Crop Rotations for Sweet Sorghum Production in the Southeastern United States

CROP ROTATIONS FOR SWEET SORGHUM PRODUCTION IN THE SOUTHEASTERN UNITED STATES

JEFFREY ROBERT FEDENKO

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

2015

To my family, for all their support.

ACKNOWLEDGMENTS

The work necessary to complete a dissertation is not a solitary effort, and I would like to thank my lab group for their individual and collective efforts over the years.

Without my advisor, Dr. John Erickson, I would undoubtedly still be collecting samples and trying to expand my projects, and thank him for providing focus and direction.

Additionally, I would like to thank Rezzy Manning for his assistance with the field and lab work portions of my research since we first established the experiment. I would also like to thank Andrew Schreffler, Tim Havlock, Jim Boyer, and the PSREU staff for their assistance with field work. This project was partially funded by a USDA-Southern SARE

Graduate Student Research Grant, for which I am grateful and thank SARE for their support. I wish to thank Dr. Danielle Treadwell, Dr. Ann Wilkie, Dr. Diane Rowland, Dr.

Kenneth Quesenberry and Dr. Tesfamariam Mengistu for serving on my committee, for their assistance with lab analyses and design, and in introducing me to applied sustainable agriculture.

I thank my parents and family for their support, not only while in graduate school but for as long as I can remember, including the last six years of graduate education. In particular, their continual urgings to finish writing have been invaluable. Finally, I thank

Shannon Brown, for many long nights suffered and shared together in our labs, writing, and working on research.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 8

LIST OF FIGURES ...... 10

LIST OF ABBREVIATIONS ...... 11

ABSTRACT ...... 12

CHAPTER

1 INTRODUCTION ...... 14

2 LITERATURE REVIEW ...... 16

Background ...... 16 Sorghum ...... 20 Crop Rotations and Benefits ...... 23 Soil Nitrogen and Soil Properties ...... 24 Plant Nitrogen Uptake and Cycling ...... 25 Nematodes ...... 25 Rotational Crops ...... 27 Camelina ...... 27 Rye ...... 29 Sugar Beet ...... 30 Red Clover ...... 31 Spring Rotational Crops ...... 32

3 SWEET SORGHUM YIELD AND PARTITIONING AS AFFECTED BY FERTILIZATION AND COOL-SEASON ROTATIONS ...... 35

Background ...... 35 Materials and Methods...... 39 Experimental Site and Design ...... 39 Cultural Practices and Harvest Management ...... 40 Harvest Procedures ...... 42 Statistical Analysis ...... 44 Results and Discussion...... 45 Sorghum ...... 45 Rotational Crops ...... 48 Conclusions ...... 52

4 EFFECT OF NITROGEN FERTILITY AND ROTATION CROP ON SOIL NITRATE DYNAMICS OF SWEET SORGHUM CROPPING SYSTEMS ...... 63

Background ...... 63 Materials and Methods...... 66 Experimental Design and Field Management ...... 66 Monthly Soil Nitrogen Monitoring ...... 67 Soil Testing ...... 68 Statistical Analysis ...... 69 Results and Discussion...... 70 Soil Available Nitrogen Dynamics ...... 70 Sorghum Season Available Nitrogen ...... 75 Soil Nitrates and Total Nitrogen ...... 77 Conclusions ...... 78

5 NEMATODE POPULATION DYNAMICS OF SWEET SORGHUM AND COOL- SEASON CROP ROTATIONS ON A SANDY SOIL IN FLORIDA ...... 88

Background ...... 88 Materials and Methods...... 91 Sorghum Damage Thresholds ...... 91 Field nematode sampling procedure and analysis ...... 92 Statistical Analysis ...... 92 Results and Discussion...... 93 Sorghum Detection Thresholds ...... 93 Field Monitoring ...... 94 Conclusions ...... 96

6 ROOT LODGING AFFECTS BIOMASS YIELD AND CARBOHYDRATE COMPOSITION IN SWEET SORGHUM ...... 100

Background ...... 100 Materials and Methods...... 102 Experimental Site and Design ...... 102 Management Practices ...... 103 Harvest Procedures ...... 104 Carbohydrate Analyses ...... 105 Statistical Analyses ...... 106 Results ...... 106 Weather Data ...... 106 Biomass Yield and Partitioning ...... 106 Brix and Carbohydrates ...... 108 Discussion ...... 109

7 CROPPING SYSTEMS AND IMPLICATIONS ...... 119

APPENDIX

A SUNFLOWER YIELD AND COMPOSITION ...... 121

Summary ...... 121

LIST OF REFERENCES ...... 125

BIOGRAPHICAL SKETCH ...... 141

LIST OF TABLES

Table page

2-1 Representative whole plant and extractive-free fiber lignin concentrations of potential second-generation biofuel feedstocks...... 34

3-1 Soil properties at planting...... 53

3-2 Total rainfall and average daily high and low temperature for each crop production season...... 53

3-3 Seeding depth and rates for sweet sorghum and cool-season rotation crops. Rates and depths were consistent across all years...... 53

3-4 Sorghum dry biomass yields, moisture as a fraction of fresh biomass, and brix of expressed juice by year for main effects and the interaction...... 54

3-5 Sorghum estimated stem sugar yields by year for main effects...... 55

3-6 Linear regression and R2 value for changes in total dry biomass yield, tissue nitrogen concentration, and nitrogen removal by sorghum over three years in a sorghum-fallow system...... 55

3-7 Dry biomass fractions of stem, grain heads (GH), dead leaves (DLF), and green leaves (GLF) as mg of tissue per g dry biomass in sorghum by year for main effects and the interaction...... 56

3-8 Whole plant tissue N concentrations and total aboveground N in sorghum biomass by year for main effects and the interaction...... 57

3-9 Cool-season rotation crop dry biomass yield (all aboveground biomass for rye, clover, and camelina seed and separated as aboveground leaves and belowground root for beets), dry matter concentration, tissue nitrogen concentration, and total nitrogen contained in biomass fraction for 2012-13 for main effects and the interaction...... 58

3-10 Cool-season rotation dry biomass yield (all aboveground biomass for rye, clover, and camelina and separated as aboveground leaves and belowground root for beets), dry matter concentration, tissue nitrogen concentration, and total nitrogen contained in biomass fraction for 2013-14 for main effects and the interaction...... 59

4-1 Monthly soil-available nitrates measured by in situ ion-exchange resin at 20 cm subsurface during 2013 as kg N ha-1...... 80

4-2 Monthly soil-available nitrates measured by in situ ion-exchange resin at 20 cm subsurface during 2014 as kg N ha-1...... 81

4-3 Cumulative soil-available nitrogen measured at 20 cm subsurface during the sorghum growing season for 2013 and 2014 ...... 82

4-4 Main effect of year on soil-available nitrate-nitrogen (NO3-N) and TKN measured at sorghum planting in 2012 and after the third sorghum harvest in 2014...... 83

4-5 Main effects of depth, sorghum nitrogen fertility rate and rotational crop on soil-available nitrate-nitrogen (NO3-N) and TKN measured at sorghum planting in 2012 and after the third sorghum harvest in 2014...... 83

5-1 Field population counts of Meloidogyne spp., Mesocriconema spp., and Trichodorus observed following sorghum harvest and at cool-season crop planting in 2013 (October), at cover crop termination in 2014 (May), and following sorghum in 2014 (August)...... 97

6-1 Planting, lodging, and harvest dates along with stem harvest density, soil pH, soil bulk density, soil texture, soil organic matter (SOM), and available P, K, Ca, and Mg...... 113

6-2 Effect of root lodging at 81 and 86 days after planting during 2012 and 2013, respectively, on sweet sorghum fresh biomass yield, whole plant dry matter concentration, leaf, stem, panicle, and whole plant dry biomass yields, 100 seed weight, and the number of seeds per panicle at final harvest...... 114

6-3 Effect of root lodging on partitioning of dry biomass to leaf, stem and panicle for sweet sorghum at final harvest during the 2012 and 2013 growing seasons ...... 115

6-4 Effect of root lodging on water soluble carbohydrate (WSC) and starch concentrations for leaf, stem, and panicle tissues of sweet sorghum at final harvest during the 2013 growing season, and initial WSC and starch concentrations prior to lodging at anthesis ...... 116

6-5 Effect of root lodging on yields of water soluble carbohydrates (WSC), starch, and total nonstructural carbohydrates (TNC, equal to WSC and starch combined) for leaf, stem, and panicle tissues, along with whole plant TNC of sweet sorghum at final harvest during the 2013 growing season ...... 117

A-1 Sunflower stalk height, average number of heads per stalk, and average face diameter for 10 varieties of sunflower grown in North Florida ...... 124

LIST OF FIGURES

Figure page

3-1 Field layout of sorghum fertility treatments and cool-season rotational crops. ... 60

3-2 Timeline for production of sweet sorghum in rotation with a cool-season crop in the current study, and potential sweet sorghum double cropping window in Florida ...... 61

3-3 M81-E means, standard deviations, and linear regressions when significant across years under moderate and low nitrogen input fallow systems ...... 62

4-1 Cumulative daily rainfall as measured at the Plant Science Research and Education Unit in Citra, Florida over the course of the study...... 84

4-2 Average daily air temperature at 2 m (gray line) and average daily soil temperature at -10 cm (black line) as measured at the Plant Science Research and Education Unit in Citra, Florida over the course of the study...... 85

4-3 Monthly nitrate-nitrogen availability by sorghum fertility rate at 20 cm under five different cover crops ...... 86

4-4 Monthly nitrate-nitrogen availability by winter rotational crop at 20 cm under moderate (solid line) and low (dotted lines) sorghum nitrogen fertility...... 87

5-1 Temperature profile of soil used in nematode pot studies during pasteurization to kill soil-borne pathogens and nematodes...... 98

5-2 M81-E mean tissue dry weights and standard deviations across nematode egg inoculation densities at harvest 60 days after planting for detection threshold determination...... 99

6-1 Root-lodged (foreground) and non-lodged (background) sweet sorghum following heavy rainfall and high wind speeds...... 118

A-1 Sunflower stalk (leaf plus stem) and seed dry biomass yields with standard deviations for 10 varieties of sunflower grown in North Florida...... 123

A-2 Sunflower seed oil concentration (g kg-1) as determined by NMR for 10 varieties of sunflower grown in North Central Florida in summer 2013 ...... 123

LIST OF ABBREVIATIONS

DLF Dead leaf, leaf blade tissue which is greater than 50% senesced and does not include leaf sheath

GH Grain head, the portion of the plant above the flag leaf (last emerged leaf) which includes the panicle and kernels

GLF Green leaf, leaf blade tissue at harvest which is less than 50% senesced and does not include the leaf sheath

GH Grain head, the portion of the plant above the flag leaf (last emerged leaf) which includes the panicle and kernels

Mod. Moderate, to indicate moderate nitrogen fertilization rate treatment in tables

NASS National Agricultural Statistics Service

OM Soil organic matter

RKN Root knot nematode, Meloidogyne spp.

TKN Total Kjeldahl Nitrogen

US-EIA United States Energy Information Administration

US-EPA United States Environmental Protection Agency

USDA-ERS United States Department of Agriculture, Economic Research Unit

USDA-NRCS United States Department of Agriculture, National Resources Conservation Service

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

CROP ROTATIONS FOR SWEET SORGHUM PRODUCTION IN THE SOUTHEASTERN UNITED STATES

Jeffrey Robert Fedenko

December 2015

Chair: John E. Erickson Major: Agronomy

Sweet sorghum (Sorghum bicolor [L.] Moench) is a promising bioenergy crop in the United States with the ability to meet feed and fuel needs while being an efficient user of both nitrogen and water. However, sweet sorghum has not been traditionally cultivated on large acreages, and as such has no established cropping systems. The warm climate and ample rainfall of the southeastern US lends itself to year-round cropping, which may allow for cool-season rotational crop production that can address the growing demand for bioenergy, and/or benefit sorghum production. The goal of this research was to evaluate potential sweet sorghum production systems. Specific objectives were:

1. Determine biomass yields and partitioning of sweet sorghum under low and moderate nitrogen levels with different cool season rotation crops

2. Quantify monthly availability of and changes in plant available soil nitrate- nitrogen

3. Monitor nematode diversity and abundance under rotational systems

4. Quantify root lodging effects on sweet sorghum biomass yield and carbohydrate allocation

Two potential cool-season bioenergy crops, sugar beet (Beta vulgaris) and camelina (Camelina sativa (L.) Crantz), and two traditional rotation crops, red clover

(Trifolium pratense) and rye (Secale cereale), were grown in rotation with sweet sorghum for three years under moderate and low-nitrogen fertility. Biomass yield, partitioning, plant and soil nitrogen, and plant-parasitic nematode populations were monitored. Sorghum biomass yields were substantially affected by fertility in all years, but effects of rotations on sorghum yields and partitioning were variable across years, due partially to nitrogen availability from incorporated rotational crops. In-situ soil monitoring of available nitrate-nitrogen showed that incorporation of clover substantially increased soil-available nitrate-nitrogen, but availability dropped sharply after incorporation and nitrates may not have been available during the majority of the growing season. Low-input fallow systems showed declining yields per unit available nitrogen over three years, suggesting that low-input production is not sustainable.

Monitored plant-parasitic nematode populations did not generally reach action-level thresholds for field crops. Lodging reduced grain head biomass fraction, but had mixed effects on total biomass yield and soluble carbohydrates. Further research on improved management may enhance the benefits of utilizing winter rotations for sweet sorghum production.

CHAPTER 1 INTRODUCTION

Sorghum has historically been cultivated as a warm-season grain or forage crop, both in the United States and globally, but is also capable of accumulating high concentrations of soluble sugars (Broadhead et al., 1981; Freeman et al., 1973). In

2014, 6.4 million acres of grain sorghum and 300,000 acres of silage sorghum were harvested for commercial sale in the United States, but current sweet sorghum production occupies only a niche market, by some estimates of less than 10,000 acres

(NASS, 2015). However, sweet sorghum is a promising bioenergy crop with substantial investment from industry and government, but without established cropping systems

(ARPA-E, 2015). Grower acceptance of sweet sorghum as a crop and long-term production sustainability will require well-designed and integrated cropping systems, including an understanding of potential factors that may contribute to yield loss, such as nematode pressure and lodging. In the southeastern US, these cropping systems may include double cropping with a cool-season crop due to plentiful rainfall and temperate winters.

Cool-season rotational crops have been well-researched in the Southeast, and several crops may integrate with sweet sorghum production. Traditional cover crops such as red clover (Trifolium pratense) may provide a source of organic nitrogen from nitrogen fixation and reduce fertilizer costs, or may scavenge residual soil nitrogen and build soil organic matter, such as rye (Secale cereale) (Cherr et al., 2006). Alternatively, emerging bioenergy crops such as cool-season oil seed or sugar crops may provide additional revenue streams and address government mandates for advanced biofuels

(EISA, 2007; Frohlich and Rice, 2005; Koga, 2008). Development of crop production

systems for sweet sorghum in the Southeast must address rotational crop impacts on yields of sweet sorghum while also considering the potential benefits and drawbacks of each cool-season crop, including with regard to fertility management, potential pest pressures, and the potential for yield loss due to weather-related damage such as lodging. To address these issues, a rotational cropping trial was established with four candidate rotation crops, including red clover, rye, camelina (Camelina sativa (L.)

Crantz), and sugar beet (Beta vulgaris). The objectives of the trial were to:

1. Evaluate the effects of nitrogen fertility by rotation crop on sorghum yield and partitioning over three years on a sandy soil in a sub-tropical environment;

2. Quantify nitrogen availability on a monthly basis in the crop rooting zone as affected by nitrogen fertility and rotational crops;

3. Determine the seasonal net change in soil-available nitrogen in the rooting zone of a sweet sorghum crop as a result of fertility and cool-season rotation management;

4. Monitor soil nematode population dynamics over a summer sweet sorghum production season following varied cool-season field management;

5. Monitor changes in carbohydrate allocation, biomass partitioning, and yield as a result of root lodging;

6. Evaluate multiple sunflower (Helianthus annuus L.) genotypes as an alternative summer crop that has the potential to for rotation with sweet sorghum.

CHAPTER 2 LITERATURE REVIEW

Background

Concerns over the impacts of fossil fuel consumption, including volatile global energy prices and rising atmospheric greenhouse gas concentrations, have sparked significant research efforts in renewable energies, including biomass. Biomass-derived renewable energy is generally divided into two categories, biopower and liquid biofuels.

Currently, approximately 13% of global energy consumption is supplied by biomass, mainly as a fuel source in rural areas and in 2014 biomass energy accounted for 5% of total primary energy consumption in the United States at 4.8 quadrillion BTUs (US-EIA,

2015; Vakkilainen et al., 2013). In the United States and Brazil, the majority of biomass derived renewable energy is as liquid biofuels for transportation, primarily ethanol from corn starch and sugarcane respectively.

Brazilian ethanol production from sugarcane supplies 23 billion liters of ethanol for fuel, or between 40 and 50% of the country’s liquid transportation fuel demand

(Somerville et al., 2010; Pohit et al., 2011). US ethanol production from corn grain, while substantially higher in terms of total production at approximately 45 billion liters, represents a significantly smaller fraction of the total US transportation fuel market, at under 10% of demand (USDA-ERS, 2014b). However, in the US, ethanol production consumes roughly one-third of annual US corn grain production and 8 million hectares of agricultural land, raising concerns over the use of food crops for fuel and the associated input costs (Mumm et al., 2014). As a result, US corn ethanol production has been capped at 57 billion liters of ethanol, with plans to supply an additional 170 billion liters of liquid transportation biofuels with advanced and cellulosic alternatives by 2030

(EISA, 2007; Himmel et al., 2007). These planned increases, as well as caps on corn ethanol production and regulation calling for advanced and alternative renewable fuel sources, create the opportunity for additional crops to meet these increased demands.

Sorghum grain has been approved as a renewable, and potentially advanced, biofuel depending on the processing pathway (US-EPA, 2012). Grain sorghum kernels are typically 65 to 80% starch, and grain sorghum can serve as a supplement or alternative to corn grain. Average yields in the Midwestern US are 6.9 Mg ha-1 for sorghum, versus 7.9 Mg ha-1 for corn (Clark et al., 2001; Staggenborg et al. 2008).

However, grain sorghum produces higher grain yields under adverse conditions of drought and temperature in comparison to corn, making it a viable option for ethanol production (Staggenborg et al., 2008). Sweet sorghum varieties, which accumulate soluble sugars in the stalk, primarily as sucrose, are an alternative for ethanol production from sugarcane. Yield trials in the Southeastern US have resulted in soluble sugar production between 4.5 and 7.1 Mg ha-1 for a plant crop depending on cultivar and location, and total seasonal soluble sugar yields of 6.3 to 12 Mg ha-1 (Erickson et al., 2011). Sugarcane yields over the same period and in the same location were 10 Mg ha-1 yr-1 for the plant and ratoon crops (Fedenko et al., 2013). As an additional advantage of sweet sorghum cultivars, many also produce grain which can be converted to ethanol as well. While this is a minor contribution to the total sugar budget, estimates for the Southeastern US are that sweet sorghum grain heads comprise 1.6

Mg ha-1 of the total plant crop biomass, of which a substantial fraction is starch

(Fedenko et al., 2015a).

Current ethanol production systems based on simple sugars and starch are first- generation systems that rely on established processes, but make little or no use of the remaining biomass fraction of a crop. These technologies are widely considered inadequate for displacing additional fractions of fossil fuel consumption (Karp and

Shield, 2008). Somerville et al. (2010) lists average corn grain ethanol yields of 2900 L ha-1, with estimated sugarcane ethanol yields of 6900 L ha-1 from sugar, 3000 L ha-1 from bagasse and 9900 L ha-1 total. Uden et al. (2013) identified sustainable corn stover ethanol production rates of 900 to 1500 L ha-1 in a productive agricultural region, though the ability to harvest this material sustainably is unknown. While sorghum can provide an alternative source of simple sugars in juice and starch in grain, it is still subject to some of the same criticisms facing conventional conversion technologies. However, sweet sorghum processing for sucrose-based ethanol results in bagasse, the fibrous portion of the plant composed primarily of cellulose and hemicellulose. This material can be used in addition to the simple sugars and starch to produce ethanol and increase the efficiency of the crop at energy generation by using second-generation conversion strategies.

Advanced-generation technologies that can produce renewable fuels from lignocellulosic material, or that can substantially reduce greenhouse gas emissions over current standards based on increasing the fraction of crop biomass converted, have been widely investigated. Potential feedstocks and their suitability for lignocellulosic conversion have received both public and private interest, with multiple companies, including Abengoa, Poet-DSM, and British Petroleum conducting large-scale research, and launching pilot and/or commercial scale facilities. Saha and Cotta (2006) reported

ethanol yields as high as 290 L Mg-1 dry biomass at a lab scale using an alkaline pretreatment with wheat (Triticum aestivum L.) straw, although commercial realities are generally substantially lower than theoretical maximums currently. This same technology can be applied to sorghum bagasse, which has relatively low lignin content in comparison to other cellulosic material, such as pine (Table 2-1) (Fedenko et al.,

2013; Fu et al., 2011; Studer et al., 2011). Ethanol produced through this process meets the US EISA requirements as an advanced biofuel. In addition, sorghum genotypes that do not accumulate soluble sugars nor produce grain, such as high biomass sorghums, offer the potential to produce much higher total sugar yields as cellulose and hemicellulose which could then undergo cellulosic conversion to advanced renewable fuels.

While cellulosic and advanced generation technologies are under investigation, simple sugar conversion currently represents the most cost-effective method of biofuel production, but still requires significant inputs, including fertilizers, water, and chemicals

(Groom et al., 2008). Sorghum is an ideal crop to address both current sugar and future fiber needs because of its diverse germplasm that contains both genotypes that can accumulate soluble sugars and genotypes that can produce high biomass. Further investigation is necessary to determine the potential environmental impacts of large- scale commercial production, and to devise strategies to reduce inputs and increase production system profitability (Amaducci et al., 2004; Goff et al., 2010). Additionally, the EPA release of the final rule on cellulosic ethanol production specifies that a feedstock stream which is at least 75% cellulosic material qualifies for 100% cellulosic status. This classification will increase the suitability of a potential high soluble sugar

accumulating feedstock, as mixed feedstock fermentation with elevated soluble sugars, up to 25%, is most profitable (US-EPA, 2010). Therefore, this research focused on crop rotations with sorghum in an attempt to identify systems which may reduce the need for chemical nitrogen fertilization, reduce nematode pest pressures as a result of continual sorghum cropping, and increase grower profitability by evaluating potential secondary cash crop rotations with sorghum.

Sorghum

Sorghum is an annual warm-season C4 grass native to the tropical and subtropical regions of much of the world. Sorghum can be further subdivided into multiple types based on phenology and end use. Major historical types include 1) grain sorghums, which are commonly grown as food crops in regions subject to drought and/or high heat, 2) sweet sorghums, which accumulate soluble sugars in the stalk and can be used for sugar production, and 3) forage sorghums, which produce less grain and stalk and greater leaf area than grain sorghums (Bishnoi et al., 1993; Dolciotti et al.,

1998). Due to recent interest, dedicated biomass type sorghums with high biomass production and lower or no grain and soluble sugar yields are being developed and released for potential use as second-generation bioenergy crops.

Due to the variety of objectives of various sorghum cultivars, plant morphology is highly variable, with heights ranging from less than 1 meter for short-duration grain sorghums to over 7 meters for hybrid biomass sorghums. Many sorghum cultivars are capable of producing a ratoon crop after harvesting in the same year, provided the growing season is long enough, and can be cultivated throughout the continental USA.

Pure-line sorghum also produces viable seed and is primarily self-pollinating. Sweet sorghum cultivars are characterized by a high concentration of readily fermentable

carbohydrates in the stalk, primarily sucrose with small fractions of glucose and fructose, which can be extracted by pressing or squeezing (Broadhead et al., 1981).

Sweet sorghum also produces high biomass yields, with consistent reports of 80 to 100

Mg ha-1 fresh weight (Nuessly et al., 2013). Planting should occur when soil temperatures are over 18°C using seed in widely spaced rows, typically 75 cm, and fertilizer recommendations vary but may be as a low as 45 kg N ha-1 on fertile soils

(Freeman et al., 1973). Erickson et al. (2012) showed no differences in dry biomass yield with nitrogen fertilization rates ranging from 45 to 180 kg N ha-1. Sorghum is susceptible to a variety of disease and insect problems, including the sugarcane aphid, which is known to cause significant damage to most current cultivars (Sharma et al.,

2014). Unlike some proposed bioenergy crops, particularly high biomass-producing perennial grasses, sorghum is not a potential invasive species (Vermerris, 2008).

In addition to disease and insects, sorghum is susceptible to both root and stem lodging, which are known causes of yield loss in multiple crops including maize, wheat and grain sorghum (Hume and Campbell, 1972; Larson and Maranville, 1977; Weibel and Pendleton, 1964). Sorghum cultivars vary in susceptibility to root and stem lodging, both of which represent potential challenges to sorghum cultivation as a bioenergy feedstock (Rooney et al., 2007). Multiple factors have also been identified as potentially influencing lodging occurrence, including plant height and high winds (Berry et al., 2004;

Murray et al., 2009). Given the frequent occurrence of severe storms and hurricanes in the Southeast, lodging of a sweet sorghum represents a major concern for wide-scale production. Root lodging has been shown to reduce yields in both corn and maize, and earlier lodging had a greater impact on yield reductions (Hume and Campbell, 1972;

Weibel and Pendleton, 1964). However, lodging research on sweet sorghum is extremely limited, and no known studies exist on the effects of naturally-induced root lodging of sweet sorghum in a field setting.

M81-E is a common, high biomass-producing, publicly available cultivar of sweet sorghum. Developed at the U.S. Sugar Crops Field Station in Meridian, Mississippi and released in 1981 as a potential energy crop, it is the result of crossing ‘Brawley’ x

(‘Brawley’ x ‘Rio’) (Broadhead et al., 1981). It is widely used in sweet sorghum research, and routinely included as a well-characterized ‘check’ cultivar. It is known to respond to nitrogen fertilization, with increasing nitrogen fertility increasing both sugar yield and water content of the stalk (Holou and Stevens, 2012). It is well suited for production in humid and storm prone environments, including North Florida, because of excellent resistance to multiple common diseases and a low incidence of lodging.

Propheter et al. (2010) reported average dry yields of 28.2 and 32.6 Mg ha-1 yr-1 for the sweet sorghum cultivar ‘M-81E’ grown in Kansas in 2007 and 2008, with estimated ethanol yields of 9660 L ha-1 and 10 200 L ha-1, which were greater than corn, forage sorghum or giant miscanthus estimated ethanol yields. Mislevy et al. (1989) reported total average dry yields of 28.9 Mg ha-1 yr-1 for M-81E grown over a 2-yr period in Central Florida, with an average of 20.4 Mg ha-1 yr-1 produced by the plant crop and

8.5 Mg ha-1 yr-1 by the ratoon crop. Erickson et al. (2011) reported yields ranging from

73 to 86 Mg ha-1 fresh (green) biomass for M-81E grown under similar conditions, and dry yields ranging from 15 to 16.3 Mg ha-1 dry biomass for the plant crop in another study with M-81E (Erickson et al., 2012).

Ratoon crop yields of sorghum grown in the Southeastern US are substantially lower than plant crop yields, ranging from 6 to 40 Mg ha-1 of fresh weight for the ratoon crop versus 51 to 86 Mg ha-1 for the plant crop across all sites, planting dates, and cultivars (Erickson et al., 2011; J.E. Erickson, unpublished data). If only the optimum planting dates (late March to early May) are considered, the ratoon crop only produces

22-56% as much fresh biomass, and has a lower brix range of 102-165 g kg-1 sugars for the ratoon versus 135-165 g kg-1 for the plant crop (Erickson et al., 2011). Production and harvesting of the ratoon crop for bioenergy may not be profitable, but modifications to production practices of the plant crop may decrease the break-even price of sorghum ethanol, and thereby increase the feasibility of sorghum production (Helsel and Álvarez,

2011). Therefore, sustainable sorghum production will depend on cool-season rotation crops to provide an additional source of revenue, or decrease the costs of sorghum production.

Crop Rotations and Benefits

Crop rotations are one of the oldest and most-studied practices in agricultural production, and evidence of the benefits of rotating crops has been published for centuries (Daubeny, 1845). Rotations are often classified based on the intended effects of the prior or following crops. A crop which is wholly incorporated into the soil and intended to improve soil quality is typically referred to as a ‘green manure,’ while a cover crop is more generally defined as any crop intended to increase soil quality, but may not be incorporated into the soil (Cherr et al., 2006; Sullivan, 2003). Additional beneficial effects of crop rotations on subsequent plant growth include decreased pest and nematode pressures, and additional nitrogen availability, either as a result of nitrogen scavenging or fixation by the rotated crop (Clark, 2008). In particular, clover and vetch

are common leguminous, nitrogen-fixing cool-season crops for the Southeastern US and have the potential to fix an average of up to 135 kg ha-1 of nitrogen from October to

April (Newman et al., 2010; Quesenberry and Blount, 2006).

Soil Nitrogen and Soil Properties

Beneficial effects of cover crops on soils have been extensively studied, and the effects on basic soil properties are well known. Generally, rotation crops can increase soil nitrogen and carbon, increase soil organic matter (OM), reduce compaction, decrease soil bulk density, improve water infiltration into the soil, and decrease pest pressures (Clark, 2008). Perennial rotation crops, such as bahiagrass, can increase soil organic matter and reduce bulk density to a greater depth and extent than annual crops, but require significantly more time and labor (Katsvairo et al., 2006). Annual rotation crops can increase organic matter and reduce bulk density while still allowing a cash crop to be produced during the main growing season, and are typically more common.

Crops with extensive root systems, such as rye, vetch, and clover, are capable of penetrating compacted soil layers, aerating the soil, and increasing water penetration, which allows for increased root development and more vigorous growth in subsequent crops (McVay et al., 1989). Additionally, as the root systems of these crops decompose, some of the carbon is assimilated into the soil matrix, increasing soil C and organic matter, which increases nutrient retention and plant growth in subsequent seasons

(Sainju et al., 2001). Leguminous crops, such as clover and vetch, also incorporate nitrogen as a result of N2 fixation, but due to a lower C:N ratio than other crops tend to be decomposed more rapidly and have less of an impact on OM accumulation.

However, the rapid breakdown can increase short-term available soil nitrogen, though long-term increases are more difficult to achieve (Clark, 2008).

Plant Nitrogen Uptake and Cycling

Crop rotations can increase available soil N and serve as an N source for subsequent crops, either by scavenging residual soil N or by fixing atmospheric N, and then releasing nitrogen-containing compounds during decomposition (Clark, 2008). Due to lower N concentrations and higher C:N ratios, non-legume rotation crops such as rye decompose and release N more slowly to subsequent crops in comparisons to legumes.

Legumes, due to high N concentrations as a result of N2 fixation, tend to decompose more rapidly and release N earlier as a larger initial pulse to subsequent crops, thereby reducing the need for chemical N fertilization, in some cases by as much as 120 kg ha-1

(Ingels et al., 1994; Sainju et al., 2001; Smith et al., 1987a). In particular, clovers are excellent nitrogen-fixing crops with the potential to fix up to 135 kg ha-1 during the cool- season growing season in the Southeastern US (Newman et al., 2010; Quesenberry and Blount, 2006). However, the exact timing of N release during breakdown, and its availability in the rooting zone of a subsequent crop, are important considerations when considering appropriate reductions in N fertilization to maintain yields, and are variable between species and environments.

Nematodes

Nematodes are a significant issue in Florida crop production. However, while widely recognized as an issue in horticultural and ornamental production due to visible deformation of fruit and vegetables. Nematode damage in agronomic crops is often overlooked. Nematode damage can reduce yield by 15% in corn production, similar to other major commonly treated factors, including weeds (10-25% yield loss) and insect damage (highly variable) (Johnson and Sprenkel, 1991; Noling, 2005). This tendency to overlook nematode damage is frequently attributable to nematode damage not being

readily apparent in some agronomic crops, as damage is inflicted on the root system and therefore not visible, confusion of the initial cause, and misattribution to more visible problems or secondary effects, such as insect damage or nutrient deficiency (Barker and Olthof, 1976).

Root-knot nematodes (Meloidogyne spp.) are widespread, plant-damaging parasites in Florida, of which M. arenaria, M. incognita, and M. javanica cause the vast majority of problems (Rich et al., 2010). Grain sorghum supports high populations of root-knot nematodes, including M. incognita and Criconemella spp. (McSorley and

Gallaher, 1992), which can have significant negative impacts on yield and performance, especially over a period of years. On sandy soils in Burkina Faso, Bado et al. (2011) showed significant reductions in sorghum grain yields under monocropped sorghum compared with legume rotations, with grain yield losses ranging from 33 to 75% in monocropped systems compared to other systems. These results were consistent across both high and low fertility systems, with residue removal, and nematode presence in the soil increased under continuous sorghum cropping. As such, nematode control is an important consideration in sorghum cropping. While management can be accomplished in a variety of ways, many methods employed for the control of other factors that reduce crop yields, such as fallowing a field or chemical applications, are substantially more difficult to implement for nematode control.

Chemical methods are significantly more difficult to implement due to difficulties with movement through the soil, and loss of active chemical to leaching, immobilization, and breakdown (Noling, 2011). Fallowing is effective for control of some nematode species, but is ineffective unless maintained for long periods (greater than 18 months)

with significant weed control for other species, such as the lesion nematode, due to the ability of some species to persist in a juvenile or egg stage, and non-specificity for hosts. In particular, the lesion nematode is extremely persistent, and is a documented pest of sorghum that is not easily controlled by fallowing (Dover et al, 2012). Discing can reduce nematode populations by drying the surface layer of soil where nematodes are most concentrated, causing desiccation and death of the nematodes (Govaerts et al., 2007). However, crop rotations are often the best option for control of nematodes, and also have the added benefits of controlling various other pests and weeds, and increasing soil fertility (Ingels et al., 1994; McSorley and Gallaher, 1992; Snapp et al.,

2005).

Rotational Crops

Camelina

Camelina is an allohexaploid (n=20) of the Brassicaceae family native to Europe

(Hutcheon et al., 2010). Prior research has shown that camelina seeds are typically 30-

40% oil by weight, and that the oil composition is favorable for use as a liquid transport fuel (Budin et al., 1995; Frohlich and Rice, 2005). Like most members of the

Brassicaceae family, C. sativa is known to produce allelopathic chemicals that can have inhibitory or suppressive effects on pest populations. For example, Grummer and Beyer

(1960) demonstrated that camelina produces compounds toxic to nematodes that provide allelopathic effects and increase yield in subsequent flax crops. Vollmann et al.

(1996) evaluated 32 C. sativa genotypes for seed yield, oil content and total oil yield in

Austria, and obtained average values of 1939 kg ha-1, 410 g kg-1 dry seed, and

792 kg ha-1 respectively. Total oil yields ranged from 604 to 1011 kg ha-1 (Vollmann et al., 1996). Zubr (1997) found yields consistent with these results, averaging 430 g oil

per kg dry seed, and total seed yields of 2600 to 3300 kg ha-1 for summer and winter varieties respectively in Denmark.

Fertilization recommendations are highly variable; while the crop is typically considered low input, Zubr (1997) suggests 100 kg ha-1 of nitrogen, while research from

Oregon indicates 56 kg N ha-1 (Ehrensing and Guy, 2008). Recent research from

Florida shows mixed results, with maximum yields produced with 80 kg N ha-1 in one year and with no supplemental N fertilization on camelina grown on a sandy soil in

North Florida the second year (R. Schnell, 2012, West Florida Research and Education

Center, University of Florida, Jay, FL [personal communication]). However, camelina is generally considered cultivable on marginal lands and is an efficient nutrient extractor.

Typical production methods include broadcast planting or sowing with a grain drill

10-13 cm row spacing at 5-7 kg ha-1. Drilling reduces weed competition in comparison to broadcasting, though may potentially result in slightly lower yields. Fertilizer should be applied after crop emergence in early spring when at least 4-6 leaves are present per plant. Harvesting is by small grain combine modified to capture extremely small seeds. Camelina does not suffer significantly from insect damage, and is genetically resistant to most diseases (Ehrensing and Guy, 2008; Vollmann et al., 1996; Zubr,

1997). Camelina sativa was selected as a rotation with sweet sorghum for potential allelopathic effects on plant pests and diseases, and because of prior research on planting and harvest dates. Based on experience in North Florida, camelina performed well when planted in October, while most sorghum harvesting is completed prior to this time, and camelina matures prior to optimal sorghum planting in May (R. Schnell, 2012,

West Florida Research and Education Center, University of Florida, Jay, FL [personal communication]).

Rye

Rye is widely cultivated as a grain and forage crop, and it is a common cover crop in much of the world. Unlike many other cover crops, rye is often more intensely managed to produce greater biomass yields. It can produce over 6500 kg ha-1 of dry biomass and contain up to 55 kg N ha-1 which can be returned to the soil or subsequent crops after incorporation (Newman et al., 2010). High yields from rye as a cover crop can serve a variety of functions, including as forage for grazing, or can provide weed and nematode suppression while increasing soil OM and water-holding capacity (Snapp et al., 2005)

There is significant evidence documenting rye’s allelopathic attributes on both nematode and weed suppression. Zasada et al. (2007) identified the degree to which various rye cultivars serve as hosts to M. incognita, and the quantity of benzoxazinoids

(a chemical constituent of rye that suppresses nematodes) produced by each cultivar.

Cultivars varied in host status to nematodes, but benzoxazinoid concentrations were fairly consistent, indicating that suppressive effects are likely to be determined by cultivar host status and total biomass production, as greater biomass production will result in higher soil levels of suppressive chemicals.

Rye was selected as a rotational crop with sorghum as it has been shown to significantly decrease some nematode populations, including M. incognita populations because of allelopathic effects (McSorley and Gallaher, 1992). Gill and McSorley (2011) have shown that rye, crimson clover and hairy vetch all maintain low M. incognita populations over winter. The specific cultivar ‘FL-401’ was also selected based on

widespread grower use in the region and early maturity, allowing it to fit well in the fallow period between warm-season sorghum crops.

Sugar Beet

Sugar beet is an annual or biennial C3 dicot notable for its accumulation of soluble sucrose in the taproot (USDA-NRCS, 2015). Sugarbeets of today are the result of several centuries of breeding efforts with the domesticated beet and are native to

Europe (Harveson, 2012). Koga (2008) reported yields of 16 Mg ha-1 dry weight of roots and 6 Mg ha-1 dry weight of shoots, with an average sugar content of 172 g sugar per kg fresh weight of beet. Typical fertilization for beet ranges from 100 to 200 kg N ha-1, with 120 kg N ha-1 most common, and no yield increases observed over 160 kg N ha-1 total available N (soil plus fertilizer) (Wiesler et al., 2002). Based on obtained yields and estimated input costs, sugar beet has an energy output:input ratio of 10.5 considering only production, a significantly positive energy balance (Koga, 2008).

Sugar beet is susceptible to many nematodes, including lesion, root knot, and sting. However, root-knot nematodes are specific to some hosts, and their presence does not necessarily indicate yield declines. Yield losses to nematodes are especially dramatic as damage to the root compromises sucrose storage, leading to rapid sugar deterioration. Sugar beet was selected as a potential rotation for sweet sorghum because of potential downstream processing similarities due to high concentrations of soluble sugars in both crops. Additionally, sugar beets are poorly suited to the summer climate of North Florida due to pest and disease pressures, but offer the potential for substantial growth in early spring when temperatures remain cooler, and they can be planted in the fall and over winter.

Red Clover

Red clover is native to Southeastern Europe and was introduced to the US in the

1600’s. It is a common, widely studied nitrogen-fixing legume with well-documented applications as an animal forage, hay and cover crop, and as a green manure which can help improve soil quality and growth of subsequent crops (Caddel and Redmon,

1995). Total biomass yield of clover is dependent on planting and harvest dates, but a fall-seeded clover harvested in mid-April typically produces 1900-3000 kg ha-1 of dry matter (Quesenberry and Blount, 2006).

McVay et al. (1989) demonstrated the ability of cool-season clover grown in rotation in Florida to reduce the need for fertilization in subsequent summer crops of corn and sorghum while increasing yields. Clovers can fix significant quantities of nitrogen from the atmosphere during cool-season growth, in some cases exceeding fertilization recommendations for the next crop, which can significantly reduce production costs (Newman et al., 2010). Newman et al. (2010) places the high end of clover nitrogen fixation from a cool-season crop at 135 kg ha-1, comparable to the amount of artificial fertilizer N which may be applied in sorghum production.

Cool-season clover can lead to greater susceptibility to nematode damage in following grass crops, as demonstrated in both sorghum and maize (Gill and McSorley,

2011; McSorley and Gallaher, 1992). Quesenberry and Blount (2006) bred increased root-knot nematode resistance into Southern Belle, but the resistance is not complete.

Additionally, Pratylenchus penetrans (lesion nematode) is reported to use clover as a host and can exist at fairly high concentrations in the root system (440 nematodes g-1 root) (Miller, 1978). However, clover is not as severely damaged as some other crops, such as rye, which suffer more root necrosis (rated from 1 [no necrosis] to 5 [severe

necrosis]) at lower lesion nematode loading (4.0 for rye vs 2.8 for clover and 2.7 for corn) (Miller, 1978).

Southern Belle red clover is ideally suited for rotations with summer annual grass crops for multiple reasons. Southern Belle can be planted from mid-October onwards, after the harvest window of most summer grain crops in North Florida. Southern Belle is typically first cut between mid-April and mid-May, and so it fits with the appropriate pre- plant tillage timing in the region. Additionally, Southern Belle is a high-biomass producing variety with reasonable tissue nitrogen concentrations, resulting in potentially large returns of organic nitrogen following tillage and incorporation. Finally, diseases of sweet sorghum are unlikely to overwinter on clover, and disease and insect problems affecting clover are unlikely to affect sweet sorghum.

Spring Rotational Crops

Cool-season rotational crops offer substantial promise as they can be produced during an otherwise fallow period in row-crop cultivation in North Florida and much of the Southeast. However, early maturing spring-planted crops offer the ability to double- crop with sweet sorghum in a single season, and they avoid potentially damaging winter weather. Sunflower may be a viable option for double-cropping with sweet sorghum, as it is can be planted as early as February 15th in North Florida and varieties are available with a range of maturities (Wright et al., 2013). Robertson and Green (1981) demonstrated that sunflowers planted in February in Florida produced higher yields than later plantings and matured by June 2nd, early enough to allow for a succeeding sorghum crop. Across 11 hybrids, seed yields at 10% moisture averaged 2030 kg ha-1, with 423 g oil kg-1 (Robertson and Green, 1981). These results are consistent with more recent research by Chellemi et al. (2009), who reported a dry seed yield of 1921 kg ha-1

and oil content (dry weight basis) of 451 g oil kg-1 with a March 3rd planting of sunflower as a rotational crop to produce both biodiesel from seed oil and high-quality seed meal.

However, Chellemi et al. (2009) also documented that sunflower served as a host for root-knot nematodes (Meloidogyne spp.), and that this susceptibility may render sunflower unsuitable as a rotation with nematode susceptible crops due to a lack of observed resistance in sunflower. Methods and results for a sunflower genotype evaluation performed in North Florida from May to August 2013 are presented in

Appendix A.

Table 2-1.Representative whole plant and extractive-free fiber lignin concentrations of potential second-generation biofuel feedstocks. Lignin Concentration (g kg-1) Feedstock Whole Plant Fiber Source sweet sorghum 92-195 - Stefaniak et al., 2012 sweet sorghum - 228-240 Fedenko et al., 2015a corn stover 123-151 - Emerson et al., 2014 corn stover - 223 Min et al., 2014 energycane - 183-195 Na et al., 2016 sugarcane - 261-268 Fedenko et al., 2013 Pennisetum purpureum Schum 183-207 Na et al., 2016 Arundo donax (L.) - 274-281 Fedenko et al., 2013 Populus trichocarpa 157-279 - Studer et al., 2011 Miscanthus giganteus 147-208 - Emerson et al., 2014

CHAPTER 3 SWEET SORGHUM YIELD AND PARTITIONING AS AFFECTED BY FERTILIZATION AND COOL-SEASON ROTATIONS

Background

Sorghum (Sorghum bicolor (L.) Moench) is an annual C4 row crop that has gained significant attention because of its wide applicability to multiple applications, including: sugar production, animal feed, first-generation ethanol and advanced biofuel production (Dahlberg et al., 2011; Rooney et al., 2007). However, the production of current first-generation biofuel crops competes directly with crop production for food and feed for nutrient inputs, land and water (Rathmann et al., 2010; Gerbens-Leenes et al.,

2009). In 2013, the 38 million planted hectares of corn in the US represented 29% of the total planted principal crop lands, with a yield of 13.9 billion bushels of grain, of which

38% was directed to ethanol production (USDA-ERS, 2014b; NASS, 2015). This represents close to the maximum allowance of first-generation ethanol from corn starch, which is limited to 57 billion liters in the US (EISA, 2007). Additionally, corn receives significantly more nitrogen fertilizer than many advanced biofuel crops. In 2010, the average fertilized corn crop in the US was amended with 157 kg N ha-1, which relies heavily on fossil fuels for fertilizer production and can release NOx, a greenhouse gas

(GHG), versus an average of 75 kg N ha-1 for sorghum (US-EPA, 2012; USDA-ERS,

2014a). Thus, there is a need for feedstock production for alternative and advanced biofuels that represents a significant decrease in life cycle GHG emissions and requires reduced external nitrogen inputs.

Sorghum has demonstrated potential as an advanced biofuel feedstock to supplement or provide an alternative to corn grain, and grain sorghum has been approved as an advanced feedstock under certain conditions. Average grain yields in

the Midwestern US are 6.9 Mg ha-1 for sorghum, versus 7.9 Mg ha-1 for corn

(Staggenborg et al., 2008). However, grain sorghum produces higher grain yields under adverse conditions of drought and temperature in comparison to corn, making it a viable option for ethanol production (Staggenborg et al., 2008). Similarly to sugarcane, sweet sorghum accumulates soluble sugars in the stalk, primarily as sucrose, that can be readily converted into multiple products biologically, including biofuels such as ethanol and bio-methane and bio-hydrogen, and bio-based precursor chemicals such as acetic and lactic acid (Angenent et al., 2004; Balat, 2011). Sweet sorghum yield trials in

Florida have found plant crop yields to range from 51 to 86 Mg ha-1 of fresh biomass across sites, cultivars and planting dates, with stalk Brix values of 128 to 165 g kg-1

(Erickson et al., 2011). Sweet sorghum also produces a grain head that is primarily starch, and ranged from 3 to 18% of total dry biomass in the plant crop and 6 to 25% in the ratoon crop in the same study across sites, cultivars, and planting dates in Florida

(Erickson et al., 2011; Fedenko et al., 2015b). While this is a minor contribution to the total sugar budget, estimates for the Southeastern US are that grain heads comprise

1.6 Mg ha-1 of the total plant crop biomass, of which a substantial fraction is starch

(Fedenko et al., 2015a). Thus, sweet sorghum is likely to provide greater total soluble sugar and starch yields than either corn or grain sorghum with lower nitrogen application rates than typical for corn. Additionally, sweet sorghum processing for sucrose-based ethanol results in bagasse, the fibrous portion of the plant composed primarily of cellulose and hemicellulose. This material can be used in addition to the simple sugars and starch to produce ethanol via cellulosic conversion strategies and increase the energy generation efficiency of the crop, or can be fired to produce power

as a co-product (Trebbi, 1993). Sweet sorghum is a likely candidate for advanced biofuel status, and may be a candidate as a cellulosic biofuel feedstock when bagasse is considered. Management strategies that reduce nutrient inputs, and/or increase the efficiency of conversion, will also help reduce the GHG emissions of sweet sorghum- based biofuels.

Nitrogen fertility effects on sweet sorghum have been widely researched, but have shown mixed results. Multiple researchers (Soileau and Bradford, 1985;

Wortmann et al., 2010; Erickson et al., 2012) have shown that total dry biomass yields are either inconsistently correlated with, or not significantly affected by, applied nitrogen.

In these studies, sorghum was grown on plots for a single year and followed a well- fertilized crop or managed cover crop. However, in studies where sorghum is grown continuously on the same plots, nitrogen fertilizer application significantly increases yield over no application in the second year (Tamang et al., 2011). This is consistent with research in maize grown for bioenergy, where maize grown without nitrogen fertilizer application on the same plot for 3 years shows a 20% reduction in average yearly yield when compared with maize grown with 120 kg applied N ha-1 yr-1 (Boehmel et al., 2007). Similarly mixed responses have been observed for juice Brix (a measure of soluble solids per volume that is well correlated with fermentable sugars) and soluble sugar yields. Juice brix generally declines slightly at higher nitrogen application levels, though these declines are usually not significant, and total sugar yields are generally estimated as not significantly different under varying N fertility after a single crop

(Thivierge et al., 2015). Partitioning to leaf and grain are generally more affected by nitrogen application and partitioning to these tissues as a fraction of total dry biomass

tends to increase under increasing nitrogen fertility. Van Oosterom et al. (2010b) demonstrated that both grain number per square meter and total grain nitrogen increase with increasing N fertility application in sorghum from 0 to 353 kg N ha-1. Sawargaonkar et al. (2013) showed significant increases in leaf number and grain yield of sweet sorghum with nitrogen applications up to 90 kg N ha-1, with no significant increases at higher fertility. Erickson et al. (2012) showed a linear increase in leaf dry biomass with nitrogen applications from 45 to 180 kg N ha-1, but showed only a marginal relationship between grain yield and N application at one site. Based on these studies, predicted nitrogen fertilization requirements for a sweet sorghum crop range from 86-110 kg N ha-

1 to maintain yields (Erickson et al., 2012; Sawargaonkar et al., 2013; Thivierge et al.,

2015).

Fertilizer production and application represents a significant energy sink and source of GHG emissions in agricultural production, accounting for 31-55% of total energy consumed, and 55-86% of GHG emissions associated with corn production (Kim et al., 2014). Any reduction in applied chemical fertilizers is likely to increase the net energy balance of a biofuel production system, and decrease GHG emissions, as N fertilizer losses as N2O can range up to 7% of total N applied (Bouwman, 1996). In particular, rotation crops that fix nitrogen, or act as catch crops for applied nitrogen, may significantly benefit biofuel production systems by adding organic N, or conserving previously applied N, for subsequent crops, and may increase soil carbon sequestration

(McVay et al., 1989; Sainju et al., 2001; Clark, 2008; Newman et al., 2010). Hargrove

(1986) rotated grain sorghum with cool-season legumes, rye, or fallow systems, and found that after one year legumes could replace 28 kg ha-1 yr-1 of applied fertilizer N

relative to an unfertilized field, but after 2 or 3 years of continuous sorghum cropping, cool-season legumes could replace an average of 103 kg N ha-1 yr-1. This is consistent with research from Florida that red clover may produce 1900-3000 kg ha-1 of dry matter by mid-April following a winter planting and that crimson clover may provide up to 135 kg N ha-1 yr-1 from fixed nitrogen in aboveground biomass (Quesenberry and Blount,

2006; Newman et al., 2010).

Based on the commonly observed lack of response to nitrogen fertilization in a single year of sweet sorghum production and the importance of applied nitrogen in GHG emissions from crop production there is a need for longer-term studies on the effects of yearly cropping of sweet sorghum. Additionally, there is significant potential for cool- season rotation crops to offset a fraction of chemical N application, either through nitrogen fixation or recovery from the soil. Therefore, the objectives of this research were to evaluate the effects of nitrogen fertility by rotation crop on sorghum yield and partitioning over three years on a sandy soil in a sub-tropical environment.

Materials and Methods

Experimental Site and Design

A replicated field experiment was conducted in North Florida at the University of

Florida Plant Science Research and Education Unit (29°24’N 82°10’W) in Citra, Florida.

The soil at the site is a relatively well-drained Arredondo fine sand (loamy, siliceous, semiactive, hyperthermic Grossarenic Paleudults). The previous crop was bahiagrass pasture followed by cool-season fallow. A soil analysis was done in May of each year to assess soil fertility. Selected properties are presented in Table 3-1. Soil organic matter was determined by combustion of a known weight of oven-dry soil at 450°C for 6 hours, soil pH was determined in solution using a 1:2 dilution with water and pH probe, soil

texture was determined by the Bouyoucos hydrometer method, and nitrate-nitrogen was determined by EPA method 353.2 following water extraction. Weather data was collected by the Florida Automated Weather Network at Citra, FL, and total seasonal rainfall and average seasonal daily high and low temperatures for each crop are presented in Table 3-2. The trial was designed with sweet sorghum (‘M81-E’) nitrogen rate as the main plot in a completely randomized design with cool-season rotation crop as the split plot in a completely randomized design within each main plot (Figure 3-1).

Treatments consisted of nitrogen fertilization rate as main plot effects (low, 20 kg N ha-1 sorghum crop-1; or moderate, 100 kg N ha-1 sorghum crop-1) and cool-season rotation crops as sub-plots (fallow, rye [Secale cereale ‘FL-401’], camelina [Camelina sativa (L.)

Crantz ‘311’], sugar beet [Beta vulgaris ‘EN-413’ (Betaseed, Inc.)], and red clover

[Trifolium pratense ‘Southern Belle’]). Main plots were 30 m long and 6 m wide and sub- plots were 6 m by 6 m. There were 4 replicates of each main plot, for a total of eight 30 m by 6 m plots.

Cultural Practices and Harvest Management

Plot areas were rototilled to incorporate rotational crops, cultivated and packed 2 weeks prior to sweet sorghum planting. Planting was accomplished using a 4-row John

Deere MaxEmerge planter with John Deere 7620 tractor with 0.76 m between-row spacings, in-row spacing of 6-8 cm and an approximate planting depth of 2.5 cm. Plots were planted on April 25, 2012; May 2, 2013; and May 6, 2014, with a replanting on

June 2, 2014 (Figure 3-2). In 2014, replanting followed a chemical burndown with glyphosate [N-(phosphonomethyl)glycine] and surface cultivation. Replanting was conducted according to the same procedure as the initial planting. Liquid fertilizer (11-

37-0) was applied at planting at a rate of 20 kg N ha-1, in conjunction with terbufos [O,O-

Diethyl S-{[(2-methyl-2-propanyl)sulfanyl]methyl} phosphorodithioate], a systemic insecticide-nematicide at 9.4 kg ha-1. Atrazine [1-Chloro-3-ethylamino-5- isopropylamino-2,4,6-triazine] and Dual II Magnum [Acetamide, 2-chloro-N-(2-ethyl-6- methylphenyl)-N-(2-methoxy-1-methylethyl]-,(S)] were applied immediately following planting at 1.1 kg active ingredient (a.i.) ha-1 each to control weeds. For moderate nitrogen rate treatments, the remaining 80 kg N ha-1 was side-dressed as ammonium nitrate (34-0-0) in two split applications of 50 and 30 kg, approximately three and six

-1 weeks after planting. All plots were also side-dressed with a total of 135 kg K2O ha and micronutrient mixture according to soil fertility analysis split between three and six weeks after planting. Subsequently, weeds were removed mechanically by rotary hoe or hand. Overhead irrigation was provided at planting, and limited overhead irrigation was applied as necessary at signs of visual drought stress (e.g., leaf rolling).

After final sampling and sorghum harvest each year, remaining sorghum was cut at a stubble height of 10 cm using a two-row forage harvester and removed from the field on September 19, 2012; September 25, 2013; and October 13, 2014. All plots were rototilled to incorporate sorghum stubble and cultivated. Cool-season rotation crops were planted according to best management practices at the rates and depths shown in

Table 3-3 in mid-October of each year, 1 to 4 weeks after sorghum harvest (Betaseed,

Inc., Bloomington, MN, USA; Newman et al., 2010; Wright et al., 2013). Camelina, rye, and clover were established using a grain drill with between-row spacing of 0.19 m, and sugar beets were planted by push planter with 0.76 m between-row spacing and 6-8 cm in-row spacing. Rye and camelina were fertilized with granular fertilizer (15-0-15) to a total of 50 kg N ha-1 as two equally split broadcast applications approximately four and

eight weeks after planting in each year. On the same dates, clover received broadcast

-1 applications of muriate of potash (0-0-60) for a total of 50 kg K2O ha . Sugar beets were fertilized in three split side-dressed applications to a yearly total of 100 kg N ha-1 and 50

-1 kg K2O ha . Fertility levels were determined based on moderate input cover crop production management strategies. Overhead irrigation was provided to all plots at establishment, and as necessary at signs of visual drought stress. Weeds were controlled mechanically in all plots, including fallow. Ridomil Gold SL fungicide [(R,S)-2-

[(2,6-dimethylphenyl)-methoxyacetylamino] propionic acid methyl ester] was applied at

0.50 kg a.i. ha-1 24 weeks after planting in the first year to control fungal pressure.

Harvest Procedures

Sorghum plots were harvested when approximately half of the grain heads had reached the soft dough stage, which is optimal for sugar recovery (Lingle, 1987; Tarpley et al., 1994). Sorghum plots were harvested 117-124 days after planting in 2012, 124-

146 days in 2013, and 130-142 days in 2014. A 3-m section from each of the two inner rows was harvested by hand at a 10-cm stubble height and immediately weighed fresh in the field. A four-stalk subsample was collected and partitioned into green and dead leaf blades (sheath remained with stem), grain head (all biomass above the flag leaf) and stem, and dried at 60°C until a constant dry weight was achieved to determine dry matter concentration and dry biomass yield. A second four-stalk subsample was collected, chopped, weighed, and dried at 60°C until a constant dry weight was achieved for whole-plant tissue N analysis. Dried tissue samples were run through a commercial chipper-shredder (DEK, MODEL CH1) and then ground with a Thomas-

Wiley mill (Thomas Scientific, Swedesboro, NJ) to pass through a 2-mm screen.

Another subsample of 10 stalks was collected in the field for juice extraction and brix values. Grain heads and leaves were removed and stems were weighed fresh.

Stems were then run through a grooved two-roller mill under pressure, and the expressed juice was weighed. Extracted juice was then mixed thoroughly and two subsamples per plot were collected for brix (g kg-1) measurements using a portable refractometer after samples had equilibrated to room temperature, 23°C (ATAGO PAL-

1, ATAGO USA, Inc., Bellevue, WA). Estimated sugar yields were calculated for each plot by multiplying total stem moisture by [Brix divided by 100] by 0.9 (to estimate the fraction of Brix that is fermentable sugars [Han et al., 2013]).

Sugar beets were harvested as one 3-m section from each of two inner rows of each plot. Tops and roots were separated in the field and weighed fresh after cleaning beets of residual soil by compressed air. An approximately 1-kg subsample of each top and root was taken from each harvested row and chopped, weighed fresh, and dried at

60°C until a constant dry weight was achieved to determine dry matter concentration, dry biomass yield, and for tissue N analysis.

Rye, clover and camelina were harvested from an interior 4 x 3 m area of each plot using a sickle bar mower with a cutting height of 2.5 cm and by hand raking and collecting all cut biomass. Whole plot samples were weighed fresh in the field. A subsample from each plot was collected, weighed fresh, and dried at 60°C to a constant dry weight to determine dry matter concentration, dry biomass yield, and for tissue N analysis. For the 2012 harvest, camelina samples were threshed after drying and seed was collected for seed yield and oil determination by Time-Domain Nuclear Magnetic

Resonance (TD-NMR), and seed nitrogen and oil data are presented. TD-NMR

quantification was performed with an MQC-23 NMR analyzer (Oxford Instruments, UK), equipped with an 18 mm diameter probe operating at a resonance frequency of 23.4

MHz and maintained at 40°C in accordance with ISO 10565 (1998). Data was acquired with MultiQuant calibration and analysis software (Oxford Instruments, UK). Extracted, purified camelina oil was used for a 5-point NMR calibration with r2=0.999. For the 2013 harvest in 2014, seed collection and oil analysis was not possible due to crop failure and seed loss as a result of disease and abiotic factors, and only total aboveground plot biomass yield and nitrogen are reported.

Statistical Analysis

Statistical analyses were performed using analysis of variance procedures in the

GLIMMIX procedure of SAS (SAS, 2009). The Gaussian (normal) conditional distribution and the identity link function were used. Residuals from each model fit were checked for normality graphically and numerically with the Shapiro-Wilk W test. Data were analyzed separately by year and season (summer [sorghum], or cool-season

[rotation crops]), with nitrogen rate (low and moderate) and cool-season rotation (fallow, clover, rye, camelina, and sugar beet) as fixed effects and main plot within nitrogen as a random effect. Degrees of freedom were determined using the Kenward-Roger method.

Pairwise comparisons were made using the lsmeans statement with the Tukey method.

All treatment effects were considered significant at P ≤ 0.05. As cool-season rotations were not planted prior to sorghum in 2012, sorghum 2012 data was only analyzed for the effect of nitrogen fertility. Linear regressions were performed for sorghum dry biomass yield, sorghum tissue nitrogen concentration, and sorghum tissue nitrogen removal across years in a sorghum-fallow system separately for moderate and low

fertility with Microsoft Excel 2007 (Microsoft Corp., Redmond, WA, USA), and slopes considered significant at P ≤ 0.05.

Results and Discussion

Sorghum

Sorghum dry biomass yields in 2012 were not significantly affected by nitrogen fertility, and averaged 15.2 Mg ha-1 (Table 3-4). This is consistent with prior literature for one-year nitrogen fertility studies on sandy soils, which have shown minimal effects of nitrogen fertilizer addition on sorghum dry biomass yields after one season (Erickson et al., 2012). However, estimated sugar yields were significantly higher under moderate than low nitrogen fertility (Table 3-5), likely due to the combination of higher moisture and higher Brix under moderate fertility. In both 2013 and 2014, dry biomass yields were affected by fertility and were 55 and 60% higher respectively under moderate versus low fertility (Table3-4). Sugar yields followed the same pattern as dry biomass yields, but were more strongly affected by fertility, with increases of 93 and 83% from low to moderate fertility in 2013 and 2014 respectively (Table 3-5). The moderate fertility rate in the present study is within 15 kg N ha-1 of the identified optimum nitrogen rate for soluble carbohydrate accumulation, and results are consistent with the more rapid increase in sugar yields compared with dry matter yields observed by Thivierge et al.

(2015). Additionally, sorghum biomass yields under low fertility with a winter fallow over the 3 years showed a linear decrease in yield (Table 3-6, Figure 3-3). This data suggests that long-term, low-input sorghum production with high yields is not feasible, in opposition to previous work that has suggested sorghum may be produced with minimal fertilizer inputs. Nitrogen effects on whole plant moisture were consistent across years, with the moderate nitrogen rate treatment having consistently higher whole plant

moisture than the low nitrogen rate, which is consistent with prior results (Table 3-4).

This may be partially attributable to increased stem and decreased dead leaf fractions of dry biomass (Table 3-7), as well as decreased nitrogen availability potentially resulting in decreased root growth and water uptake. Moderate fertility resulted in increased Brix of stems in 2012 and 2014; prior research has shown mixed effects of fertility, with some reports of decreasing Brix at increased fertility (Erickson et al., 2011), and alternate reports of increasing Brix with increasing fertility (Holou and Stevens,

2012). Brix values for 2014 (9.9-11.3) were substantially lower than 2012 and 2013, and lower than typically reported for M81-E (Lingle et al., 2012). This was likely due to the late planting date, necessitated by initial stand failure from herbicide damage, and is consistent with prior reports of decreasing Brix with delayed planting (Almodares and

Mostafafi Darany, 2006). Brix in 2013 was not significantly affected by N rate, cover crop or the interaction at P ≤ 0.05, and averaged 12.4 (Table 3-4).

Cover crop effects on dry biomass production were significant in 2013, with sorghum producing more biomass when preceded by clover or beets than rye (Table 3-

4). On-farm trials in Iowa demonstrated similar results in corn when followed by a fall- seeded clover, which increased yield relative to a fallow control (Fawcett et al., 2014).

However, cover crop did not affect sorghum dry biomass yields in 2014. This was likely due to the replanting of sorghum in 2014 that occurred 49 days after cover crop incorporation, versus 14 days after incorporation in 2013. As shown in Chapter 4, the majority of additional nitrogen that an incorporated cover crop may supply is lost within the first 30 days after incorporation. The cover crop by fertility interaction was not

significant in either year (Table 3-4). Sugar yields tracked biomass yields for cover crop effects in both years, and were significant in 2013 and not 2014.

Dry biomass partitioning was consistent across years for nitrogen rate, with moderate fertility treatments consistently having a higher fraction of total biomass as grain heads, and a lower fraction of total biomass as dead leaf, than low fertility treatments (Table 3-7). Based on visual observation, senesced leaves did not detach from the stalk prior to harvest. The increased fraction of dead leaf tissue is attributable to increased nitrogen translocation to newer leaves due to nitrogen deficiency, supported by SPAD readings (data not shown), that showed lower values in low versus moderate fertility plots. Cover crop effects on tissue partitioning showed a similar pattern, with clover rotations that increase nitrate availability in the soil, increasing partitioning to grain heads relative to rye and camelina in 2013 and fallow in 2014.

Clover rotations also had a reduced fraction of dead leaf biomass relative to fallow, camelina and rye in 2013 (Table 3-7).

Total nitrogen removal by sorghum was higher in all years for moderate versus low fertility sorghum (Table 3-8), and differences were driven primarily by differences in total biomass yields. Tissue N concentrations were not affected by application rate in

2012 or 2014 (6.4 and 4.8 mg N g dry biomass-1 respectively), but were affected in 2013 and were higher in moderate than low fertility applications (Table 3-8). Cover crop effects on tissue N concentration were significant in 2013 for clover and fallow systems, with clover resulting in higher tissue N concentrations than fallow systems. Gentry et al.

(2013) showed an apparent nitrogen credit of 30 to 48 kg N ha-1 from red clover to corn, similar to the 27 kg N ha-1 difference between the fallow and clover systems in 2013.

These results are also consistent with Kuo et al. (1996), who showed that yield effects on a corn crop were more influenced by the N concentration of an incorporated cover crop than total nitrogen supplied by the cover. Additionally, the reported time to mineralize half of the available nitrogen from an incorporated cover crop ranged from 18 to 28 days; thus, planting 14 days following cover crop incorporation in 2013 resulted in the majority of cover crop N remaining available during sorghum growth, while in 2014 the delayed planting likely resulted in the majority of the nitrogen having been mineralized and lost prior to planting.

Consistent with the strong linear decline in sorghum biomass yields under low input conditions discussed above, both sorghum tissue nitrogen concentration and total sorghum nitrogen removal showed strong negative linear trends across years (Table 3-

6; Figure 3-3).These results further support the inability to sustainably produce sorghum under continuous low-input conditions. However, further research to optimize cover crop management in rotation with sorghum planting and maximize nitrogen contribution from the rotation crop may allow for the apparent credit of 27 kg N ha-1 to sorghum from red clover to be combined with reduced nitrogen fertility to reach the predicted nitrogen fertilization requirement for yield maintenance with reduced fertilizer inputs. The observed credit of 27 kg N ha-1 may also be increased by alternative management practices that might conserve or concentrate cover crop nitrogen returns to the system, and will be discussed in more detail in Chapter 4.

Rotational Crops

In 2012 and 2013, rotation crop tissue nitrogen concentrations for incorporated biomass were highest for red clover, followed by beet leaves, and lowest in rye (Table

3-9, Table 3-10), and are consistent with previously reported values (Newman et al.,

2010; Wiesler et al., 2002). Camelina seeds had the highest observed nitrogen concentrations, and beet root was lowest (Table 3-9), but both were removed from the plots to simulate commercial harvest prior to incorporation. Total aboveground dry biomass yields were highest in clover and sugar beet in 2012-13, and highest for clover in 2013-14, averaging 5.0, 5.0, and 3.7 Mg ha-1 respectively, relative to other crops

(Table 3-9, Table 3-10). Reports of pure stand dry matter yields of red clover are rare in the peer-reviewed literature, but are generally lower than those observed here

(Quesenberry and Blount, 2006). Gentry et al. (2013) reported red clover biomass yields of 2 to 2.2 Mg ha-1 in Michigan, much lower than obtained in the present work, but with tissue nitrogen concentrations of 37 to 45 g N kg-1 dry biomass, that are higher than the

29-30 g N kg-1 obtained here. However, Mia et al. (2014) reported similar tissue nitrogen concentrations of 25 to 34 g N kg-1 biomass in a pure stand of unspecified red clover.

Climactic conditions in the present study were highly favorable in both years for clover production (mild winters with cool, wet springs) and, as such, nitrogen may have been allocated to growth over quality, as yields are consistent with high-end yields of

Quesenberry and Blount (2006).

Beet root dry yield was 5.0 Mg ha-1 in 2012-13 and only 1.1 Mg ha-1 in 2013-14, but in both years was substantially lower than reported by Koga (2008). Root yields were lower than reported by Lauer (1995) for beets grown in Wyoming with no applied nitrogen. Lauer (1995) reported fresh beet yields of 30 to 50 Mg ha-1 with nitrogen fertility increasing from 0 to 336 kg N ha-1, while fresh yields in the current study were 27 and 6 Mg ha-1 in 2012-13 and 2013-14 respectively (Table 3-9 and Table 3-10, calculated as ‘Dry Yield’ divided by ‘Dry Matter Concentration’). Beet top yields were

also lower in 2013-14 than 2012-13 (1.2 vs 2.1 Mg ha-1 respectively). Low beet root and shoot yields are likely attributable to multiple factors, including sub-optimal nitrogen fertility, short growing season, and pest and disease pressures. Subsequent work with beet management in Florida has resulted in increased yield and decreased yield loss due to disease and insect pressures [data not shown], and is consistent with the work of

Lauer (1995). Further refinement of sugar beet production practices in Florida may increase beet yields to practical levels.

Rye biomass yields obtained in the current study of 2.4 to 3.4 Mg ha-1 are similar to Kuo and Jellum (2002), who reported biomass yields of 1 to 2.5 Mg ha-1 over 4 years for rye grown in Washington state. However, these yields are generally lower than reported for well-managed rotationally cropped rye, as shown by Mirsky et al. (2012), who reported averaged rye biomass yields of 6.3 to 10.8 Mg ha-1 across treatments and locations. Nitrogen uptake was also consistent between the current work, that found 26 to 37 kg N ha-1 with 50 kg ha-1 applied nitrogen, and Kuo and Jellum (2002), who observed uptake rates of 13 to 33 kg N ha-1 in an unfertilized system. Tissue nitrogen concentrations were also consistent between the studies, averaging 10 to 11 g N kg-1 biomass in the present study and ranging from 13 to 16 g N kg-1 in Washington. The relatively high C:N ratio of rye determined by Kuo and Jellum (2002), ranging from 24 to

28, may play a role in nitrogen immobilization in the soil and reduced yields in sorghum following rye rotations. This is supported by Fawcett et al. (2014), who observed lower corn grain yields following rye in Iowa.

Camelina seed oil concentrations were not significantly affected by nitrogen fertility rate of sorghum in 2012, and threshed seeds averaged 358 mg g-1 oil (P = 0.65),

consistent with prior research by Budin et al. (1995). However, seed yields were much lower than reported across 32 genotypes by Vollmann et al. (1996), potentially due to early flowering, sub-optimal nitrogen fertility, or insufficiently dense stands, but are within the range of 100 to 2900 kg ha-1 reported by Schillinger et al. (2012). Wysocki et al. (2013) demonstrated an increase in camelina seed yield with applications of up to 90 kg N ha-1, with greater effects in nitrogen-poor soils. The difficulty of mechanical weed control in the narrowly spaced rows and poor canopy closure led to increased weed pressure in 2013-14 and a lack of harvestable seed yield. However, total aboveground

N removal was similar between camelina, beet leaves, and rye in 2013-14 (Table 3-10).

Further research on management strategies, particularly regarding stand establishment, fertility, and weed management, will be necessary for camelina to become a viable crop.

Rotational crop yields and tissue nitrogen were not affected by sorghum fertility in either 2012-13 or 2013-14 (Table 3-9, Table 3-10). While some studies report residual effects of applied fertilizers in subsequent crops, this effect was not observed in the current study. This is likely due to a variety of factors, including: i) low initial available soil N (Table 3-1), ii) sandy soils, resulting in rapid loss of nitrogen to leaching and volatilization, and iii) nitrogen removal by sorghum that is greater than application rate

(Table 3-8), leaving little to no residual fertilizer N for the rotational crops to scavenge.

Additionally, total nitrogen contained in the nitrogen fertilized rotational crops (sugar beets, camelina, and rye) never exceeded applied fertilizer nitrogen, and ranged from

34 to 75% of applied nitrogen. These nitrogen recovery rates are substantially lower than in sorghum, that removed more nitrogen than was applied in both 2012 and 2013,

and may be due to a variety of factors including: insufficient stand density, leaching losses, or poor uptake efficiencies.

Conclusions

Low nitrogen-input sweet sorghum production is likely not sustainable over the long term in the Southeast due to the observed nearly 2 Mg ha-1 yearly decline in dry biomass yields, but a clover rotational crop may have the ability to offset chemical nitrogen fertilizer requirements, and provided the greatest nitrogen return to the soil. In

2013, clover incorporated 2 weeks prior to sorghum planting provided an additional 27 kg N ha-1 over a winter fallow; decreasing the interval between incorporation and planting, or switching to alternative cover crop strategies, such as strip tillage, may substantially increase this nitrogen credit. However, nitrogen inputs from clover are unlikely to meet the full nitrogen demand of sweet sorghum, given that rotational crop nitrogen does not offset fertilizer nitrogen at a 1:1 rate. Bioenergy rotation crop feasibility could be improved by modifications to management practices, such as splitting fertilizer applications to beets and postponing beet harvest until later in May or early June and increasing beet yields (data not shown), but at the detriment of sorghum yields by delaying planting date. Sorghum under the low nitrogen-input rate of the current study did not benefit over fallow from return of beet tops, despite the 27 to 46 kg ha-1 of available nitrogen, but may benefit over the long term if these inputs can offset the declines observed in low-input sorghum tissue nitrogen concentrations and likely eventual corresponding decreases in yield.

Table 3-1. Soil properties at planting. Factor Unit Value Soil Texture Sand g kg-1 884 Silt g kg-1 52 Clay g kg-1 64 Organic Matter g kg-1 12 Soil pH 6.5 Nitrate Nitrogen kg ha-1 5.5

Table 3-2. Total rainfall and average daily high and low temperature for each crop production season- summer sweet sorghum in 2012, 2013 and 2014, and cool-season rotations over 2012-2013 and 2013-2014. Sorghum Rotations Sorghum Rotations Sorghum 2012 2012-13 2013 2013-14 2014 Total Rainfall (mm) 1019 181 759 802 491 Average Daily High 31.7 23.1 31.3 22.5 31.9 Temperature (°C) Average Daily Low 20.7 9.0 20.9 9.9 18.9 Temperature (°C)

Table 3-3. Seeding depth and rates for sweet sorghum and cool-season rotation crops. Rates and depths were consistent across all years. Crop Seed Depth Seeding Rate Sweet Sorghum 2.5 cm 100,000 seed ha-1 ‘M81-E’ Clover 1 cm 6 kg ha-1 Rye 7.5 cm 20 kg ha-1 Camelina 1 cm 6 kg ha-1 Sugar Beet 2.5 cm 100,000 seed ha-1

Table 3-4. Sorghum dry biomass yields, moisture as a fraction of fresh biomass, and brix of expressed juice by year for main effects and the interaction. Data are means across four replications for the interaction, eight replications for the main effect of cover, and twenty replications for the main effect of N rate. 2012 2013 2014 Factor Dry Yield Moisture °Brix Dry Yield Moisture °Brix Dry Yield Moisture °Brix Mg ha-1 g kg-1 Mg ha-1 g kg-1 Mg ha-1 g kg-1 Cover Clover - - - 16.6a† 708 12.6 13.0 710 9.9c Beet - - - 15.8a 714 12.6 14.6 714 10.5abc Fallow - - - 15.5ab 711 12.4 13.1 710 11.4a Camelina - - - 14.4ab 720 12.2 12.9 725 10.1bc Rye - - - 13.0b 719 12.3 13.3 714 11.1ab N rate Moderate (Mod.) 16.9 691a 16.9a 18.4a 732b 12.6 16.5a 712b 11.3a Low 13.5 666b 16.2b 11.8b 697a 12.1 10.3b 707a 9.9b Cover x N rate Clover x Mod. - - - 19.3 724 12.7 16.2 719 10.4 Clover x Low - - - 14.0 693 12.5 9.8 702 9.5 Beet x Mod. - - - 18.6 731 12.9 16.0 724 11.6 Beet x Low - - - 13.1 697 12.2 13.2 705 9.4 Fallow x Mod. - - - 18.8 725 12.8 15.7 717 11.9 Fallow x Low - - - 12.2 696 12.0 10.4 702 11.0 Camelina x Mod. - - - 18.5 736 12.8 17.1 723 11.0 Camelina x Low - - - 10.3 705 11.6 8.8 728 9.2 Rye x Mod. - - - 16.8 742 12.1 17.3 729 11.6 Rye x Low - - - 9.3 697 12.5 9.4 700 10.7 P-value Cover - - - 0.0028 0.0506 0.4725 0.7080 0.4715 0.0043 N rate 0.1072 0.0164 0.0504 0.0003 <.0001 0.1531 0.0019 0.0375 0.0114 Cover x N rate - - - 0.3943 0.3984 0.0684 0.2741 0.4361 0.3497 †Means not followed by the same letter within a column and factor are different at P ≤ 0.05.

Table 3-5. Sorghum estimated stem sugar yields by year for main effects. Data are means across eight replications for the main effect of cover, and twenty replications for the main effect of N rate. Estimated Sugar Yield 2012 2013 2014 Factor Mg ha-1 Cover Clover - 4.44a 2.41 Beet - 4.38a 2.79 Fallow - 4.11ab 2.72 Camelina - 4.07ab 2.94 Rye - 3.64b 3.01 N rate Moderate (Mod.) 5.02a 5.43a 3.58a Low 3.61b 2.82b 1.96b P-value Cover - 0.0160 0.4334 N rate 0.0230 <.0001 0.0017 Cover x N rate - 0.2110 0.9450

Table 3-6. Linear regression and R2 value for changes in total dry biomass yield, tissue nitrogen concentration, and nitrogen removal by sorghum over three years in a sorghum-fallow system. Nitrogen Fertility Slope Intercept R2 P value Dry Biomass Yield Moderate -1.00 19.4 0.10 0.32 Low -1.97 14.9 0.44 0.04 Tissue N Concentration Moderate -0.05 0.73 0.11 0.28 Low -0.11 0.73 0.78 <.0001 Nitrogen Removal Moderate -16.2 141 0.20 0.15 Low -19.1 98 0.80 <.0001

Table 3-7. Dry biomass fractions of stem, grain heads (GH), dead leaves (DLF), and green leaves (GLF) as mg of tissue per g dry biomass in sorghum by year for main effects and the interaction. Data are means across four replications for the interaction, eight replications for the main effect of cover, and twenty replications for the main effect of N rate. 2012 2013 2014 Factor Stem GLF DLF GH Stem GLF DLF GH Stem GLF DLF GH Cover Clover - - - - 754 29 89c† 128a 666b 23 97 214a Beet - - - - 762 30 95bc 113ab 687ab 32 80 191ab Fallow - - - - 754 31 111ab 104ab 696a 39 93 172b Camelina - - - - 753 25 121a 101b 686ab 24 90 199ab Rye - - - - 764 25 116a 95b 682ab 35 87 197ab N rate Moderate (Mod.) 779 79 41b 101a 766a 36a 81b 117a 717a 43a 59b 208a Low 792 74 57a 77b 749b 20b 131a 100b 649b 19b 120a 182b Cover x N rate Clover x Mod. - - - - 768 31b 73 128 688 37 62 212 Clover x Low - - - - 741 26b 104 129 643 9 132 216 Beet x Mod. - - - - 776 34ab 75 115 723 51 53 174 Beet x Low - - - - 748 26b 115 112 652 13 106 209 Fallow x Mod. - - - - 750 50a 80 121 738 48 61 153 Fallow x Low - - - - 759 13b 141 87 653 31 125 191 Camelina x Mod. - - - - 764 33ab 90 113 719 30 59 192 Camelina x Low - - - - 744 16b 152 89 653 19 120 207 Rye x Mod. - - - - 774 31b 87 109 718 47 57 179 Rye x Low - - - - 754 19b 145 82 646 22 117 216 P-value Cover - - - - 0.582 0.210 0.001 0.009 0.045 0.268 0.228 0.005 N rate 0.185 0.421 0.001 0.039 0.002 0.019 <.001 0.005 <.001 0.017 <.001 0.010 Cover x N rate - - - - 0.163 0.001 0.115 0.242 0.302 0.577 0.853 0.296 †Means not followed by the same letter within a column and factor are different at P ≤ 0.05.

Table 3-8. Whole plant tissue N concentrations and total aboveground N in sorghum biomass by year for main effects and the interaction. Data are means across eight replications for the main effect of cover, and twenty replications for the main effect of N rate. Sorghum Tissue Nitrogen 2012 2013 2014 Factor g kg-1 kg ha-1 g kg-1 kg ha-1 g kg-1 kg ha-1 Cover Clover - - 7.04a† 119a 4.66 60 Beet - - 6.96ab 113ab 4.86 74 Fallow - - 5.71b 92ab 5.20 66 Camelina - - 6.16ab 95ab 4.63 67 Rye - - 6.44ab 85b 4.83 72 N rate Moderate 6.68 113a 7.35a 136a 5.11 85a Low 6.11 82b 5.58b 66b 4.56 51b P-value Cover - - 0.0349 0.0077 0.8961 0.7451 N rate 0.1263 0.0241 <.0001 0.0004 0.4765 0.0686 Cover x N rate - - 0.3235 0.4581 0.6098 0.8420 †Means not followed by the same letter within a column and factor are different at P ≤ 0.05.

Table 3-9. Cool-season rotation crop dry biomass yield (all aboveground biomass for rye, clover, and camelina seed and separated as aboveground leaves and belowground root for beets), dry matter concentration, tissue nitrogen concentration, and total nitrogen contained in biomass fraction for 2012-13 for main effects and the interaction. Data are means across four replications for the interaction, and eight replications for the main effect of cover. Factor Dry Yield Dry Matter Tissue Total Concentration Nitrogen Nitrogen Mg ha-1 g kg-1 g kg-1 kg ha-1 Crop Clover 5.0a† 132c 29.5b 140a Beet root 5.0a 187b 5.4e 27cd Beet leaf 2.1c 137c 22.4c 46b Camelina seed 0.4c nd◊ 36.9a 15d Rye 3.4b 566a 10.8d 37bc Cover x N rate Clover x Mod. 5.0 129e 29.3 148 Clover x Low 5.0 135e 29.7 132 Beet root x Mod. 5.2 184cd 21.8 25 Beet root x Low 4.9 189c 21.3 29 Beet leaf x Mod. 2.2 133ex 4.7 47 Beet leaf x Low 2.1 140de 6.0 44 Camelina seed x Mod. 0.4 nd 35.5 12 Camelina seed x Low 0.5 nd 38.2 18 Rye x Mod. 3.2 529b 11.3 37 Rye x Low 3.7 603a 10.4 38 P-value Cover <.0001 <.0001 <.0001 <.0001 N rate 0.9659 0.0617 0.2235 0.6741 Cover x N rate 0.8998 0.0011 0.6824 0.3542 †Means not followed by the same letter within a column and factor are different at P ≤ 0.05. ◊nd indicates measurement was not determined,

Table 3-10. Cool-season rotation dry biomass yield (all aboveground biomass for rye, clover, and camelina and separated as aboveground leaves and belowground root for beets), dry matter concentration, tissue nitrogen concentration, and total nitrogen contained in biomass fraction for 2013-14 for main effects and the interaction. Data are means across four replications for the interaction, and eight replications for the main effect of cover. Factor Dry Yield Dry Matter Tissue Total Concentration Nitrogen Nitrogen Mg ha-1 g kg-1 g kg-1 kg ha-1 Crop Clover 3.7a 196c† 28.7a 108a Beet root 1.1c 193c 5.9e 7c Beet leaf 1.2c 164c 22.5b 27b Camelina 1.9b 260b 16.1c 29b Rye 2.4b 463a 11.0d 26b Cover x N rate Clover x Mod. 4.1a 205 29.0 118 Clover x Low 3.4ab 187 28.3 97 Beet root x Mod. 1.0e 194 5.9 6 Beet root x Low 1.2e 192 5.9 7 Beet leaf x Mod. 1.1e 157 22.1 25 Beet leaf x Low 1.3de 171 23.0 29 Camelina x Mod. 2.0cd 269 15.6 32 Camelina x Low 1.8cde 251 16.6 26 Rye x Mod. 2.8bc 469 11.3 30 Rye x Low 2.0cd 457 10.8 22 P-value Cover <.0001 <.0001 <.0001 <.0001 N rate 0.1286 0.4955 0.8491 0.1028 Cover x N rate 0.0213 0.7214 0.7456 0.0601 †Means not followed by the same letter within a column and factor are different at P ≤ 0.05.

Figure 3-1. Field layout of sorghum fertility treatments and cool-season rotational crops. Fertility treatments are applied to main plots, and were 20 kg N ha-1 for low (L) fertility, and 100 kg N ha-1 for moderate (M) fertility. Winter rotations were assigned to sub-plots are indicated as: 1=clover, 2=rye, 3=fallow, 4=camelina, and 5=sugar beet.

2012 2013 2014 Sweet Winter Sweet Winter Sweet Sorghum Rotational Sorghum Rotational Sorghum Crop Crop

Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct

Plant Ratoon Plant Ratoon Plant Ratoon

Sweet Sorghum Sweet Sorghum Sweet Sorghum Double Cropping Double Cropping Double Cropping Window Window Window

Figure 3-2. Timeline for production of sweet sorghum in rotation with a cool-season crop in the current study (above), and potential sweet sorghum double cropping window in Florida (below). Vertical bars represent the 1st of each month, and shaded boxes the listed crop. Lighter shading identifies cool-season rotations, and darker shading sweet sorghum production.

Figure 3-3. M81-E means, standard deviations, and linear regressions when significant across years (2012 through 2014) under moderate (filled squares) and low (open squares) nitrogen input fallow systems (n=4 per point). A) Sorghum dry biomass yield in Mg ha-1, B) Sorghum whole plant tissue nitrogen concentration in g kg-1, and C) Total nitrogen removal by aboveground sorghum biomass in kg N ha-1.

CHAPTER 4 EFFECT OF NITROGEN FERTILITY AND ROTATION CROP ON SOIL NITRATE DYNAMICS OF SWEET SORGHUM CROPPING SYSTEMS

Background

High levels of chemical nitrogen fertilization are a staple of modern agricultural practices in the Unites States, with 11.6 million metric tons of nitrogen applied to agricultural lands in 2011 (USDA-ERS, 2014a). These inputs, combined with other factors, have significantly increased agricultural productivity over the last 50 years, but also have negative effects on agroecosystems, including decreasing soil fertility and increasing erosion and leaching losses (Matson et al., 1997; Tilman et al., 2002).

Chemical nitrogen fertilizer application has also been indicted as a major driver of increasing N2O concentrations in the atmosphere, an important greenhouse gas and cause of concern with regard to climate change, and is a major energy sink due to the production of nitrogen fertilizers from fossil fuels and the associated energy inputs (Park et al., 2012). Growing concern over the impacts of chemical fertilizers on agroecosystems and the broader environment has spurred research on means to reduce nitrogen inputs and mitigate the detrimental effects of fertilization, while maintaining yields (Grant et al., 2002; Smith et al., 1997). Based on prior research,

Grant et al. (2002) concluded that continuous rotated cropping systems can provide multiple benefits to subsequent crops and the agroecosystem, particularly in no-till systems relative to fallow systems, provided proper management strategies are followed.

Rotational cropping and residue return have been advocated to decrease the need for external nitrogen inputs, but crop selection and return practices must be properly managed. Vigil and Kissel (1991) determined that return of crop residues with a

C:N ratio greater than 40, which typically corresponds to tissue N concentrations equal to or lower than 10 g N kg-1 biomass, will generally result in net soil nitrogen immobilization and not a return of N to a subsequent crop. Residues with higher tissue

N concentrations have the potential to return significant quantities of nitrogen to the soil and a subsequent crop. Yamoah et al. (1998) showed that grain sorghum grown following soybean had higher average yields over an 18-year period relative to continuous sorghum (5130-7120 kg ha-1 versus 4050-6260 kg ha-1, respectively) in

Nebraska, and that soybean could contribute up to 83 kg N ha-1 yr-1 to sorghum depending on climactic factors. However, Havlin et al. (1990) demonstrated that continuous sorghum cropping provided the greatest increase in soil nitrogen when compared with sorghum-soybean or continuous soybean systems, and that the effects of fertilizer application on soil organic nitrogen were minimal in Kansas. These results indicate that the residual fertilizer nitrogen credit to a subsequent crop is strongly affected by environmental conditions as well as crop selection (Cameron et al., 2013).

Lacking from these studies though is a quantified understanding of the temporal dynamics of nitrogen in the rooting zone of the subsequent crop, as these studies integrated the cumulative effects of years. An understanding of environmental effects, particularly temporal dynamics in a sub-tropical environment, is also lacking, as much of the prior research has focused on temperate environments.

Prior research on grain sorghum following a crimson clover crop has shown the potential for legumes to offset fertilizer requirements. However, even when clover contained 202 to 216 kg N ha-1, only an estimated 128 kg N ha-1 of fertilizer N was replaced by the clover-supplied N (Hargrove, 1986). Of the incorporated nitrogen from

clover, only 59-63% was accessible to the succeeding sorghum crop, likely due to multiple factors, including:

 incomplete breakdown and release of nitrogen from the clover biomass  microbial immobilization of released nitrogen in the soil  leaching losses to the environment  volatilization of nitrogen-containing compounds

McVay et al. (1989) reported similar effects, with crimson clover preceding grain sorghum replacing 21-80 kg fertilizer N ha-1 based on yield relative to an unfertilized sorghum system with cool-season fallow. In contrast, Singh et al. (2012) reported nitrogen uptake rates of 133 to 139 kg N ha-1 by two sweet sorghum cultivars grown in the Southeastern US for two years at two sites when fertilized with chemical N at 135 kg ha-1, a recovery rate of 99-103% that is considerably higher than found with the legume residue studies. These differences in nitrogen uptake are attributable to multiple factors, including cover crop decomposition rate and microbial lockup of nitrogen, but must be considered when selecting rotational crops and calculating nitrogen offsets. In order to accurately determine how much nitrogen may be supplied to a subsequent crop, soil nitrogen mineralization and availability should be investigated for various rotations and environmental conditions.

Nitrogen mineralization and availability in the soil can be measured in numerous ways (Bai et al., 2012; DiStefano and Gholz, 1986). Soil incubation in buried plastic bags or columns is a common measure of soil mineralization, but may produce an unnaturally constrained environment for nitrogen mineralization, including excessive moisture, even in the sandy soils of North Florida (DiStefano and Gholz, 1986; He et al.,

2000). Ion-exchange resins have been widely used and reviewed as a method for in-situ comparisons of nitrogen dynamics, and have the ability to accumulate cations or anions

from water moving through the soil. Exchange resins also mimic the effects of plant roots, and do not allow ion concentrations to build in the surrounding soil (Durán et al.,

2013; Ziadi et al., 2006). Additionally, the use of ion-exchange methods has been shown to be sensitive to smaller perturbations in soil ion availability than other methods while maintaining a more natural, and potentially less disturbed, environment after insertion. Research on low-fertility sandy soils under lab conditions has shown that ion- exchange resins can differentiate between applied nitrogen rates of 0 or 11.2-22.4 kg N ha-1, but not between 0 and 11.2 kg N ha-1 (Jones et al., 2012). While this level of resolution does not allow for exact quantification at low levels of available nitrates in the soil, it does allow for identification of agronomically relevant rates and differences.

The objectives of the current study were to 1) quantify nitrate-nitrogen availability on a monthly basis in the rooting zone as affected by nitrogen fertility and cool-season rotational crops; 2) determine the seasonal net change in plant available nitrate-nitrogen in the rooting zone of a summer sorghum crop as a result of fertility and cool-season rotation management; and 3) identify increases or decreases in soil nitrate-nitrogen and soil total nitrogen after three years of sorghum production.

Materials and Methods

Experimental Design and Field Management

The field layout, cultural practices, management and harvest of the field experiment and aboveground biomass were as described in Chapter 3. This chapter deals specifically with continuous in-situ monitoring of available nitrates from cover crop incorporation, fertilization, and natural processes in the soil by use of ion-exchange resins, with monthly sampling. Additionally, yearly destructive soil testing for organic matter, total Kjeldahl nitrogen (TKN), nitrates, and bulk density was conducted. Weather

data collected by the Florida Automated Weather Network at Citra, FL over the course of the study (January 2013 to September 2014) is presented in Figure 4-1 (cumulative rainfall) and Figure 4-2 (average daily air temperature at 2 m and soil temperature at -

10 cm).

Monthly Soil Nitrogen Monitoring

Soil nitrate-nitrogen availability was monitored with ion-exchange resin bags installed approximately every 30 days in each plot, beginning in January of the first year of rotational crop planting and lasting until after harvest of sweet sorghum in the 3rd year. Bags were constructed monthly by packing 5 g wet weight of anion-exchange resin (Amberlite IRA-400(Cl), Alfa Aesar, Ward Hill, MA) in a synthetic nylon mesh bag.

Zip-ties were used to seal the resin inside the bag (Castle, 2009). Prior to soil incubation, bags were leached with 0.5 M HCl for at least 30 minutes, and then soaked in sequential deionized-water baths until the resultant pH was neutral. Bags were installed in the field, one per plot, on a monthly basis by removing an intact soil core to a depth of 20 cm using a soil core sampler that was replaced intact over each bag. Bags were located within 5 cm of one of the two inner rows of sorghum over the summer, and beet over the winter, and were between rows in the middle of each plot over the winter for all other rotational crops. Bags were removed at the end of each month by means of a string tied to each bag at installation and left on the surface.

After removal, loose soil was brushed from the surface of each bag and any roots that had grown into a bag were removed by forceps. Cleaned bags were stored in individual 50-mL conical centrifuge tubes (Corning, Inc., Corning, NY) at 4°C until extraction. Each bag was extracted with a known volume of 0.1 M HCl and 2.0 M NaCl

(ACS Reagent Grade, Thermo Fisher Scientific, Waltham, MA) in capped tubes while shaking at 100 RPM for at least 12 hours at ambient temperature. Extracts were filtered through coarse porosity filter paper to remove residual soil particles, and then analyzed for nitrate-N according to EPA Method 353.2. Blank correction bags were prepared as above on a monthly basis and stored in sealed containers at 4°C until extracted with the soil incubated bags. Extraction solutions and water were tested monthly for nitrate- nitrogen.

Nitrate-nitrogen availability was calculated as the sum of adsorbed nitrate- nitrogen per plot for each month when sorghum was present in the field, scaled based on resin bag size. To scale resin bag measurements, the following formula was used:

kg ha-1 adsorbed nitrogen = resin bag surface area x mg adsorbed nitrogen area of a hectare 1,000,000

Where: o Surface area of the top face of a resin bag in cm (length * width) o Surface area converted to m2: (Surface area)/(10,000) o Area of a resin bag divided by area of a hectare (10,000 m2) o Quantity of nitrogen adsorbed by resin bag in mg o To convert mg to kg, divide mg by 1,0000,000

Uniform incorporation of the cover crops to a depth of 40 cm (the measured depth of the rototiller used for cover crop incorporation) was assumed.

Soil Testing

Initial and final total soil nitrogen and nitrates were measured at the beginning of the study in April 2012, prior to sorghum planting but following field preparation, and at harvest of the third sorghum crop in September 2014. Samples for total soil nitrogen and nitrates were collected by combining 5 cores taken from each plot using a 2.54 cm inner diameter metal pipe, with depths of 0 to 25 and 25 to 50 cm. Samples were air

dried, passed through a 2-mm sieve, and screened for removal of root biomass.

Nitrogen analyses were performed by the UF Analytical Research Laboratory (ARL).

Total soil nitrogen was measured as TKN following EPA Method 351.2. Nitrate-N was measured according to EPA Method 353.2 and Ammonium-N was measured according to a modified EPA Method 350.1 following water extraction of a known weight of prepared soil. Organic matter was also measured on samples collected in 2012, and on a subset of samples collected using the procedure above in September 2014 and May

2015. Organic matter was determined by loss on ignition by combustion of 40 g of oven- dried soil in a muffle furnace for a minimum of 6 hours at 450°C (Mylavarapu and Moon,

2007).

Soil bulk density was determined in April 2012 prior to planting, and in

September 2014 prior to field clearing. In 2012, bulk density was measured as a field average by randomly sampling cores to depths of 25 and 50 cm, and in 2014 four composite samples were taken per plot, per depth. A 3.82 cm inner diameter sampling corer was used to collect samples that were oven dried at 105°C for 7 days to determine dry weight.

Statistical Analysis

Statistical analyses were performed using the analysis of variance procedure in the GLIMMIX procedure of SAS with the Gaussian (normal) conditional distribution and the identity link function (SAS, 2009). Residuals were checked for normality graphically and numerically with the Shapiro-Wilk W test, and when necessary to account for varying fertilizer regimens, normality was checked by sorghum fertility level for summer months and by cool-season crop over winter months. Data were analyzed separately by year and season (summer [sorghum], or cool-season [rotation crops]), with nitrogen rate

(low and moderate) and cool-season rotation (fallow, clover, rye, camelina, and sugar beet) as fixed effects and main plot by nitrogen rate as a random effect. For TKN, nitrates, bulk density, and organic matter, data were analyzed with depth, rotational crop, sorghum nitrogen fertility, and year as main effects, and main plot as a random effect. Degrees of freedom were determined using the Kenward-Roger method.

Pairwise comparisons were made using the lsmeans statement with the Tukey method.

All treatment effects were considered significant at P≤0.05. Where marginal treatment effects (P≤0.10) were observed, both marginal and significant effects are provided in tables. Due to loss of sample data as a result of instrument failure, values for July 2014 are not included in the tables and figures and were not included in calculations for season-long nitrogen budgets.

Results and Discussion

Soil Available Nitrogen Dynamics

Soil nitrogen availability was significantly affected by cover crop during 11 of 17 monitored time periods, and significantly or marginally interacted with sorghum fertility rate over half of those times (6 of 11 periods) (Figure 4-3). Following cover crop incorporation, all plots experience a spike in soil-available nitrates in the first month,

May (Table 4-1, Table 4-2). This effect is partially due to tillage, which introduces oxygen deeper into the soil profile, disrupts soil aggregates, and stimulates organic matter breakdown, but the effects are variable across cover crops (St. Luce et al.,

2011). In the first month after incorporation in 2013, clover plots showed nitrate levels almost three times higher than in fallow plots, and twice as high as observed for beet tops (Table 4-1). These results are consistent with prior research on clover decomposition kinetics that has shown that approximately 50% of the nitrogen

mineralized from an incorporated clover over 20 weeks occurs, and thus may be lost, in the first 4 weeks (Frankenberger and Abdelmagid, 1985). However, rye and camelina both show relatively lower soil-available nitrates than under a fallow system, though the effect was marginal (P=0.11). This is likely due to nitrogen immobilization by microbial communities breaking down the higher C:N residue and is consistent with prior research. Vigil and Kissel (1991) identified a C:N ratio greater than 40 as necessary for net nitrogen immobilization following cover crop incorporation. Later research by Kuo and Sanjou (1998) identified incorporation of greater than 60% cereal rye biomass in conjunction with a legume as resulting in increased net nitrogen immobilization. Wells et al. (2013) determined that soil nitrogen availability is reduced following rolling of a rye cover crop relative to conventional tillage and that peak nitrogen immobilization occurred 4-6 weeks after rolling.

Nitrate-nitrogen availability in the soil following cover crop incorporation shows clear patterns in 2013, particularly with regard to clover decomposition based on nitrate availability, but the reason for this pattern is unclear. Clover breakdown appears to have been slowed by increased fertilizer application, as there was a cover x nitrogen rate effect that persisted throughout the 2013 sorghum growing season. While nitrogen additions are generally known to stimulate biomass decomposition and decrease soil organic carbon (Enríquez et al. 1993; Ladd et al., 1994), recent research has indicated that this pattern does not necessarily hold true when fertilizer nitrogen is applied. Allison et al. (2013) observed that litter mass loss in a California grassland ecosystem did not vary between plots with no added nitrogen and plots amended with 60 kg N ha-1 annually. While Allison et al. (2013) did suggest that this might be an artifact of the

incubation bags used to measure litter decomposition, including the potential inability of soil fungi to access litter inside the bags, these results are not unique. Knorr et al.

(2005) demonstrated that nitrogen additions can actually inhibit biomass degradation, and that total annual nitrogen additions to the soil in excess of 125 kg N ha-1, as observed for high-fertility clover plots, decrease biomass decomposition by 9%, compared with a 17% increase when nitrogen additions are 75 to 125 kg N ha-1.

Similarly, in the current study, clover and sugar beet tops both display a trend towards slower decomposition under high than low fertility in 2013 (Figure 4-4), though these results are only statistically significant for clover. In the four months following rotational crop incorporation in 2013, soil nitrate availability for clover under moderate fertility was significantly higher than all other treatments (Figure 4-4), indicating that nitrogen availability to the crop was substantially higher. Based on van Oosterom et al. (2010a and 2010b), continued availability of nitrates in the rooting zone may allow for a near- linear increase in total plant nitrogen on a land-area basis, though the effect on yield may not be as distinct. However, Olson et al. (2013) reported that total plant nitrogen did not significantly increase from 60 to 150 days after emergence in a high-biomass producing sorghum, indicating that the majority of nitrogen may be taken up in the first

60 days. Thus, a rapid breakdown of clover under low fertility may result in the loss of nitrates from the rooting zone and a subsequent lack of benefit to the following crop if not planted shortly after incorporation.

In 2014, similar results for monthly nitrogen availability were observed in the month following incorporation. Due to stand emergence issues, however, sorghum was re-planted approximately 6 weeks following cover crop incorporation, versus 2 weeks

post-incorporation in 2013. In the first month following incorporation (May), clover produced the highest levels of soil-available nitrates, comparable to beet tops and higher than all other crops (Table 4-2). Beet tops were not significantly different from camelina, and higher than fallow and rye plots across nitrogen rates (Table 4-2).

However, the delayed release and elevated level of soil-available nitrates observed under moderate N clover in 2013 were not present in 2014, potentially due to heavy early-season rainfall, which is known to accelerate decomposition and leaching of nitrogen (St. Luce et al., 2011). Rye, camelina and fallow plots showed no differences in the first month following incorporation in 2014 (Table 4-2). During the second month

(June) following incorporation, no significant differences were observed between any of the incorporated covers (P=0.24), nor between the moderate and low fertility treatments

(P=0.99). In the fourth month following incorporation (August), the last split application of nitrogen fertilizer to the moderate fertility plots resulted in a seven-fold increase in available nitrates at 20 cm relative to the low-fertility plots. No differences in soil- available nitrates were observed during the last (5th, September) month of sorghum growth in 2014. The lack of differentiation between cover crops after the first month post-incorporation, coupled with the rapid release of nitrogen from low C:N biomass

(clover and beet tops), indicates that the succeeding crop must be planted shortly after cover crop termination to maximize potential nitrogen uptake.

In both years, known inputs of nitrogen from chemical fertilizers and incorporated clover biomass surpassed known nitrogen removal by the sorghum crop with 119 and

60 kg N ha-1 removed after additions of 240 and 208 kg N ha-1 in 2013 and 2014, respectively (Chapter 3). When considered in conjunction with total nitrate-nitrogen

availability in the rooting zone during sorghum production in each year (Table 4-3), and decreasing soil total nitrogen from 2012 to 2014 (Table 4-4), there is a substantial fraction of nitrogen unaccounted for in the crop nitrogen budgets following clover incorporation. This discrepancy suggests that a substantial fraction of nitrogen is lost to leaching or not available in the soil. Further work may focus on ascertaining the fate of this nitrogen fraction, and tailoring fertility and crop management practices to allow the sorghum crop to uptake this nitrogen while reducing chemical fertility and maintaining yields.

During rotational crop production over the fall, winter and spring of 2013-14, nitrate levels were generally low except immediately following tillage activities when higher nitrate levels were uniformly observed across all plots (3.09 kg N ha-1 in

September 2013), or in plots fertilized with 40 kg N ha-1 in a single month (beets in Jan

2013 and Jan 2014) (Figure 4-3, Figure 4-4). Tillage and incorporation of sorghum stubble produced an increase in observed nitrates at 20 cm, but the magnitude of the increase was not significantly different between treatments. Fertilization of 25-30 kg N ha-1 to beets, rye, and camelina in December of 2013 did not significantly affect soil- available nitrates (P = 0.18) but observed nitrate levels were numerically smaller in beet, rye and camelina plots relative to clover and fallow. Nitrate availability in the soil was relatively low and consistent across clover, rye, camelina and fallow plots, suggesting that N applied as fertilizer was either taken up by the crop or by soil microbial biomass.

This is supported by cover crop nitrogen removal rates that equaled or exceeded applied N (data not shown). This is also consistent with rye’s use as a scavenger of soil residual nitrogen (Kuo and Jellum, 2002), as well as the documented ability of weedy

fallows to uptake moderate levels of nitrogen. Gentry et al. (2013) demonstrated that even in southern Michigan a weedy fallow crop may contain up to 38 kg N ha-1 in aboveground tissue scavenged from the soil.

Sorghum Season Available Nitrogen

Across rotational crops, sorghum nitrogen fertilization rate affected cumulative soil-available nitrogen during the sorghum seasons, but the magnitude of the effect of fertilization was not proportional to application rate (Table 4-3). On average, low fertility treatments (20 kg N ha-1) showed half as much available nitrate in the rooting zone over the course of the 2013 sorghum growing season and three-quarters as much available nitrate during 2014 versus moderate fertility treatments (100 kg N ha-1). Given that the moderate fertility rate delivered five times more applied nitrogen than the low, this suggests significant nitrogen uptake potential by sorghum. This is consistent with prior research that has shown the high nitrogen use efficiency of sorghum, as well as the more rapid nitrogen uptake dynamic of sorghum under higher fertility (Olson et al.,

2013; Singh et al., 2012; van Oosterom et al., 2010a). In particular, recent research on sandy, low-nitrogen soils shows plant nitrogen uptake that matches or exceeds applied nitrogen (Erickson et al., 2012; Singh et al., 2012). The 10 kg N ha-1 increase in available nitrogen in the soil under increased fertility suggests that the selected fertility rates were either not in excess of necessary rates for maximum growth, or that luxury consumption may have been observed. Recent research has suggested that appropriate fertilizer application rates for maximum dry matter yields of sweet sorghum should be 107-121 kg N ha-1, which is slightly higher than the moderate fertility level

(100 kg N ha-1) applied here (Thivierge et al., 2015). The data indicate that leaching of applied nitrogen through the soil is unlikely to be an issue of concern in sorghum at the

tested fertilizer rates, and that applied nitrogen will likely be cycled through the agrocosystem or removed in biomass at harvest.

Cool-season rotational crop effects on cumulative soil-available nitrogen during the sorghum growing season were not different between moderate and low nitrogen application treatments in either year, i.e., there was no nitrogen rate x rotational crop interaction (Table 4-3). The observed effects of rotation crop were comparable between

2013 and 2014, but only marginally significant (P = 0.08) for 2014. In both years, clover incorporation resulted in increased soil-available nitrogen (251% in 2013 and 76% in

2014 relative to fallow), while incorporation of beet tops, rye and camelina stems did not significantly affect mineralized N. However, beet tops showed a trend towards increasing available soil N, while rye and camelina showed a trend towards decreasing available N based on soil-available nitrates in the month (May) immediately following incorporation (Table 4-1, Table 4-2). The substantial increase in soil-available N following clover incorporation is well-documented, as red clover has been shown to contain fixed canopy nitrogen of 150 kg N ha-1 at April harvests in Florida (Chapter 3).

Gentry et al. (2013) demonstrated a nitrogen credit to corn of 30-48 kg N ha-1 from incorporated red clover, which is comparable to the values obtained here (Table 4-3).

Similarly, sugar beet tops in the present study were fertilized with 100 kg N ha-1 each winter that was primarily apportioned to leaves, and incorporated into the soil at harvest.

While the effect of sugar beet leaves was not significant, the observed trend is likely attributable to higher N fertility on the cool-season crop. Interestingly, rye and camelina incorporation showed decreased soil-available nitrates in 2013 relative to fallow plots

(66% and 39% respectively). Due to lower N concentrations and higher C:N ratios, non-

legume rotation crops such as rye decompose and release N more slowly to subsequent crops in comparisons to legumes (Ingels et al., 1994; Sainju et al., 2001;

Smith et al., 1987a). This is supported by field observations of the crops: at incorporation, rye plots were senesced with no green leaf biomass remaining, and camelina stems had been threshed to remove seed, leaving only the woody stem.

Additionally, soil nitrogen immobilization as microbial communities attempt to break down high C:N ratio material is well documented (St. Luce et al., 2011). This is further supported by the 2014 data; due to replanting sorghum was effectively not planted until

6 weeks after crop incorporation, versus 2 weeks in 2013, and rye and camelina nitrate availability levels are within 3% and 8% of fallow levels, respectively.

Soil Nitrates and Total Nitrogen

Soil nitrate-nitrogen and total nitrogen decreased over three sorghum growing seasons when averaged across all rotational crops, depths, and fertility levels (Table 4-

4). Soil nitrate-nitrogen and TKN were higher in the upper 25 cm of the soil profile in both 2012 and 2014, while ammonium-nitrogen was higher in the 25-50 cm profile

(Table 4-5). TKN did not show a significant effect of rotational crop in 2014, but nitrate- nitrogen was affected by rotation. Nitrate levels were highest in soil cores from beet plots, likely as a result of the 100 kg N ha-1 applied over the winter, and lowest in clover plots, likely due to the lack of nitrogen fertilization and dense root system of red clover.

Overall, soil-available nitrate-nitrogen decreased to 35% of pre-plant levels after the third sorghum harvest, and TKN decreased 17% to 356 mg N kg-1 soil (Table 4-4).

Interactions among depth, rotation crop, fertility and year were not significant at P ≤

0.05. In conjunction with decreasing tissue nitrogen concentrations and total nitrogen removals by sorghum from Chapter 3, this data supports the proposition that low-input

sorghum production is unsustainable in terms of maintaining soil nitrogen, and likely soil carbon, stocks. This is consistent with Ladd et al. (1994), who observed a decrease in microbial biomass carbon, and thus soil organic matter, in nitrogen-fertilized soils. While soil organic matter was monitored in the present work, no significant effects over time were found. However, this is likely due to the magnitude of the soil nitrogen pool, and the variability associated with measurement. When scaled by bulk density (soil bulk density averaged 1.49 g cm-3 and did not vary across treatments), the soil pool contained 3200 kg N ha-1 at the start of the study, and 2700 kg N ha-1 after the end of the sorghum crop based on soil nitrogen sampling. These results are similar to those obtained by Doran et al. (1998), who observed soil organic carbon losses of 320-530 kg

C ha-1 yr-1 in a wheat-based cropping system.

Conclusions

Nitrogen mineralization from an incorporated leguminous cover crop may be sufficient to meet 10 to 27 kg of a successive crop’s nitrogen demand based on the present study, and potentially more if chemical fertilizer additions are tailored to maximize mineralization, as discussed in Knorr et al. (2005). To maximize nitrogen availability to the subsequent crop, the time between incorporation and planting should be minimized to prevent losses to leaching, volatilization, and microbial activity. The lack of differentiation in soil nitrate-nitrogen availability among the fallow, rye and camelina rotations is likely the result of insufficient sensitivity in the ion-exchange resin measurement , the relatively small quantities of nitrate-nitrogen released during decomposition and the rapid uptake by plant roots and the microbial community. The lack of substantive differences in soil nitrate availability between moderate and low fertility treatments during sorghum cropping indicates high nitrogen-uptake efficiency

and little cause for concern regarding potential environmental losses of nitrogen during sorghum production.

Table 4-1. Monthly soil-available nitrates measured by in situ ion-exchange resin at 20 cm subsurface during 2013 as kg N ha-1. Data are means across four replications for the interaction, eight replications for the main effect of cover, and twenty replications for the main effect of N rate. Factor January February March May June July August Cover Clover 0.10b† 0.07c 0.00c 11.27a 5.09a 2.24a 0.75a Beet 6.24a 2.52ab 2.07a 6.83b 0.80b 0.35b 0.09b Fallow 0.37b 2.91a 1.44b 4.27bc 0.67b 0.35b 0.00b Camelina 0.20b 1.02bc 0.00c 3.01c 0.58b 0.45b 0.00b Rye 0.07b 0.35c 0.03c 2.01c 0.76b 0.05b 0.10b N rate Moderate (Mod.) 1.08 1.45 0.95 5.62 2.18a 1.80a 0.37a Low 1.60 1.30 0.48 5.23 0.97b 0.00b 0.00b Cover * N rate Clover* Mod. 0.06 0.09 0.00b 11.38 6.85 5.17a 2.36a Clover*Low 0.14 0.05 0.00b 10.61 3.33 0.89b 0.07b Beet* Mod. 4.28 3.20 1.73a 7.58 1.60 1.29b 0.19b Beet*Low 6.86 1.83 0.91ab 6.08 0.00 0.00b 0.00b Fallow* Mod. 0.60 2.85 0.46b 4.23 0.78 1.35b 0.04b Fallow*Low 0.45 2.97 1.63a 4.32 0.55 0.04b 0.00b Camelina* Mod. 0.36 1.00 0.02b 2.71 0.76 0.51b 0.46b Camelina*Low 0.52 1.04 0.00b 3.30 0.40 0.00b 0.00b Rye* Mod. 0.10 0.11 0.06b 2.19 0.93 0.95b 0.23b Rye*Low 0.03 0.59 0.18b 1.83 0.59 0.00b 0.00b P-value Cover <.0001 <.0001 <.0001 <.0001 <.0001 0.0010 0.0083 N rate 0.3171 0.7731 0.5022 0.3417 0.0046 0.0149 0.0058 Cover x N rate 0.5990 0.5181 0.0027 0.1064 0.0713 0.0417 0.0148 †Means not followed by the same letter within a column and factor are different at P ≤ 0.05.

Table 4-2. Monthly soil-available nitrates measured by in situ ion-exchange resin at 20 cm subsurface during 2014 as kg N ha-1. Data are means across four replications for the interaction, eight replications for the main effect of cover, and twenty replications for the main effect of N rate. Factor January February March May August Cover Clover 2.12b† 0.71b 0.65b 7.93a 1.84 Beet 5.89a 1.69a 3.25a 6.78ab 3.02 Fallow 1.54b 1.67ab 2.27ab 3.93c 0.42 Camelina 1.37b 0.97ab 0.57b 4.45bc 1.42 Rye 0.85b 0.56b 0.57b 3.55c 0.92 N rate Moderate (Mod.) 2.03 0.60b 0.95 5.24 2.68a Low 2.68 1.64a 1.97 5.28 0.38b Cover * N rate Clover* Mod. 2.29 0.60b 0.36b 8.00 2.97 Clover*Low 1.96 0.82b 0.93b 7.88 0.72 Beet* Mod. 4.07 0.40b 1.83ab 5.35 5.67 Beet*Low 7.72 3.86a 4.66a 8.20 0.36 Fallow* Mod. 1.69 0.82b 1.18b 4.32 0.80 Fallow*Low 1.39 2.52ab 3.35ab 3.54 0.05 Camelina* Mod. 1.49 0.66b 0.70b 5.50 2.59 Camelina*Low 1.25 1.29b 0.44b 3.40 0.25 Rye* Mod. 0.63 0.55b 0.68b 3.73 1.34 Rye*Low 1.07 0.58b 0.46b 3.37 0.51 P-value Cover 0.0040 0.0071 0.0002 0.0004 0.1285 N rate 0.4758 0.0059 0.1229 0.8886 0.0117 Cover x N rate 0.4736 0.0047 0.0497 0.1511 0.1750 †Means not followed by the same letter within a column and factor are different at P ≤ 0.05.

Table 4-3. Cumulative soil-available nitrogen measured at 20 cm subsurface during the sorghum growing season for 2013 and 2014. Data are means across eight replications for the main effect of cover, and twenty replications for the main effect of N rate. Mineralized N kg ha-1 Factor 2013 2014 Cover Clover 37.2a† 23.8a Beet 14.4b 20.0a Fallow 10.4b 13.6a Camelina 6.8b 14.0a Rye 4.0b 14.4a N rate Moderate 20.0a 20.2a Low 9.8b 14.2b Standard Error Cover 2.8 3.0 N Rate 1.8 1.9 P-value Cover <.0001 0.0819 N rate 0.0002 0.0369 Cover x N rate 0.1277 0.7464 †Means not followed by the same letter within a column and factor are different at P ≤ 0.05.

Table 4-4. Main effect of year on soil-available nitrate-nitrogen (NO3-N) and TKN measured at sorghum planting in 2012 and after the third sorghum harvest in 2014. NO3-N TKN Year mg kg-1 2012 3.90a 426a 2014 1.36b 356b Standard Error 0.19 8.8 P-value Year 0.0283 <.0001 †Means not followed by the same letter within a column and factor are different at P ≤ 0.05.

Table 4-5. Main effects of depth, sorghum nitrogen fertility rate and rotational crop on soil-available nitrate-nitrogen (NO3-N) and TKN measured at sorghum planting in 2012 and after the third sorghum harvest in 2014. 2012 2014 NH3-N NO3-N TKN NO3-N TKN Factor mg kg-1 Depth 0-25 cm 1.16b 3.28a 523a 1.63a 442a 25-50 cm 1.51a 2.28b 329b 1.11b 270b N rate Low 1.32 3.19a 422 1.44 355 Moderate 1.35 2.37b 430 1.28 357 Cover Clover 1.28 2.87 416 0.85c 344 Beet 1.35 2.73 427 2.16a 350 Fallow 1.45 2.79 424 1.43b 361 Camelina 1.36 2.87 419 1.02bc 360 Rye 1.27 2.84 444 1.31bc 364 P-value Depth <.0001 0.0119 <.0001 0.0014 <.0001 N rate 0.7601 0.0393 0.7743 0.3401 0.9390 Cover 0.7690 0.9988 0.9642 <.0001 0.9854 †Means not followed by the same letter within a column and factor are different at P ≤ 0.05.

Figure 4-1. Cumulative daily rainfall as measured at the Plant Science Research and Education Unit in Citra, Florida over the course of the study.

Figure 4-2. Average daily air temperature at 2 m (gray line) and average daily soil temperature at -10 cm (black line) as measured at the Plant Science Research and Education Unit in Citra, Florida over the course of the study.

Figure 4-3. Monthly nitrate-nitrogen availability by sorghum fertility rate (moderate, top graph, solid line; low, bottom graph, dotted lines) at 20 cm under five different cover crops (clover [Δ], sugar beet [◊], camelina [□], rye [○], and fallow [-]). Values for each month represent the available nitrate-nitrogen adsorbed while in the rooting zone. Significant differences by cover crop (n = 8), nitrogen fertility rate (n = 20), or interaction (n = 4) within each month are indicated by C, N, or * respectively.

Figure 4-4. Monthly nitrate-nitrogen availability by winter rotational crop (clover [Δ], sugar beet [◊], camelina [□], rye [○], and fallow [-]) at 20 cm under moderate (solid line) and low (dotted lines) sorghum nitrogen fertility.

CHAPTER 5 NEMATODE POPULATION DYNAMICS OF SWEET SORGHUM AND COOL-SEASON CROP ROTATIONS ON A SANDY SOIL IN FLORIDA

Background

Sweet sorghum is rapidly gaining popularity as a sugar and biomass crop for bioenergy production, with substantial investment in breeding and management research (ARPA-E, 2015). Large-scale cultivation of sweet sorghum as a result of this research will require development of integrated cropping systems, and an understanding of potential soil pests associated with a transition to sweet sorghum production. However, sweet sorghum has not been widely cultivated in the US, and there is little existing literature on the relationship between sweet sorghum and plant parasitic nematodes. In particular, Meloidogyne incognita (root-knot) and Belonolaimus longicaudatus (sting) nematodes are common and highly destructive plant-parasitic nematodes found in sandy soils in Florida, with wide host ranges and known potential to impact crop yields.

Prior research on nematode host suitability and management with sorghum has yielded mixed results, with substantial variability between studies, locations and cultivars. McSorley and Gallaher (1992) found that Meloidogyne incognita populations increased from anywhere between 1.6 and 66 times initial population densities after a single season of grain sorghum cropping, which was comparable to the increases observed in a susceptible corn population at five of seven locations. Increases in populations of Pratylenchus spp., Paratrichodorus minor, and Criconemella spp. were also observed for grain sorghum, though at fewer sites and to a lesser degree than for

M. incognita. In contrast to these increases, Kratochvil et al. (2004) observed reductions in M. incognita and Pratylenchus penetrans populations relative to a control when a

green manure crop of sorghum-sudangrass was produced in rotation with susceptible host crops.

Nematode effects on crop yields are not well understood for sweet sorghum, and current literature on yields from field settings is sparse. An understanding of the potential effects is essential, as nematode damage has a yield reduction potential in some crops that is comparable to damage from weeds (10-25% yield loss) and insects

(Johnson and Sprenkel, 1991; Noling, 2005). Effects of population levels on grain sorghum have been documented with mixed results. McSorley and Gallaher (1992) identified no correlation between nematode densities and crop yields (including grain sorghum) across sites. Bado et al. (2011) also showed no yield response of grain sorghum to Pratylenchus, Helicotylenchus, or Trichodorus at levels as high as 407,

1892, and 122 nematodes per 100 cc soil at 90 days after planting, respectively.

However, a survey of extension personnel across the United States by Koenning et al.

(1999) reported average yield losses of approximately 1.5% for row crops, with a maximum range of 5-10%. Pratylenchus spp. were most commonly identified as the major genera responsible for yield loss across row crops in Georgia and Louisiana, while damage from Meloidogyne spp. was generally reported in Kansas, Arizona and

New Mexico. Sting nematodes (Belonolaimus longicaudatus) were also reported as a major pest and are a known major pest in some Florida agronomic production systems

(Crow and Brammer, 2001). There is a general lack of consensus on the effects of plant-parasitic nematode populations on sorghum yield and on the effects of rotational cropping systems on soil nematode populations.

The benefits of double cropping are well-documented (Ingels et al., 1994), and double cropping is increasing in the US (Seifert and Lobell, 2015). Southern Belle red clover, a potential green manure in double-cropping, has been bred for increased resistance to root-knot nematodes but is a known host of the lesion nematode

(Quesenberry and Blount, 2006; Quesenberry et al., 2005; Miller, 1978). Camelina and rye have both been shown to produce allelopathic chemicals that can reduce nematode populations, though the magnitude of the effect varies by cultivar and quantity of biomass produced (Grummer and Beyer, 1960; McSorley and Gallaher, 1992).

Korayem et al. (2012) noted the high susceptibility of diverse sugar beet cultivars to root-knot nematodes and consistently high galls, indicating host suitability for reproduction and posing the potential for nematode population build-up in soil.

Fallowing, as an alternative to double cropping, can reduce some populations of some nematode species, but is not always effective and requires proper fallow-season management (Govaerts et al., 2007).

The objectives of the current study were to determine damage detection threshold levels for M. incognita and B. longicaudatus on ‘M81-E’ sweet sorghum, and to monitor soil nematode population dynamics over a summer sweet sorghum production season following a varied cool-season field management, including known susceptible and non-susceptible crops. Both M. incognita and B. longicaudatus are commonly found in sandy soils in Florida throughout the year, and have wide host ranges (Crow and Brammer, 2001; McSorley and Gallaher, 1992). Additionally, detectable populations as low as 1 nematode per 100 cc of soil have the potential to

cause yield reductions and reach treatment thresholds in multiple crops commonly grown in Florida (Crow, 2006).

Materials and Methods

Sorghum Damage Thresholds

Two pot experiments were conducted in Gainesville, Florida at the University of

Florida during the summer of 2013 at an outdoor plant growth facility on raised tables to determine damage detection thresholds for M. incognita and B. longicaudatus on ‘M81-

E’ sweet sorghum, and over the winter of 2013-14 in a heated greenhouse maintained between 15-27°C for M. incognita. In each experiment, 6 replicates of 8-L pots were established in a randomized complete block design, and were filled with steam- pasteurized soil (90°C for 13 hours) (Figure 5-1) from the field site. Each experiment was inoculated with eggs of M. incognita Race 2 or J2 larvae of B. longicaudatus provided by the University of Florida Nematode Assay Lab. Eggs and juveniles were isolated from tomato (Solanum lycopersicum) plants grown in pasteurized soil under greenhouse conditions at the University of Florida Department of Entomology and

Nematology using sodium hydroxide extraction. Inoculation rates were 0.125, 0.25, 1, 2,

4, 8 and 16 eggs per cm3 soil for the summer experiments, plus a control treatment of no inoculation for each species. Rates of 32 and 64 eggs per cm3 soil were added for

M. incognita in the second experiment. M81-E seed was planted the day after inoculation and thinned to one plant per pot after one week. Sprinkler irrigation was provided on a daily basis. A blended fertilizer with micronutrients was applied at planting, and 3 and 6 weeks after planting, at a rate of 35 kg N ha-1 equivalent per application. Plants were harvested at 60 days after emergence, and height (soil level to top of stem), diameter (between the 4th and 5th nodes from the soil), and leaf and stem

fresh and dry weights were recorded for all replicates. Root dry weights were determined for 4 replicates of all densities of M. incognita for the first experiment.

Field nematode sampling procedure and analysis

Beginning with fall 2013, samples for nematode analysis were taken in the study described in Chapter 3. Samples were collected at rotation crop planting (October), at cover crop incorporation and sorghum planting (May), and at sorghum harvest (August).

Two to three days prior to collection, plots were irrigated to ensure that the top 20 cm was not dry at sampling. Soil cores were taken from the yield areas of all moderate nitrogen fertility-rotation sorghum plots and from low fertility-fallow sorghum plots to quantify nematode presence and populations at multiple sampling dates. Cores were taken according to established sampling procedures: within rows and from the rooting zones when taken in sorghum and beets, and in a random sampling pattern under rye, clover, camelina and fallow. In each sub-plot, 10 to 15 cores were collected from the top

20 cm at each sampling date and pooled to provide a single sample per plot for nematode analysis. Samples were stored on ice in the field and transferred to a 10°C cool room for storage until analyzed by the University of Florida Nematode Assay Lab.

Nematodes were extracted from a 100 cc soil subsample from each submitted sample by sucrose centrifugation, then identified and counted by trained personnel.

Statistical Analysis

Statistical analyses were performed using analysis of variance procedures in the

GLIMMIX procedure of SAS (SAS, 2009). The Gaussian (normal) conditional distribution and the identity link function were used. For the damage threshold determinations, data were analyzed with inoculation density as the fixed effect and block as the random effect. For the field experiment, data were log transformed

[log10(count+1)] and analyzed separately for the cool season and summer growth periods, with management as the fixed effect. Degrees of freedom were determined using the Kenward-Roger method. Pairwise comparisons were made using the lsmeans statement with the Tukey method. All treatment effects were considered significant at

P≤0.10.

Results and Discussion

Sorghum Detection Thresholds

Dry matter yields, plant height, and stem diameter did not vary across inoculation densities for either M. incognita or B. longicaudatus in the first experiment and did not vary across densities of M. incognita in the second experiment (Figure 5-2). Therefore, detection thresholds could not be calculated. Non-effects of nematode inoculation may be attributable to several factors, including:

 soil drying, resulting in nematode death  temporary flooding, resulting in nematodes being flushed out of the pots  high soil temperatures, thereby killing the nematode populations, for the experiments not conducted in the greenhouse

Additionally, it is possible that nematode cultures selected for use in the detection thresholds may not parasitize ‘M81-E’ sweet sorghum, or may only marginally damage and reproduce in the root system. Differential genotype susceptibility within a crop species to hosting nematode populations is well documented, as are differences in nematode ability to parasitize crops (Crow and Brammer, 2001; Korayem et al., 2012).

The lack of effect is consistent with results from field monitoring, at least at low population densities, as root-knot nematodes were present in the field at sorghum planting but did not increase during sorghum production. However, Knoll and Anderson

(2014) indicated that ‘M81-E’ is comparable to a standard susceptible corn line for

susceptibility as a host of root-knot nematodes, but may not suffer yield losses despite high levels of reproduction.

Field Monitoring

Root-knot (Meloidogyne spp.), stubby root (Trichodorus), and ring

(Mesocriconema spp.) nematodes were routinely found in plots of all management strategies at all sampling dates (Table 5-1, non-transformed means). Log transformed population differences for a genus and across cool-season management were only significant for ring nematode populations in October of 2013 and August of 2014. In

2013, ring nematode density in sorghum following camelina was 2 per 100 cm3 of soil, versus 27 per 100 cm3 following rye (P=0.099). In 2014, ring nematode density in sorghum following camelina was 20 per 100 cm3 of soil, versus 153 per 100 cm3 following beet. Observed populations were generally lower than seen by Breman et al.

(2009) in a sweet sorghum and potato production system in similar soils in North

Florida, likely due to the increased susceptibility of potato to multiple nematode pests.

These results are consistent with Grummer and Beyer (1960), who demonstrated an allelopathic effect of camelina residue on nematodes, and subsequent reductions in population. Similar results may not have been observed for root-knot nematodes in the current study due to spatial and temporal variability of nematode populations in the field.

Alternatively, preceding rotational crops are known to alter sorghum root biomass production, as well as root characteristics, and as such may have differing abilities to support nematode populations (Sainju et al., 2005). Additionally, changes in nematode populations within a management strategy across time were generally not significant.

Clover plots showed a marked increase in Trichodorus present, increasing on average by 4 nematodes per 100 cm3 of soil from October to May, while camelina plots had

lower overall populations and a slower rate of increase. Again, these results are consistent with Grummer and Beyer (1960), and with the susceptibility of clover to multiple nematode species.

The observed root-knot and stubby root nematode populations following both sorghum and rotation crops are sufficient to merit grower actions for control according to the Florida Nematode Assay Service recommendations for many common horticultural crops, including strawberry and tomato. Additionally, root-knot populations observed following sorghum were sufficient to warrant action for all field crops identified as susceptible by the Assay Service, including field and sweet corn, cotton, soybean, sugarcane, and peanut. However, ring nematode populations did not exceed the damage thresholds established for any crop except peaches. Based on these recommendations and observed populations, a sweet sorghum rotation with horticultural crops would not be advisable without routine monitoring of soil nematode populations to avoid potential yield losses.

Following three years of sorghum cropping, regardless of management strategy, no nematodes of the genera Belonolaimus (sting), Pratylenchus (lesion), Hopolaimus

(lance), Helicotylenchus (spiral), Peltamigratus, Paratrichodorus,Tylenchorhynchus

(stunt), Hemicycliophora (sheath), Hemicriconemoides (sheathoid), or Rotylenchulus

(reniform) were observed. Xiphinema spp. was ocassonally observed, but with insufficient points to analyze changes over time.

One-year nematode population changes did not vary significantly between management strategies, but sorghum following camelina resulted in lower ring nematode densities in both 2013 and 2014 relative to rye and beets respectively. Prior

field research with Brassicaceae, including Brassica napus (rapeseed), has shown significant negative effects on nematode populations following a Brassicaceae crop

(Villate et al., 2012), and similar effects here may indicate the potential for allelopathic control of ring nematode populations in a field setting following incorporation of camelina.

Conclusions

The current work is inconclusive with regard to the susceptibility of ‘M81-E’ to M. incognita and B. longicaudatus based on pot studies of damage detection thresholds.

However, field monitoring suggests that ‘M81-E’ does not result in increases of any monitored nematode population. Cool-season production of clover does increase populations of Trichodorus to potentially damaging levels for succeeding crops, but further research is needed as to potential impacts in the current system. Given the susceptibility of multiple Florida-grown crops to both root-knot and sting nematodes, and the potential for negative impacts on other crops, further monitoring and evaluation of these nematodes in sweet sorghum production systems is warranted.

Table 5-1. Field population counts of Meloidogyne spp., Mesocriconema spp., and Trichodorus observed following sorghum harvest and at cool-season crop planting in 2013 (October), at cover crop termination in 2014 (May), and following sorghum in 2014 (August). Data are means across four replicated plots (n = 4). P-values are for non-transformed data. Meloidogyne spp Mesocriconema Trichodorus Nematodes per 100 cm3 of soil Management Oct. 13 May 14 Aug. 14 Oct. 13 May 14 Aug. 14 Oct. 13 May 14 Aug. 14 Mean Clover 114† 13 9 14 3 44 0 4 5 Beet 91 28 2 22 8 165 1 1 5 Fallow 92 17 2 12 2 86 3 1 8 Camelina 97 18 2 4 0 29 2 1 15 Rye 190 22 5 36 5 140 0 1 4 Low nitrogen 122 13 3 41 10 224 1 1 3 Standard Error 12 6 1 3 2 27 1 1 2 P-value 0.1459 0.6723 0.2703 0.4284 0.6138 0.2604 0.5533 0.3404 0.2664 †Means not followed by the same letter within a column and factor are different at P ≤ 0.1.

Figure 5-1. Temperature profile of soil used in nematode pot studies during pasteurization to kill soil-borne pathogens and nematodes.

A B

C D

E Figure 5-2. M81-E mean tissue dry weights and standard deviations (n = 6 for leaf and stem, n = 4 for roots) across nematode egg inoculation densities at harvest 60 days after planting for detection threshold determination. A) Leaf dry weight when inoculated with Belonolaimus longicaudatus, B) Stem dry weight when inoculated with B. longicaudatus, C) Leaf dry weight when inoculated with Meloidogyne incognita, D) Stem dry weight when inoculated with M. incognita, E) Root dry weight when inoculated with M. incognita.

CHAPTER 6 ROOT LODGING AFFECTS BIOMASS YIELD AND CARBOHYDRATE COMPOSITION IN SWEET SORGHUM

Background

Bioenergy and bio-based products research have intensified over the last decade due to rapidly increasing fossil fuel prices, concerns over global oil supplies, and the need to mitigate carbon emissions associated with climate change. The use of sweet sorghum [Sorghum bicolor (L.) Moench] as a feedstock has been a major focus of these recent research efforts because it does not directly compete with existing food or feed crops and it is capable of rapid biomass production and soluble sugar accumulation with minimal nutrient removal (Goff et al., 2010; Rotaceli et al., 2012; Singh et al., 2012).

Thus, sweet sorghum could be used for bioethanol and energy production much like sugarcane (Saccharum spp. hybrids) in Brazil, but sweet sorghum can be produced in areas not suitable for sugarcane. Potential bioethanol production per unit land area from sweet sorghum in the southeastern U.S. is comparable to that currently seen using maize (Zea mays L.) starch in the midwestern U.S. (Erickson et al., 2011). However, in addition to variability in yield associated with cultural practices (Erickson et al., 2011,

2012; Singh et al., 2012; Teetor et al., 2011; Wortmann et al., 2010), yield loss in sweet sorghum due to lodging is a concern.

Lodging is a common problem in many crops, including sorghum, and can be caused by a number of factors such as insects and diseases, high winds, plant height, and stem diameter (Berry et al., 2004; Esechie et al., 1977; Frezzi and Teyssandier,

11980; Murray et al., 2009; Wani et al., 2000). Two types of lodging can occur in

Reprinted with permission from “Fedenko, J.R., J.E. Erickson, and M.P. Singh. 2015b. Root lodging affects biomass yield and carbohydrate composition in sweet sorghum. Ind Crop Prod. 74:933-938.”

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sorghum. Stalk lodging results from severe bending or breakage of the stem. Root lodging occurs when stalks lean or fall over as a result of the roots losing contact with the soil (Berry et al., 2003, 2004; Rooney et al., 2007), which is generally more common for sweet sorghum in the southeastern U.S.A. where high winds and rainfall from tropical weather events often occur during the growing season.

Lodging of a crop can cause multiple issues, ranging from decreased yield to total crop loss, and in crops harvested for biomass, may significantly reduce harvestable material due to equipment limitations. Prior research has considered the effects of lodging on multiple crops, including maize, wheat (Triticum aestivum L.), and sorghum.

Lodging research has typically been accomplished by imposition of artificial lodging, or has been rated and noted as a potential causative factor when naturally occurring and not under direct investigation (Teetor et al., 2011; Weibel and Pendleton, 1964;

Wortmann et al., 2010). In wheat, Weibel and Pendleton (1964) showed that root lodging earlier in the season increased yield losses and decreased grain weight relative to lodging later in the season, with up to a 31% yield loss when lodging occurred at heading. Hume and Campbell (1972) reported that root lodging in maize resulted in decreased yield, but noted varying effects on total soluble solids in stems of lodged plants. Larson and Maranville (1977) examined the effects of artificial root lodging in grain sorghum. Root lodging at anthesis reduced grain yield significantly (15 to 21%), while root lodging at soft and hard dough stages resulted in variable, but generally lower grain yield effects across years. As a result of combined root and stalk lodging, Rutto et al. (2013) reported grain yield declines from 25 to 80% across three sweet sorghum cultivars when compared with prior year yields.

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Sweet sorghum cultivars intended for use as bioenergy crops may be particularly susceptible to both root and stalk lodging given their rapid growth and height, which are associated with increased susceptibility to both root and stalk lodging (Esechie et al.,

1977; Monk et al., 1984; Smith et al., 1987b). Relations between total soluble solids and soluble carbohydrates in the stem with both root and stalk lodging are unclear. Putnam et al. (1991) reported increased susceptibility to lodging among sweet sorghum cultivars with increasing levels of stem soluble solids and carbohydrates, while Esechie et al.

(1977) reported decreasing susceptibility to lodging with increased soluble solids and carbohydrate concentrations in grain soghums. Broadhead (1973) demonstrated that panicle removal could reduce sorghum root lodging, though the degree of reduction was not quantified. Wani et al. (2000) proposed that remobilization of stem material to grain may contribute to increased lodging susceptibility. Current approaches to mitigate root and stalk lodging include breeding for traits that may reduce susceptibility to lodging, and modifying cultural practices, such as avoiding excessive seeding rates and optimizing planting windows (Teetor et al., 2011).

Although root lodging in sweet sorghum is well known, there is a dearth of published data quantifying the effects of root lodging on sweet sorghum yield and composition. The objectives of the current study were therefore to quantify biomass and carbohydrate yield, partitioning, and recovery of sweet sorghum grown in the field.

Materials and Methods

Experimental Site and Design

During the 2012 and 2013 growing seasons, sweet sorghum cultivars and hybrids were established from seed in the field at the University of Florida Plant Science

Research and Education Unit at Citra, Florida (29°23’60’’ N, 82°12’0’’ W) for relatively

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large-scale yield trials. The soil at the experimental site (Table 6-1) was a Hague sand,

2 to 5 percent slopes (loamy, siliceous, semiactive, hyperthermic Arenic Hapludalfs) in

2012, and an Arredondo sand, 0 to 5 percent slopes (loamy, siliceous, semiactive, hyperthermic Grossarenic Paleudults) in 2013. In 2012, weather induced root lodging occurred in a commercial sweet x sweet sorghum hybrid (EJ 7281; Blade® Energy

Crops, Thousand Oaks, CA) that was established on 0.76 m row spacings in a field 70 rows wide and 83 m long. In 2013, root lodging was not seen in the commercial hybrid, but weather induced root lodging occurred in ‘M-81E’ sweet sorghum (Broadhead et al.,

1981) that was established on 0.76 m row spacings in a field 43 rows wide and 64 m long. Limited information on the susceptibility of these particular sweet sorghums to root lodging is available, but sweet sorghums in general are susceptible to root lodging due to their height (Rooney et al., 2007). Weather data were collected from a Florida

Automated Weather Network (FAWN) station located <1 km from the field.

Management Practices

In 2012, planting occurred on 5 April using a precision vacuum planter (Monosem

Inc., Edwardsville, KS) with a seeding rate of 118,000 seeds ha-1. In 2013, planting was on 15 May with the vacuum planter at a seeding rate of 110,000 seeds ha-1. At planting in both years, chlorpyrifos (O,O-Diethyl O-3,5,6-trichloropyridin-2-yl phosphorothioate) was applied at 10.3 kg ha-1 to control insects, and metolachlor (S-2-Chloro-N-(2-ethyl-6- methyl-phenyl)-N-(1-methoxypropan-2-yl) acetamide) and atrazine (1-Chloro-3- ethylamino-5-isopropylamino-2,4,6-triazine) were sprayed at 1.2 and 2.3 L ha-1 respectively to control weeds.. A liquid fertilizer (11-37-0) was injected at planting at a

-1 -1 -1 rate of 185 kg ha to supply 20 kg ha N and 68 kg ha P2O5. Supplemental nitrogen

(as ammonium nitrate) and potassium (as muriate of potash) were supplied in two spilt 103

side dress applications around 4 and 7 weeks after planting to a season total of 135 kg

-1 ha each N and K2O in both years. Irrigation was only applied using overhead linear move system when rainfall was insufficient to provide about 25 mm of water per week.

In 2012, 29 mm of irrigation water was applied in April, 90 mm in May, and 11 mm in

June. In 2013, 63 mm of irrigation water was applied in May, 15 mm in June, and 5 mm in July.

In both 2012 and 2013, inclement weather events resulted in large areas of complete root lodging in each of the sweet sorghum fields at 81 and 86 days after planting, respectively. Immediately after lodging occurred in each field, plots were assigned to completely (100%) lodged and non-lodged (0% lodged) areas based on visual assessment of lodging (Fig. 6-1) in a completely randomized design. Each plot was 5 rows by 6 m in both years. In 2012 three replicates of each treatment (lodged and non-lodged) were established, and 4 replicates of each treatment were established in

2013. No stem lodging was observed in any of the plots in either year.

Harvest Procedures

At late soft dough to early hard dough stage, a 4-m section from the inner row of each plot was harvested using a gasoline powered trimmer (Echo, Inc., Lake Zurich, IL).

The 4-m section was immediately weighed in the field to provide estimates of fresh yield, and a 3-stalk subsample was collected, weighed fresh in the field, and partitioned into leaves (excluding leaf sheaths), stems, and panicles (including peduncle). Stem diameter (average of 2 perpendicular measurements) was also measured on the fifth internode from the base of the plant of each of the stems in the subsample. These subsamples were dried at 60°C until a constant dry weight was achieved to determine dry matter concentration and partitioning of dry biomass to leaf, stem, and panicle. 104

Dried biomass samples were run through a commercial chipper (DEK Chipper Shredder

Model CH1; GXI Outdoor Power, Clayton, NC), subsampled, and then ground with a

Thomas-Wiley mill (Thomas Scientific, Swedesboro, NJ) to pass through a 1-mm screen. A second subsample of 7 stalks was collected and the leaves and panicles were removed, and stems were then pressed using a roller mill to extract juice. Juice was weighed and allowed to equilibrate to room temperature, and then Brix was measured using a portable refractometer (Atago PAL-1, Atago U.S.A., Inc., Bellevue,

WA).

Also at the time of final harvest in 2012, the remainders of the lodged and non- lodged areas of the field were harvested separately to a stubble height of 5 cm using a commercial flail harvester (Hiniker 5700). Harvested material was collected in weigh wagons and weighed, and harvested areas for each lodged and non-lodged areas were calculated separately in order to obtain an estimate of recoverable biomass using a commercial flail harvester.

Carbohydrate Analyses

Water soluble carbohydrate (WSC) and starch concentrations in ground leaf, stem, and panicle tissue samples from the second year of the study (2013) were analyzed by a commercial lab (Dairy One Forage Lab, Ithaca, NY) according to established procedures. Briefly, samples were pre-extracted to remove free sugars by incubation in water at 40°C for 1 hour and filtered. Filtrate was acid hydrolyzed with sulfuric acid, reacted with potassium ferricyanide, and the colorimetric reaction analyzed for WSC using a Thermo Scientific Genesys 10S Vis Spectrophotometer. The residues from incubation were thermally solubilized and then incubated with glucoamylase

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enzyme to produce glucose and analyzed using a YSI 2700 SELECT Biochemistry

Analyzer (YSI Incorporated Life Sciences, Yellow Springs, OH).

Water soluble carbohydrates and starch in stem, leaf and panicles were then calculated by multiplying the concentrations by their respective dry biomass yields. Total nonstructural carbohydrates (TNC) for each tissue type were calculated by adding WSC and starch for each tissue. Whole plant TNC was then calculated as the sum of TNC for each tissue type.

Statistical Analyses

Statistical analyses were performed in Microsoft Excel 2007 (Microsoft Corp.,

Redmond, WA, USA) using the Student’s t-test with significance at P < 0.05 to compare root-lodged and non-lodged treatments. Data were analyzed separately by year.

Machine harvested fresh biomass yield were analyzed only for 2012, and seed weight, seed number, WSC, and starch only for 2013. Residuals were checked for normality numerically using the Shapiro-Wilk test.

Results

Weather Data

During 2012, in the 48 hours preceding lodging, 270 mm of rain and maximum wind speeds measured at 15-minute intervals averaged 24 kph, whereas in the preceding 3 weeks a total of 75 mm of rain and average maximum wind speeds of 14 kph were recorded. This weather event resulted in large areas of severe root lodging of the sweet sorghum at 81 days after planting (Fig. 6-1).

Biomass Yield and Partitioning

Total fresh biomass yields were not different between lodged and non-lodged sweet sorghum and averaged 75.0 Mg ha-1 (Table 6-2). However, lodged sweet

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sorghum showed a trend towards lower dry matter concentration, resulting in 19% lower dry biomass yield compared to non-lodged sweet sorghum during the 2012 growing season. The reduction in total dry matter yield in 2012 was due in large part to a reduction in panicle yield, which was reduced by 50% as a result of lodging. Leaf and stem dry biomass were not affected by lodging. Therefore, lower partitioning of total dry biomass to panicles was seen in lodged sweet sorghum at final harvest (Table 6-3).

Stalk diameter at final harvest was 2 mm greater (P = 0.01) in lodged compared to non- lodged stalks. Additionally, biomass recovery in 2012 of hand-harvested plots and plots harvested with a commercial flail harvester in both root-lodged and non-lodged sweet sorghum was compared. Total fresh biomass recovered by a commercial flail harvester in root-lodged areas of the field (44.3 Mg ha-1) was 40% lower when compared to hand- harvested biomass (74.0 Mg ha-1). In contrast, machine- and hand-harvested fresh biomass yields were within 10% in non-lodged areas of the field.

During the 2013 growing season, lodging occurred at 86 days after planting after a brief but intense weather event with 30 mm of rain falling in less than 30 minutes accompanied by wind speeds in excess of 39 kph. In the preceding 3 weeks, a total of

136 mm of rain and average maximum wind speeds of 12 kph were recorded.

Similar to 2012, total fresh biomass yields in 2013 were not different between lodged and non-lodged sweet sorghum and averaged 77.2 Mg ha-1, but no difference in total dry matter yield was observed (Table 6-2). Dry matter concentration was reduced by 14% in lodged compared to non-lodged sorghum. A 48% reduction in panicle weight due to lodging was seen in 2013 that again was associated with lower partitioning of total dry biomass to panicles at final harvest, while no differences in leaf or stem dry

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matter yields were observed as a result of lodging (Table 6-2, Table 6-3). Total number of seeds per panicle and average seed weight were both lower in lodged plots (Table 6-

2). Stalk diameter was unchanged by lodging in 2013 (16.4 mm).

Brix and Carbohydrates

Despite similar stem fresh biomass yields at final harvest in non-lodged and root- lodged sweet sorghum during 2012, lodging reduced juice Brix at final harvest by 32%, from 147 mg g-1 in non-lodged sorghum to 100 mg g-1 in lodged sorghum (P = 0.01). In

2013, lodging reduced juice Brix at final harvest by 17%, from 114 mg g-1 in non-lodged plots to 95 mg g-1 in lodged plots (P < 0.01).

Despite differences in juice Brix in 2013, neither WSC nor starch as a fraction of stem biomass differed between the lodged and non-lodged sorghum (Table 6-4). Water soluble carbohydrates in leaf tissue were lower in lodged than non-lodged plants. Starch concentration in leaf tissue did not differ between lodged and non-lodged sorghum.

Starch concentrations in lodged panicles at harvest were lower than in non-lodged sorghum, however WSC did not change with lodging (Table 6-4).

There was a trend (P = 0.10) towards reduced WSC yield (~17%) in stem tissue at harvest due to root lodging (Table 6-5). A similar trend in reduced leaf TNC was observed as a result of lodging. The most pronounced effect of root lodging, however, was on TNC yield from panicles, as a 53% decline in TNC yield was observed, due largely to reduced yield of starch. Overall, whole plant TNC showed a reduction of 27% as a result of lodging, driven primarily by panicle starch and the trend for reduced WSC in the stem (Table 6-5).

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Discussion

Potentially recoverable fresh biomass yields did not differ between lodged and non-lodged sorghum in either year of the study. However, commercially harvestable yields of lodged fresh sorghum biomass may be significantly reduced due to plant biomass left in the field by harvesting equipment designed primarily for erect plant biomass, or may incur increased harvest costs (Bischoff et al., 2001; Broadhead, 1973;

Turhollow et al., 2010). In the present study, mechanized harvest of fresh biomass using a commercial flail chopper left residual biomass in the field, which resulted in approximately 40% reduction in fresh biomass harvested compared to hand-harvested plots. Additionally, lower juice Brix and higher plant moisture concentration seen in lodged sorghum at harvest would likely increase transportation and processing costs as more water and less sugar will be transported per tonne of green stalk (Turhollow et al.,

2010).

Although root lodging did not seem to affect fresh biomass in the field, dry biomass yields were greater in non-lodged than lodged sorghum in 2012, but not in

2013, due to differences in dry matter concentration (Table 6-2). Larson and Maranville

(1977) observed similar whole plant yield reductions in grain sorghum that experienced root lodging at heading, with lodging losses ranging from 15 to 21% (from 6422 to 5085 and 6234 to 5311 kg ha-1 in control versus lodged treatments), as compared with 19% observed during 2012 in the current study. Biomass partitioning was affected by lodging in both years, primarily due to reduced grain production, which has also been observed for root-lodged maize (Hume and Campbell, 1972). The reduction in the fraction of total plant biomass allocated to panicle formation, and effects on seed production and quality was quantified in 2013, as seed size and number were both significantly reduced due to 109

lodging. These results are consistent with Weibel and Pendleton (1964), where lodging in wheat at heading caused a 22% reduction in 100-seed weight (from 2.7 to 2.1 g), while lodging observed in sorghum in this study during 2013 reduced 100-seed weight by 19% (Table 6-2).

In addition to grain production, sweet sorghum can accumulate a significant fraction of assimilated carbon as soluble carbohydrates in the stalk of the plant (Carpita and McCann, 2008; Murray et al., 2009). Root lodging reduced juice Brix (a measure of soluble solids) at harvest by 32 and 17% for the 2012 and 2013 seasons, respectively.

Laboratory analyses of stem WSC did not differ significantly between treatments during

2013. This apparent discrepancy between juice Brix and stem WSC could potentially be explained by the higher within treatment coefficient of variation seen in stem WSC

(approx. 11%) compared to juice Brix (approx. 4%) measurements and/or lodging may have affected the fraction of WSC in juice soluble solids. There was a trend (P = 0.10) towards a 17% reduction (approx. 800 kg ha-1) in stem WSC in 2013, which was comparatively less than the 53% reduction in panicle starch, but of similar absolute magnitude (loss of 863 kg ha-1 grain starch). Thus, in 2013, lodging caused a 27% reduction in potentially recoverable whole plant TNC (Table 6-5). With the additive effect of potential fresh biomass loss due to mechanized harvest as described above, losses in recoverable TNC due to lodging could be even greater.

Taken together, the results of the present study indicated that lodging around the time of flowering had minimal effect on leaf and stem biomass production, but significantly reduced grain production and to a lesser extent accumulation of soluble sugars in the stem. The mechanism for reduced grain yield and soluble stem sugars is

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not clear from the present study, but the overall decline of potentially recoverable TNC with root lodging is consistent with a reduction in source photosynthate compared to non-lodged plots. Immediately after lodging, light interception by the canopy was likely reduced and/or a change in canopy architecture led to increased self-shading, resulting in decreased carbon assimilation (Setter et al., 1997). Over time lodged plants began to right themselves and may have been able to recover some of this lost photosynthetic capacity (Berry and Spink, 2012), but the loss in photosynthate strongly limited grain production and did not allow for any further accumulation of soluble sugars in the stem from anthesis to final harvest, and may have actually resulted in decreased stem sugars that may have been translocated from the stem to the panicle to support grain yield

(Erickson et al., 2012; Hume and Campbell, 1972).

Thus, the present study confirmed numerous prior studies that reported significant losses in grain yield associated with lodging (Larson and Maranville, 1977;

Rutto et al., 2013; Setter et al., 1997;Weibel and Pendleton, 1964), but this study is one of few, if any, that reports results of weather-induced root lodging on soluble stem sugars in sweet sorghum, a crop that has received much attention for use as an industrial crop (Rooney et al., 2007; Takaki et al., 2015; Teetor et al., 2011). While lodging did tend to reduce recoverable nonstructural carbohydrates in the stem, these losses were relatively modest compared to relative grain yield losses, leaving producers interested in soluble stem sugars with some flexibility to deal with lodging. If severe lodging is expected based on weather conditions, producers may choose to harvest early to reduce losses due to mechanized harvest of lodged material. If root lodging does occur around anthesis, producers may decide to wait to harvest as there does not

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appear to be a large reduction in soluble stem sugars compared to anthesis, although this may vary with cultivar, and there may be greater stalk recovery as the stalks re-right themselves over time following lodging. Although root lodging may not cause total loss of recoverable TNC in sweet sorghum, it will likely reduce grain yield, soluble stem sugars, and recoverable stalk biomass, therefore continued research on management practices that contribute to reduced root lodging in sweet sorghum is needed along with breeding of improved cultivars resistant to root lodging.

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Table 6-1. Planting, lodging, and harvest dates along with stem harvest density, soil pH, soil bulk density, soil texture, soil organic matter (SOM), and available P, K, Ca, and Mg using the Mehlich-1 extractant in the upper 15 cm for sorghum plots in 2012 and 2013.

Unit 2012 2013

Planting date April 5 May 15 Lodging date June 25 (81 DAP) August 9 (86 DAP) Harvest date August 1 (118 DAP) September 3 (111 DAP) Harvest density 105,000 plants ha-1 187,000 plants ha-1 Soil analysis pH 6.2 6.6 Bulk Density g cm-3 1.54 1.48 Sand g kg-1 9.84 9.36 Silt g kg-1 0.04 0.37 Clay g kg-1 0.12 0.27 SOM g kg-1 0.049 0.122 P mg kg-1 110 151 K mg kg-1 39 44 Ca mg kg-1 726 1030 Mg mg kg-1 49 66

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Table 6-2. Effect of root lodging at 81 and 86 days after planting during 2012 and 2013, respectively, on sweet sorghum fresh biomass yield, whole plant dry matter concentration, leaf, stem, panicle, and whole plant dry biomass yields, 100 seed weight, and the number of seeds per panicle at final harvest. Data represent treatment mean values (n = 3 in 2012, n = 4 in 2013). Treatment Fresh biomass Dry Whole plant Leaf dry Stem dry Panicle dry 100 seed Seeds per yield matter dry biomass biomass biomass biomass weight panicle Mg ha-1 g kg-1 Mg ha-1 g No. 2012

Non-lodged 75.9 306 23.2a 3.1 15.0 5.2a -b - Lodged 74.0 255 18.8b 2.7 13.4 2.6b - - P-value 0.19 0.07a <0.01 0.18 0.36 <0.01 - - 2013

Non-lodged 73.8 256a 18.9 2.2 14.2 2.5a 1.22a 3442a Lodged 80.6 221b 16.7 2.1 13.3 1.3b 0.99b 1772b P-value 0.54 <0.01 0.42 0.94 0.68 <0.01 <0.01 0.01 a P-values from 2-tailed t-test comparing lodged and non-lodged data within columns and within year. b No data collected during 2012.

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Table 6-3. Effect of root lodging on partitioning of dry biomass to leaf, stem and panicle for sweet sorghum at final harvest during the 2012 and 2013 growing seasons. Data represent treatment mean values (n = 3 in 2012, n = 4 in 2013). Treatment Leaf Stem Panicle g kg-1 2012 Non-lodged 134 641 224a Lodged 145 715 139b P-value 0.42a 0.07 0.01 2013 Non-lodged 116 749b 135a Lodged 129 792a 79b P-value 0.28 <0.01 <0.01 a P-values from 2-tailed t-test comparing lodged and non-lodged data within columns and within year.

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Table 6-4. Effect of root lodging on water soluble carbohydrate (WSC) and starch concentrations for leaf, stem, and panicle tissues of sweet sorghum at final harvest during the 2013 growing season, and initial WSC and starch concentrations prior to lodging at anthesis. Data represent treatment mean values (n = 4). Treatment Leaf Stem Panicle WSC Starch WSC Starch WSC Starch mg g-1 At anthesisa 81a 9 304 4 - - Non-lodged 68b 2 330 6 23 638a Lodged 55c 7 298 13 25 600b p-value 0.03b 0.19 0.24 0.48 0.62 <0.01 a Leaf and stem samples were collected at anthesis (78 DAP, 8 days before lodging) from 4 subsampled plots for carbohydrate and starch analysis. No panicles were present at sampling. b P-values from 2-tailed t-test comparing lodged and non-lodged sorghum at final harvest within column.

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Table 6-5. Effect of root lodging on yields of water soluble carbohydrates (WSC), starch, and total nonstructural carbohydrates (TNC, equal to WSC and starch combined) for leaf, stem, and panicle tissues, along with whole plant TNC of sweet sorghum at final harvest during the 2013 growing season. Data represent treatment mean values (n = 4). Treatment Leaf Stem Panicle Whole plant WSC Starch TNC WSC Starch TNC WSC Starch TNC TNC kg ha-1 Non-lodged 148a 4 152 4632 108 4740 57 1622a 1678a 6571a Lodged 113b 13 126 3833 62 3896 31 759b 790b 4812b p-value 0.03a 0.15 0.10 0.10 0.24 0.10 0.08 <0.01 <0.01 0.01 a P-values from 2-tailed t-test comparing lodged and non-lodged data within column.

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Figure 6-1. Root-lodged (foreground) and non-lodged (background) sweet sorghum following heavy rainfall and high wind speeds.

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CHAPTER 7 CROPPING SYSTEMS AND IMPLICATIONS

Cool-season legume rotations show promise for incorporation into sweet sorghum production systems in the Southeastern US, particularly in conjunction with moderate sweet sorghum nitrogen fertility. This system demonstrated increased partitioning to sorghum grain head biomass, suggesting a greater production of soluble carbohydrates over the season which was allocated to grain fill. Soil-available nitrogen was also highest in these plots immediately following incorporation, suggesting greater potential nitrogen availability for crop growth, and supported by tissue nitrogen removal rates from the sorghum crops. However, further research is necessary to determine if these factors may also increase total soluble carbohydrate and/or dry biomass yields.

Additional research to optimize planting and harvest timing (i.e., decreasing the interval between rotational crop incorporation and sorghum planting, and harvesting prior to late soft dough) may also amplify the beneficial effects of legume rotational cropping.

Altering harvest timing may also contribute to decreased potential for lodging, and the potential to mitigate yield loss when lodging does occur. Alternative cool-season rotations generally did not demonstrate substantial benefit; however, this lack of benefit is generally attributable to poor stand establishment or growth. Selection of different cultivars or crops may increase the effectiveness of cool-season rotations, as would increases in management intensity. In particular, sugar beets have shown promise as a cool-season crop in Florida in other research, but require higher input levels than used here, and may increase plant-parasitic nematode populations.

Low-input sorghum production is generally not viable, from either a grower or sustainability perspective. Consistent linear declines in both total dry biomass yield and

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tissue nitrogen concentration of sweet sorghum indicate that low nitrogen levels are unsustainable. These effects may potentially be mitigated by the use of incorporated high-nitrogen rotational crops, such as clover, but further research on management practices would be necessary to demonstrate these benefits. Lodging tended to reduce yield and soluble carbohydrates, and reduced grain yield by 50% when it did occur, but was generally less severe than observed in dedicated grain crops, such as grain sorghum.

The current research has shown that low-input systems are not sustainable for sweet sorghum production, but that cool-season rotations have the potential to increase sweet sorghum nitrogen concentrations and alter biomass partitioning. Additionally, a lack of buildup in plant-parasitic nematode populations has been demonstrated, even with a susceptible rotation, reducing concerns regarding potential crop rotations. Further research is necessary to definitively determine the potential yield boost and contributions of cool-rotations, but these results are promising indicators of long-term benefits which may be realized with additional work.

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APPENDIX A SUNFLOWER YIELD AND COMPOSITION

Summary

During the summer of 2013 a sunflower genotype evaluation trial was conducted at the University of Florida Center for Bioenergy and Sustainable Technology

Laboratory Complex in Gainesville, FL, USA. Ten varieties of sunflower were selected from the US National Plant Germplasm System and US Germplasm Resources

Information Network based on diversity of growth habit and established in a randomized complete block design with four replicates. Each variety was planted as a single 3 m row per plot, with 0.76 m between rows and 1.8 m between blocks. Seeds were germinated in a greenhouse setting in 0.4 L pots with potting mix, and transplanted to the field 21 days after emergence. Weeds were removed mechanically, and plots were fertilized with15-0-15 at a rate of 100 kg N ha-1 three weeks after transplanting. Drip irrigation was provided as needed to meet crop demand. Plots were hand-harvested at maturity by cutting the stalk at ground level, and separating the seed head. Stalks

(stems plus leaves) were weighed fresh, dried at 60°C for two weeks, and re-weighed for dry matter. Heads were threshed by hand to remove seeds that were subsequently weighed and analyzed for oil by NMR according to the procedure described in Chapter

3, with a sunflower oil standard.

Seed yield was highest for PI 650590 at 327 kg ha-1, and was lower and not significantly different among the remaining lines (Figure A-1). Seed oil concentrations were lowest in PI 650592, followed by PI 650567 and PI 650573 (Figure A-2). PI

650567 had more flowers per stalk than all other varieties (Table A-1). Plant height and seed face diameter did not vary significantly among cultivars (Table A-1).

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Due to difficulties with stand establishment, one block of the study was lost after transplanting, and populations were reduced below agronomic optimums in other plots.

These difficulties, coupled with plant loss due to bird damage and disease, resulted in lower than expected yields (Robertson and Green, 1981). Sunflower seed oil concentrations are also lower than reported by Chellemi et al. (2009), though for PI

650567 this may be attributable to the high number of flowers per stalk.

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Figure A-1. Sunflower stalk (leaf plus stem) and seed dry biomass yields with standard deviations for 10 varieties of sunflower grown in North Florida (n=3).

Figure A-2. Sunflower seed oil concentration (g kg-1) as determined by NMR for 10 varieties of sunflower grown in North Central Florida in summer 2013 (n=3). Error bars are one standard deviation.

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Table A-1. Sunflower stalk height, average number of heads per stalk, and average face diameter for 10 varieties of sunflower grown in North Florida (n=3).

Height Heads per stalk Face Diameter Variety cm # cm PI 596744 105 12b† 6.4 PI 599755 121 6b 6.0 PI 599775 127^ 4b 10.4 PI 599778 111 2b 7.2 PI 599979 97 4b 9.0 PI 650567 103 36a 8.5 PI 650573 102 4b 7.7 PI 650580 125 4b 13.5 PI 650590 140 6b 14.3 PI 650592 124 5b 7.0 P-value 0.1308 <.0001 0.7905 †Means not followed by the same letter within a column and factor are different at P ≤ 0.05. ^Data could not be analyzed statistically (n=1), but is included for comparison.

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BIOGRAPHICAL SKETCH

Jeffrey Fedenko is a native Floridian, and attended the University of Florida as an undergraduate, where he received his B.S. degrees in Biology and Psychology in 2009.

He started as an M.S. student in Agronomy Department at the University of Florida in

2009 with Dr. John Erickson, where he worked annual and perennial bioenergy feedstocks. He graduated in 2011 and entered a Ph.D program with Dr. John Erickson, working on crop rotations for sweet sorghum production systems. During this time, he also worked on several other projects, including lodging and carbohydrate partitioning in sweet sorghum. He completed his doctoral degree program in 2015.

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