

Materials and Methods

Site Description and Experimental Design

A field experiment was conducted at the University of Florida West Florida

Research and Education Center, near Jay, FL (30° 46’ N, 87° 08’ W) from 2013 to 2015.

The soil was a Dothan fine sandy loam (fine-loamy, kaolinitic, thermic, Plinthic

70

Kandiudult) with 74% sand, 16%, 10% clay, 2% organic matter, and pH 6. The

experimental site was maintained fallow for three years before experiment initiation, and

previously was under a conventional cotton-cotton-peanut rotation.

The experiment was established as a randomized complete block design with

four replications. Treatments consisted of different cropping systems: i) cotton + 150 kg

N ha-1 (COT); ii) peanut + 30 kg N ha-1 (PEA); iii) sweet sorghum + 30 kg N ha-1 (S30);

iv) sweet sorghum + 30 kg N ha-1 + 8 Mg vinasse ha-1 (S30V); v) sweet sorghum + 30

kg N ha-1 + 5 Mg biochar ha-1 (S30B); and vi) sweet sorghum + 150 kg N ha-1 (S150).

All phases of the conventional cotton-peanut rotation were present in all years rotating

plots from peanut to cotton and vice versa. Plots were 110 m2 (10 by 11 m) and

separated by 9 m buffer areas that were maintained fallow.

Cultural Practices

The experimental area was disk-tilled to 0.15 m soil depth before planting each

year. Cotton (10-50 Pioneer®), peanut (Georgia-06G), and sweet sorghum (M81E),

were sown on May 28, 2013; June 17, 2014; and June 2, 2015. Crops were planted in

0.9 m row spacing, and plant spacing within row was 10, 5, and 5 cm for cotton, peanut,

and sorghum, respectively. Seeds were planted at 2 cm depth for all crops.

Each year, all plots received an initial rate of 30 kg N ha-1 from 16-4-8 N-P2O5,

K2O, respectively, blended granular fertilizer broadcast after planting. Sweet sorghum

receiving 150 kg N ha-1 and COT plots were fertilized with additional 120 kg N ha-1 as

ammonium sulfate 45 d after the first N application. Weeds were suppressed in all plots

mechanically or with the use of herbicides based on grower practices in the area (Ferrell

et al., 2015a, 2015b, 2015c).

71

Land application rates of bioenergy residuals to sweet sorghum were based on

estimated residual production from reported sweet sorghum dry matter yield of 16 Mg

ha-1 yr-1 with 50% fiber content (Erickson et al., 2012b) and 50% biomass conversion to

biofuel and recovery efficiency from either combustion or digestion conversion

processes (McKendry, 2002). Thus, based on these estimates, vinasse was applied at a

rate of 8 Mg ha−1 after evaporating ethanol residues and concentrating the residual

approximately 2 fold, and at 5 Mg ha−1 of powdery biochar produced by pyrolysis of

reclaimed wood at 760 °C obtained from a commercial source (Table 4-1) (Standard

Purification, Dunnellon FL, U.S.) was suspended in water and sprayed with an air

compressor on the soil surface to minimize wind losses. Subsequently after application,

residuals were incorporated in the top 10 cm of soil with a field cultivator.

Lysimeters with 1.47 m height, 0.25 m diameter, and 0.051 m2 collection area

(Drain Gauge G3, Decagon Devices, Pullman, Washington) were installed in the center

of each plot on 12 March 2013 to collect water samples below the root zone. During

lysimeter installation, soil was mechanically removed and filled back in by depth to

minimize disturbance. The depth from soil surface to the lysimeter collection point was 1

m, to allow water percolating below crops root zone. Every two weeks, a portable

vacuum pump was used to collect the drainage (D) from the lysimeter reservoir. The

volume of drainage was measured and three replicate subsamples were collected in 20

mL scintillation vials. Immediately after collection, sulfuric acid was added to each

sample, and samples were frozen at −20 °C, for subsequent NO3-N concentration

analysis at the Analytical Research Laboratory (University of Florida, Gainesville).

72

Soil volumetric water content (VWC) was measured in each plot 1 m from the

lysimeter access ports using a multi-sensor soil moisture probe (Profile Probe PR2,

Delta-T Devices Ltd, Cambridge, UK). VWCs at six depths (0.1, 0.2, 0.3, 0.4, 0.6 and 1

m) were used to calculate the water stored in the soil to a depth of 1 m by multiplying

the sensor reading by the respective volume of each layer. Readings were collected

every two weeks at the same time as leachate collection.

Solar radiation, air temperature, and precipitation data were collected from a

Florida Automated Weather Network (FAWN, <fawn.ifas.ufl.edu>) weather station

located 200 m from the experimental site (Table 4-2). The experiment did not receive

irrigation, except during very dry conditions in May 2013 and June 2014 when 3.2 and

19.1 mm were applied, respectively, to facilitate seed establishment and germination. A

rain gauge coupled with a datalogger (WatchDog 1120, Spectrum Technologies,

Aurora, Illinois) was installed in the field to measure water inputs.

Sweet sorghum was harvested at late soft dough stage, when biomass

accumulation reaches a maximum and sugar content in stalks is ideal for recovery for

ethanol production (Tarpley et al., 1994), which occurred 127, 150, and 121 days after

planting (DAP) for 2013, 2014, and 2015, respectively. A 4−m section of a middle row

was cut in each plot to 7 cm stubble height. Total aboveground fresh weight was

recorded. A subsample of 7 representative shoots was fresh-weighed, partitioned to

leaves, panicles, and stalks and placed separately in an oven at 65 °C until constant

weight was reached, when samples were weighed to determine dry matter and yield.

Stalk samples were chipped using a (DEK Chipper Shredder Model CH1; GXI Outdoor

Power, Clayton, NC), and subsamples were ground with a Wiley mill (Thomas Scientific,

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Swedesboro, NJ) to pass a 2-mm sieve. Subsequently, a subsample (3 g) of fine sweet

sorghum stalk material was diluted in 29 mL of deionized water, placed in an oven at 55

°C for 24 h and centrifuged at 45 g for 3 minutes, 3 mL of the supernatant were used for

brix determination with a portable refractometer (Atago PAL-1, Atago USA Inc.

Bellevue, WA) following a modified procedure reported by Smith et al. (1964). The brix

value of the juice was averaged from two readings per sample.

Two representative cotton plants per plot were cut at ground level before

defoliation, when at least 25% of bolls were open, and samples were oven-dried at 65

°C for 72 h to measure dry matter accumulation. COT plots were harvested 35 days

after chemical defoliation when an average of 70% bolls were open using a two-row

cotton picker, which occurred 175, 167, and 181 DAP in 2013, 2014, and 2015,

respectively.

Before harvest, two peanut plants were collected (aboveground tissue) to

determine plant dry weight. Peanut harvest time was determined with the hull scape

method (Williams and Drexler, 1981). Two center rows of peanut were mechanically

dug in each plot at 153, 113, and 133 DAP in 2013, 2014, and 2015, respectively. Pods

and vines were allowed to air dry for approximately 5 d, and then mechanically picked.

Final yield was adjusted to 14% moisture.

Data Analysis

Data were analyzed using PROC GLIMMIX of SAS 9.4 (SAS Institute, 2013) to

determine cropping systems effect on aboveground dry matter yield, soil moisture

content, and NO3-N concentration in leachates. Cropping system, year and their

interaction were considered as fixed effects, while blocks as a random effect. Normal

distribution assumption and homogeneity of variance was analyzed with PROC

74

UNIVARIATE of SAS 9.4. Tukey-Kramer honest significant difference method was used

for treatment mean separation (α=0.05).

Results

Weather data collected during the study showed that crops experienced similar

environmental conditions in all years, except during the beginning of the 2014 growing

season, which was characterized by 234 and 146 mm more precipitation compared to

2013 and 2015, respectively (Table 4-2). Average air temperature during this study was

23, 24 and 25 °C in the summer of 2013, 2014, and 2015, respectively.

Biomass Accumulation

There was a significant interaction between treatment and year (P<0.001) for

aboveground dry matter. In 2013, PEA produced 88% less aboveground biomass

compared to the other treatments. All sweet sorghum treatments and COT yielded on

average 20 Mg ha-1 of biomass (Table 4-3). In 2014, relatively late sowing date

combined with a heavy sugarcane aphid (Sipha flava) infestation severely reduced

sweet sorghum yield (Erickson et al., 2011). All sweet sorghum treatments exhibited low

dry matter in this year compared to the 20 Mg ha-1 commonly reported in the

southeastern U.S. (Adams et al., 2015a). Furthermore, COT and PEA produced 20 and

9 Mg ha-1 more aboveground biomass, respectively, than all sweet sorghum treatments.

In the third year, aboveground biomass ranged between 18 and 22 Mg ha-1 for COT and

all sweet sorghum treatments treated with or without bioenergy residuals. Conversely,

PEA produced 74% less aboveground biomass compared to the other treatments.

However, when belowground (pod yield) component of peanut plants was added to the

aboveground biomass, total PEA biomass resulted in 6, 9, and 11 Mg ha-1 in 2013,

2014, and 2015, respectively (data not shown), but even with the addition of pod yield in

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the PEA treatment, peanut biomass remained consistently lower compared to the rest of

treatments within years. No differences in aboveground biomass were found among

sweet sorghum treatments in 2015.

All sweet sorghum treatments yielded approximately 20 Mg ha-1 in two out of

three years, and nutrient input did not affect biomass production. Thus, despite the

application of additional 120 kg N ha-1 in S150, both S30 and S150 produced on

average 20 and 19 Mg ha-1 biomass in 2013 and 2015, respectively (Table 4-3).

Furthermore, applying bioenergy residuals in S30B and S30V did not promote additional

gains in aboveground biomass yield compared with S30.

Soil Moisture Dynamics

Differences in soil moisture content (SMC) were detected in the top 1 m of the

soil profile (P<0.001). In all years, S30B increased on average SMC 29% in the 0.1 m

soil depth compared to both COT and PEA and similar results were observed in the 0.2

m soil depth with S30B increasing SMC 32% compared to COT, but was only 23%

higher than PEA in two out of three years. The reported benefits of biochar application

on SMC were also measured in deep soil layers (Table 4-4). For example, at 0.6 m soil

depth, S30B produced 19 and 7% greater SMC compared to COT in 2014 and 2015,

respectively. The S30 also produced high SMC at the 0.6 m soil depth, which was in the

order of 23% greater SMC compared with COT in 2014 and 41% greater compared to

PEA in 2015.

Within the sweet sorghum treatments, the addition of biochar in the S30B

increased SMC in the 0.1 m soil depth in the order of 42, 29, and 19% compared to the

rest of sweet sorghum treatments in 2013, 2014, and 2015, respectively. Similarly, at

0.2 m soil depth, S30B increased SMC on average 31% compared to the other sweet

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sorghum treatments. Increments in SMC due to vinasse application were noticeable at

the 0.3 m soil depth in all years. In 2013, S30V had 0.26 cm3 cm-3 volumetric water

content, which was the same in S30B, but 37 and 62% higher compared to S30 and

S150, respectively. Furthermore, in this same soil depth, S30, S30V, and S30B favored

similar SMC, which were 49 and 60% higher than S150 in 2014 and 2015, respectively.

Differences in SMC were measured at 0.4 m soil depth, with S30B increasing

SMC 17 and 19% compared to all other sweet sorghum treatments in 2014 and 2015,

respectively. At 1.0 m soil depth, S150 had 9 and 12% lower SMC compared to the rest

of sweet sorghum treatments in 2014 and 2015, respectively.

NO3-N Concentration in Leachates

A significant interaction of treatments by year on NO3-N concentration in

leachates was observed (P<0.001).

In 2013, similar average NO3-N concentration was measured for COT and S150,

which was 34.4 mg L-1 whereas PEA, S30, S30B, and S30V averaged 12.9 mg NO3-N

L-1. In 2014, NO3-N concentration in leachates was low for the majority of treatments

with an average 10.6 mg L-1(Table 5). The only exception was S150 with 101% greater

NO3-N concentration compared to the other treatments. In 2015, PEA produced the

highest NO3-N concentration in leachates with 14.5 mg L-1. The S150 and S30V had

similar results with an average concentration of 8.7 mg NO3-N L-1.

No differences were found among S30, S30B, and S30V treatments during the

length of the experiment with an overall average of 10.5, 9.6, and 4.8 mg NO3-N L-1 in

2013, 2014, and 2015, respectively. However, sweet sorghum treated with low N rate

with or without bioenergy residuals produced in average 26 and 12 mg NO3-N L-1 less

than S150 in 2013 and 2014, respectively.

77

Discussion

Results of this study demonstrated that sweet sorghum fertilized with 30 kg N ha-

1 produced biomass yield similar to 21.9 and 19.9 Mg ha-1 reported by Singh et al.

(2012) and Adams et al. (2015a), respectively, under soil and environmental conditions

of the southeastern U.S., and it could be considered as an alternative crop to be grown

in the region. The high biomass production of sweet sorghum is attractive for feedstock

purposes, especially as demand for more biofuel in the U.S. (US-DOE, 2016).

Sweet sorghum high biomass production with relatively low N requirements

(Adams et al., 2015a) could be a nutrient management option further explored due to its

more efficient N uptake efficiency compared to COT. The lower amount of N fertilizer

applied to S30 (30 kg N ha-1) compared to COT (150 kg N ha-1) not only represents

significant savings in fertilizers, but also higher efficiency when considering biomass

production per unit of fertilizer used.

Results demonstrated that sweet sorghum yield was not increased by high N

fertilization (i.e. S150) or the application of bioenergy residuals. It has been

demonstrated that biomass productivity in the order of 20 Mg ha-1 could be achieved

with N input equal to 70 kg ha-1 in the southeastern U.S. (Adams et al., 2015a). For the

duration of the present study, the application of 30 kg N ha-1 was sufficient to fulfill

sweet sorghum N demand, and thus additional N amounts were not necessary to reach

sweet sorghum yields similar to those reported by Adams et al. (2015a), which

highlights sweet sorghum high N use efficiency. Sawargaonkar et al. (2013)

demonstrated that with 90 kg N ha-1 three sweet sorghum genotypes reached a yield

plateau and further addition of N did not increase biomass production. Although in our

study we did not conduct a dose−response of yield to N rate, there was no increased

78

sweet sorghum yield in response to the addition of 150 kg N ha−1, and results were

similar to applying only 30 kg N ha−1 with or without addition of bioenergy residuals.

Additionally, previous studies suggested sweet sorghum N uptake tends to reach

an optimum around 120 kg N ha-1, which indicates adequate nutrient input

(Sawargaonkar et al., 2013; Adams et al., 2015a). Beyond this point, the addition of

commercial nitrogen fertilizer will likely increase nitrate leaching as indicated by greater

nitrate concentrations in the leachate of S150 compared to the S30 treatment in two of

three years. Therefore, low N rates used in sweet sorghum represent potential cost

savings, and also reduction in N loads to groundwater.

The application of bioenergy residuals did not produce beneficial effects on

sweet sorghum biomass accumulation. This finding might not encourage farmers to

apply these residuals if only biomass productivity is considered. However, land

application of vinasse and biochar residuals to sweet sorghum did not produce

detrimental effects on crop yields and did not appear to increase NO3-N loss to

groundwater. Thus, these materials could be incorporated in the soil as a strategy to

increase carbon sequestration (Perlack et al., 2005).

Biochar Effects on Soil Moisture Retention

Besides increasing soil carbon (C) content and mitigating its release to the

atmosphere (Lehmann, 2007), biochar has been reported to increase soil WHC (Jeffery

et al., 2011). In the present study, biochar incorporation in the soil increased moisture

content not only in the soil layers where it was incorporated (i.e. 0-20 cm) but also in

deeper layers, indicating that greater moisture content could be available in the root

zone for plant uptake for longer period than for other treatments. The increase in soil

WHC is likely due to the interaction between biochar and soil particles, which modified

79

soil porosity (Barnes et al., 2014). Soil treated with biochar had been shown to decrease

soil bulk density (Laird et al., 2010b), and thereby increase soil porosity that can be

filled with water.

Results from this study provide evidence that biochar alters soil moisture

dynamics beyond the soil depth where it is applied by increasing soil water retention

downward in the soil profile. The increase of water content at deeper soil layers could

be associated with vertical movement of biochar particles that was observed when soil

samples were collected annually. Soil cores extracted from biochar treated plots (S30B)

exhibited a characteristic black color not only in the top 0.2−m where the biochar was

incorporated but also in soil sections between 0.4- to 1-m depth, but this color pattern

was absent in all other sweet sorghum treatments (data not shown). The increase of

SMC content was not observed in the other sweet sorghum treatments compared to

S30B, which indicated that biochar indeed increased moisture retention time in the soil

profile. Our findings are in accordance with Karhu et al. (2011), whom reported 11%

increase in WHC when 9 Mg biochar ha−1 were added to the soil compared to the non-

treated control.

Although S30B increased soil moisture content, this did not translate into

increased sweet sorghum yield, which was likely due to the fact that water was not

limiting yield in any of the treatments. However, biochar could contribute to increased

drought avoidance and potentially improved yields (Glaser et al., 2002), with minimum

need for supplementary irrigation. For example, biochar amendment positively

influenced soil water dynamics in a loamy soil from China, which resulted in yield

improvement in maize (Zea mays L.) under rainfed conditions (Zhang et al., 2012).

80

Additionally, high retention of water from precipitation in biochar-amended soil (Downie

et al., 2009) could also reduce irrigation frequency and intensity and nutrient leaching

compared to nontreated soils.

Nitrate Concentration in Leachates

The cultivation of sweet sorghum does not appear to increase the risk of NO3-N

loss to groundwater compared to the current conventional cotton-peanut fertilization

practices. The only exception was observed with the application of 150 kg N ha-1 to

sweet sorghum, which increased the concentration of NO3-N in the leachate at 1 m

depth. Thus, potential adoption of sweet sorghum fertilized with 30 kg N ha-1 with or

without addition of bioenergy residuals represents a nutrient management practice that

could be used to supply crop demand without increasing the risk of nitrate leaching.

Although utilization of vinasse and biochar as soil amendments added 300 and

27 kg N ha-1, it appears that fate of N from these materials was not as NO3-N in the

leachate, likely not due to runoff, which is generally negligible on the soils in this region.

These findings indicated that the fate of this applied N was either immobilized in the soil

and/or lost as gaseous forms to the atmosphere, but further research is needed to

identify the fate of this applied N. Nevertheless, data from this study support the

application of these bioenergy residuals to sweet sorghum cropping systems with

minimum detrimental effects on the NO3-N concentration in leachates. However, issues

with other minerals present in these materials and their potential toxic effect need

further study, especially if application of residuals is performed at planting, thereby

impacting seed germination and seedling establishment (Gell et al., 2011).

81

Summary

Conversion of conventional agricultural systems in the southeastern U.S. to

sweet sorghum or possibly incorporating sweet sorghum intro current cropping systems

could help the U.S. meet its demand for biofuels. In addition to its relatively high yield

potential, results from the present study indicated that sweet sorghum was not likely to

negatively affect water quality (i.e. NO3-N) when compared to current common cropping

systems of cotton and peanut. Application of biochar or vinasse did not enhance yield of

low input sweet sorghum over the duration of the study, but they did affect soil moisture

properties without any negative effects on nitrate concentration in leachates.

Incorporation of biochar increased soil moisture content, especially in the 0-0.2 m soil

depth, which could favor sweet sorghum resilience in the occurrence of intermittent

drought conditions.

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Table 4-1. Chemical and physical characteristics of the biochar residual and vinasse residual used in the study a.

Units

Biochar

Vinasse

Nitrogen g kg-1 5.3 40.2

Phosphorus g kg-1 1.8 0.36

Potassium g kg-1 1.6 0.07

Sulfur g kg-1 1.2 3.4

Zinc g kg-1 2.4 0.03

Manganese g kg-1 0.2 0.03

Calcium g kg-1 19.6 16.5

Boron mg kg-1 80 0.01

Aluminum mg kg-1 3659 0.14

Particle size μm 325 ----

Bulk density g cm-3 0.4-0.6 ----

pH ---- 9.4 5.1 a Biochar residual and vinasse residual mineral content determined by laboratory analysis prior

to land-application in the experiment. Particle size and bulk density data was obtained from biochar manufacturer.

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Table 4-2. Minimum, maximum, average air temperature measured at 2 m, precipitation, and solar radiation during May-October at the West Florida Research and Education Center located in Jay, Florida.

Temperature Solar radiation

Month Precipitation Max. Min. Avg.

mm ----------°C---------- MJ m-2 d-1 2013

May 18 27 15 21 21 June 146 31 21 25 19 July 299 30 21 25 16 Aug. 139 31 21 25 18 Sept. 59 29 20 24 21 Oct. 53 25 13 19 14 Total 714

2014

May 261 29 17 23 21 June 134 32 21 26 20 July 221 32 22 26 20 Aug. 125 33 22 27 20 Sept. 104 30 21 25 16 Oct. 103 27 13 19 17 Total 948

2015

May 67 30 18 24 21 June 109 32 22 26 20 July 233 34 23 27 21 Aug. 111 33 22 27 20 Sept. 107 30 19 24 17 Oct. 175 26 14 20 13 Total 802

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Table 4-3. Aboveground dry biomass accumulation of cotton + 150 kg N ha-1 (COT), peanut +30 kg N ha-1 (PEA), and sweet sorghum receiving one of the following: i) 30 kg N ha-1 (S30); ii) 30 kg N ha-1 + biochar (S30B); iii) 30 kg N ha-1 + vinasse (S30V); iv) 150 kg N ha-1 (S150) during the 2013, 2014, and 2015 growing season in Jay, Florida.

Year Treatment 2013 2014 2015

Biomass (Mg ha-1)

COT a 15.1 b b 18.9 a 18.2 a PEA 3.5 c 4.0 c 6.0 b S30 21.0 a 4.9 bc 22.2 a S30B 19.4 a 3.9 c 17.9 a S30V 19.4 a 5.0 bc 17.9 a S150 18.8 a 6.2 b 17.9 a a Cotton seed, lint and aboveground biomass collected before chemical defoliation. b Means within columns followed by different lowercase letter are significantly different at

P<0.05.

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Table 4-4. Soil volumetric water content at six different depths in the soil profile of cotton + 150 kg N ha-1 (COT), peanut + 30 kg N ha-1, and sweet sorghum receiving one of the following i) 30 kg N ha-1 (S30); ii) 30 kg N ha-1 + biochar (S30B); iii) 30 kg N ha-1 + vinasse (S30V); iv) 150 kg N ha-1 (S150) grown in field conditions during summer of 2013, 2014, and 2015 in Jay, Florida a.

Soil depth (m) Year Treatment 0.1 0.2 0.3 0.4 0.6 1.0

Soil volumetric water content (cm3 cm-3) a

2013 COT 0.13 b 0.16 bc 0.21 ab 0.31 ab 0.34 ab 0.36 c PEA 0.13 b 0.21 a 0.19 bc 0.27 bc 0.30 c 0.41 a S30 0.10 c 0.18 b 0.19 bc 0.25 c 0.34 abc 0.39 ab S30B 0.17 a 0.22 a 0.26 a 0.32 a 0.34 abc 0.39 ab S30V 0.13 b 0.17 bc 0.25 a 0.31 ab 0.37 a 0.38 abc S150 0.12 b 0.15 c 0.16 c 0.27 bc 0.32 bc 0.37 bc 2014 COT 0.13 b 0.20 b 0.19 c 0.23 c 0.26 b 0.54 a PEA 0.15 b 0.20 b 0.24 b 0.29 ab 0.31 a 0.44 c S30 0.13 b 0.21 b 0.24 b 0.26 bc 0.32 a 0.54 a S30B 0.18 a 0.25 a 0.27 a 0.32 a 0.31 a 0.53 a S30V 0.15 b 0.21 b 0.25 ab 0.28 b 0.34 a 0.52 a S150 0.14 b 0.15 c 0.17 c 0.28 b 0.32 a 0.48 b 2015 COT 0.14 b 0.18 b 0.21 b 0.25 b 0.28 b 0.31 b PEA 0.13 b 0.19 b 0.17 c 0.21 c 0.22 c 0.33 b S30 0.14 b 0.20 b 0.24 a 0.26 b 0.31 ab 0.38 a S30B 0.17 a 0.23 a 0.25 a 0.31 a 0.30 ab 0.38 a S30V 0.15 b 0.19 b 0.23 ab 0.26 b 0.32 a 0.37 a S150 0.14 b 0.15 c 0.15 d 0.26 b 0.30 ab 0.33 b a Data represents annual average of soil volumetric water content.

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Table 4-5. Average NO3-N concentration in leachates collected at 1 m depth in cotton + 150 kg N ha-1 (COT), peanut + 30 kg N ha-1 (PEA), and sweet sorghum receiving one of the following i) 30 kg N ha-1 (S30); ii) 30 kg N ha-1 + biochar (S30B); iii) 30 kg N ha-1 + vinasse (S30V); iv) 150 kg N ha-1 (S150) during the summer season of 2013-2015 in Jay, Florida.

Year Treatment 2013 2014 2015

NO3-N (mg L-1)

COT 31.8 a a 11.9 b 2.2 b PEA 20.1 ab 12.3 b 14.5 a S30 10.9 b 10.2 b 1.7 b S30B 9.6 b 12.0 b 1.7 b S30V 11.0 b 6.8 b 11.2 ab S150 36.9 a 21.4 a 6.2 ab a Means within columns followed by a different lowercase letter represent a treatment difference

at P < 0.05 according to Tukey-Kramer honest significant method.

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CHAPTER 5 CONCLUSIONS

Growing elephantgrass and sweet sorghum appears to have minimum

detrimental impact on crop water use compared to the crops they replace, while

significantly increasing the amount of biomass produced. Moreover, elephantgrass and

sweet sorghum cropping systems produced lower N leaching to environment compared

to traditional cropping systems they could replace, by decreasing the amount of NO3-N

leached below the root zone. Low N application to sweet sorghum and elephantgrass

could be a feasible nutrient management practice for production of these feedstocks.

Conversely, low N fertilization combined with bioenergy residuals did not increase

biomass productivity and thereby does not represent an attractive nutrient management

practice for farmers growing these dedicated bioenergy crops.

Sweet sorghum could be a promising crop to use in the traditional cotton-peanut

rotation that could benefit farmers. Additionally, soil water retention was higher in sweet

sorghum plots treated with biochar residual. Thus, the incorporation of this residual into

the soil can favor water availability, potentially reducing irrigation needs.

Application of biochar residual demonstrated to alter the pattern of soybean root

distribution in the soil profile where it was applied. Nevertheless, biochar application

should be considered carefully because this amendment increases water infiltration,

which could increase runoff and offsite nutrient movement.

This research demonstrated agronomic benefits of recycling two important

bioenergy residuals, vinasse and biochar, as soil amendments for use in bioenergy

cropping systems. However, further research needs to be conducted to elucidate the

factors responsible for the lack of response of elephantgrass and sweet sorghum

88

biomass production to application of bioenergy residuals. Additionally, a more detailed

measurement of the evaporation and transpiration components of ETc could provide a

more accurate description of water fate in bioenergy cropping systems.

This information will aid to develop recommended rates of biochar and vinasse

residuals application to dedicated bioenergy crops to increase N recovery and

agronomic effectiveness.

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BIOGRAPHICAL SKETCH

Joel Reyes-Cabrera was born in Leon, Nicaragua, where he lived 17 years and

then moved to Costa Rica. He received his Bachelor of Science degree in agricultural

engineering from EARTH University at Costa Rica. After completing his B.S. he worked

under the supervision of Dr. Johan Perret evaluating the effects of land application of

sugarcane liquid byproducts as potassium source on plant biomass accumulation,

drainage quality and soil chemistry changes. After completing this project, he decided to

enter Graduate School at the University of Florida and joined Dr. Lincoln Zotarelli's

laboratory in the Horticultural Sciences Department, where he evaluated the effects of

using drip irrigation on yield, external, and internal quality of three potato cultivars grown

in sandy soils. After completing his master’s he decided to pursue a doctorate degree to

gain more knowledge about plant physiology, sustainable bioenergy production, and

crop water dynamics. He joined Dr. Ramon Leon and Dr. John Erickson labs located in

Jay, FL and Gainesville, FL, respectively, and has collaborated in a myriad of projects

conducted in both laboratories.