Three-Year Soybean-Wheat-Corn Rotation Benefits on Soybean Production, Soil Health

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Three-year soybean-wheat-corn rotation benefits on soybean production, soil health

and soil bacterial community are site and year dependent.

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science

in the Graduate School of The Ohio State University

Daowen Huo

Graduate Program in Plant Pathology

The Ohio State University

2020

Master’s Examination Committee:

Assistant Professor Maria Soledad Benitez Ponce, Advisor

Associate Professor Laura Elizabeth Lindsey

Assistant Professor Ye Xia

Copyrighted by

Daowen Huo

2020

Abstract

Higher crop rotational diversity can improve crop productivity, soil health and soil microbial diversity. This research hypothesized that three-year (3-yr) rotation of soybean- corn-wheat would have higher soybean productivity, better soil health and more diverse soil bacterial community compared to two-year (2-yr) soybean-corn rotation. A rotation experiment was established in 2013 at two research sites in Ohio. Soybean seedling establishment and biomass, crop yield and soil were collected in 2018 and 2019. Higher seedling stand and biomass, and soybean yield were observed in the 3-yr rotation but the results were not consistent across all site-year combinations. Soil organic matter was higher in the 3-yr rotation at three out of four site-years. Similarly, higher soil carbon, nitrogen and active carbon was detected in the 3-yr rotation at one site-year. The bacterial community at NWARS and WARS also different. However, at each site, the diversity of soil bacteria, sampled at soybean seedling stage, did not differ between the 2-yr and 3-yr rotation. Seven major phyla and nine core bacterial sequence variants were found in samples from all treatments. Nevertheless, compared to the 2-yr rotation, the 3-yr rotation had a unique set of six taxa, absent in the 2-yr rotation samples, and higher abundance of

Pseudomonas sequence variants and lower abundance of Ralstonia sequence variants.

Most of the 3-yr rotational benefits were detected on site-year combinations where environment and weather conditions were unfavorable to soybean growth, such as poorly drained soil, high precipitation, and fewer growing degree days. Hence, under unfavorable conditions, the 3-yr rotation of soybean-corn-wheat is recommended for soybean and soil benefits.

Acknowledgments

My sincerest thanks to my Academic Advisor Dr. Soledad Benitez for advising me throughout my master’s degree program at The Ohio State University. She instructed me very patiently in my research and study, and also inspired and supported me to find my future. I would also like to thank my committee members Dr. Laura Lindsey and Dr. Ye

Xia for all of their guidance. I am especially thankful for Dr. Laura Lindsey’s assistance with the field experiment. Thank you to the CFAES SEEDS grant and the Plant

Pathology department for funding this research. I am also very thankful for all those who assisted with sample collection including farm managers Matt Davis and Joe Davlin, and my lab members Leslie Taylor, Wattanaporn Teerasan, Nedas Matulionis, Emmily Moses and Ana Vazquez.

Thank you to Dr. Steven Culman and his lab for helping me set up soil active carbon experiment. Also, thank you to Dr. Michael Sovic’s bioinformatic workshops and

Matthew Willman’s help in bioinformatics. Thank you to OARDC librarian Laura Miller and OSU Writing Center for checking my thesis. Also, I would like to thank Dr. Chris

Taylor for providing me the opportunity to attend the Icorps@Ohio, which open my ideas about the future career. I want to thank many of my peers and friends who have helped

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me during this journey: Ana Vazquez, Ranjana Rawal, Carlos Saint-Preux, Marlia

Bosques Martinez, Madeline Horvat, Helen Kanyagha, Nghi Nguyen, Biu Khwannarin,

Panupon Twilprawat, Bode Su, Mingde Liu and Qin Guo. Thank you to Bob James and

Kenneth Nanes for cheering me up all the time in Selby hall. Finally, I would like to thank my parents, brother and all my friends in China for supporting me along the way.

Vita

2011 ...... Gushi Ciji High School

2014...... B.S. Agronomy: Plant Protection, China

Agricultural University

2018 to present...... Graduate Research Associate, Department of Plant Pathology, The Ohio State University

Fields of Study

Major Field: Plant Pathology

Table of Contents

Abstract ...... i Acknowledgments...... iii Vita ...... v List of Tables ...... viii List of Figures ...... xi Chapter 1: Literature review ...... 1 Introduction ...... 1 Soybean ...... 2 Crop diversity loss in U.S. land ...... 5 Crop rotation ...... 6 Soil health ...... 10 Soil microbial communities ...... 12 Hypotheses and objectives ...... 16 Figures...... 18 List of References ...... 21 Chapter 2: The effects of soybean-wheat-corn rotation on soybean production and soil health compared to soybean-corn crop rotation ...... 27 Abstract ...... 27 Introduction ...... 28 Materials and methods ...... 34 Results ...... 39

Discussion ...... 41 Tables ...... 47 Figures...... 55 List of References ...... 57 Chapter 3: How soybean-wheat-corn rotation changes the diversity and composition of soil bacterial community compared to soybean-corn rotation...... 61 Abstract ...... 61 Introduction ...... 62 Methods and materials ...... 67 Results ...... 81 Discussion ...... 85 Tables ...... 93 Figures...... 101 List of References ...... 111

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List of Tables

Table 2.1. Geographic descriptions, soil types and field management at NWARS and

WARS. NWARS had much deeper and poorly drained soil compared to WARS...... 47

Table 2.2. Rotation sequences and crops per year of the experiment since 2013

(Source, Laura Lindsey). Blue shades indicate the sampled soybeans in 2-yr and 3-yr rotation treatments...... 48

Table 2.3. Experimental design in 2018 at NWARS. The randomized complete block design had four blocks as well as four replications, and each block had 3 plots for 3-yr rotation and 2 plots for 2-yr rotation...... 49

Table 2.4. Soybean varieties used in this study and field management characteristics.

NWARS had late soybean planting date in both years compared to WARS...... 50

Table 2.5. The precipitation and temperature in each experimental station during the studied period. WARS had higher precipitation in 2018, and NWARS had lower growing degree days compared to other site-years...... 51

Table 2.6. Sample coefficient variation1 of POXC values were compared at < 2 mm and < 0.6 mm from NWARS and WARS soil samples. < 2 mm soil sample had lower coefficient variation compared to < 0.6 mm soil sample in POXC test...... 52

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Table 2.7. Soybean seedling establishment and biomass in 2018 and 2019 at NWARS and WARS. 3-yr had higher stand (/meter), root and shoot biomass only in one site-year.

...... 53

Table 3.1. Summary of sequence reads per sample at each processing step for 2018 and 2019 samples ...... 93

Table 3.2. Number of ASV1 at each step in the data analysis pipeline...... 94

Table 3.3. Comparison of diversity estimates within rotation treatments based on alpha diversity indices at NWARS and WARS in 2018 and 2019. No alpha diversity difference was detected between 2-yr and 3-yr rotations’ bacterial communities...... 95

Table 3.4. The relative abundance (%) of each phyla at NWARS and WARS in 2018 and 2019. Seven major phyla were presented in all rotation treatment samples, and the most abundant phylum is Proteobacteria...... 96

Table 3.5. The taxonomy of nine core ASVs in all analyzed samples at NWARS and

WARS in 2018 and 2019. These core ASVs were reported in previous studies that play important roles in soil nitrogen and carbon cycling, thus they are important core ASVs for soybean growth...... 97

Table 3.6. The taxonomy of six unique core ASV present in the 3-yr rotation samples but not in the 2-yr rotation samples. Among these six genera, previous studies showed that Skermanella is potential pest biocontrol bacteria, Nocardioides is able to degrade ix

herbicide, and Gemmatimonadaceae had higher abundance in constant and low moisture soil...... 98

Table 3.7. 17 differentially abundant genera between 2-yr rotation and 3-yr rotation at

NWARS in 2018 and 20191. Seven genera had higher abundance in 3-yr rotation; ten genera had lower abundance in 3-yr rotation compared to 2-yr rotation...... 99

List of Figures

Figure 1.1. Global and U.S. soybean exports from 1980 to 2015. The soybean production in U.S. has been increased greatly in last several decades, and U.S. became the important country for global soybean exports...... 18

Figure 1.2. Soybean growing region and production estimates in the north central

U.S. in 2018. The major soybean growing area in U.S. is north central area, which includes parts or all of Ohio, Indiana, Illinois, Iowa, Minnesota, Missouri, Nebraska,

South Dakota, and Kentucky...... 19

Figure 1.3. The average soybean yield in the U.S. from 1989-2019. The soybean yield has been increased in last two decades due to the contribution of improved agronomics, precision agriculture, integrated disease management...... 20

WARS: Western Agricultural Research Station; 2-yr: Corn-Soybean rotation; 3-yr:

Soybean-Wheat- Corn rotation ...... 55

Figure 2.2. Soybean yield at NWARS and WARS in 2018 and 20191. At NWARS, in

2018, 3-yr rotation had higher soybean yield compared to 2-yr rotation (P<0.10). At other xi

site-years, the yield was same in both rotation treatments. NWARS: Northwest

Agricultural Research Station; WARS: Western Agricultural Research Station; 2-yr:

Corn-Soybean rotation; 3-yr: Soybean-Wheat- Corn rotation; P value was calculated by one-way ANOVA and P was marked at NWARS, in 2018 which shown a significant increase in 3-yr rotation soybean yield...... 56

Figure 3.1. The bioinformatics workflow used in this study. The workflow begins with raw sequence reads and continues through pre-processing, assigning taxonomy, analyzing the diversity and composition of the bacterial communities...... 101

Figure 3.2. Based on NMDS ordination by treatment and site in 2018 and 2019, similar bacterial community in 2-yr and 3-yr rotation samples but significantly different bacterial community at NWARS and WARS (PERMANOVA, P=0.001)...... 102

Figure 3.3. At each site, 2018 and 2019 samples had similar bacterial community, but

2018 samples had more variations compared to 2019 samples based NMDS ordination by treatment and year in NWARS and WARS ...... 103

Figure 3.4. The relative abundance (%) of each bacteria/archaea phyla within the bacteria community is similar in 2-yr and 3-yr rotation samples at NWARS in 2018 and

2019...... 104

Figure 3.5. The relative abundance (%) of bacteria/archaea phyla within the bacteria community is similar in 2-yr and 3-yr rotation samples at WARS in 2018 and 2019. ... 105

and 23 core ASVs were presented in 3-yr rotation. Nine core ASVs were shared between four rotation-site groups, and six core ASVs were unique presented only in 3-yr rotation.

...... 106

Figure 3.7. Top 10 ASV or taxa at NWARS. Eight of the most abundant taxa were the same between 2018 and 2019. For NWARS, bacterial community in same rotation treatment had similar results between 2018 and 2019 samples...... 107

Figure 3.8. Top 10 ASV or taxa at WARS. Eight of the most abundant taxa were the same between 2018 and 2019. For WARS, bacterial community in same rotation treatment had similar results between 2018 and 2019 samples...... 108

Pseudomonadaceae. Family Pseudomonadaceae had higher relative abundance in 3-yr rotation compared to 2-yr rotation at NWARS and WARS in 2018...... 109

Genus Ralstonia had lower relative abundance in 3-yr rotation compared to 2-yr rotation at NWARS in 2018 and WARS in 2019...... 110

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Chapter 1: Literature review

Introduction

Soybean (Glycine max) is an important crop in the United States (U.S.), and in 2018, soybean was the biggest crop planted in Ohio (“USDA - National Agricultural Statistics

Service - Charts and Maps - Field Crops” 2018).During the last several decades, the extension of soybean planting has enlarged and caused the loss of crop diversity in the

U.S. land (Sahajpal et al. 2014). Two-year (2-yr) soybean and corn (Zea mays) rotation is a recommended rotation model for soybean production (E. J. Anderson et al. 2019).

However, compared to a short rotation of two years, a longer crop rotations with higher crop diversity could have more benefits to crop production, soil health and soil microbial community (Tiemann et al. 2015; McDaniel, Tiemann, and Grandy 2014). Thus, a three- year (3-yr) soybean, wheat (Triticum aestivum) and corn rotation was studied to investigate how the soybean production, soil health and soil bacterial community improves compared to a 2-yr soybean and corn rotation. In order to develop this project, related background research was reviewed and is described in this chapter. First, this chapter will introduce soybean growth and agronomics, and its important role in solving food security challenges. Also, the needs and benefits of increasing crop rotational

diversity in U.S. land are explained. Finally, how crop productivity, soil health and soil microbial community are related to diverse cropping systems is described.

Soybean

Soybean history

Soybean is a species of legume in the family Fabaceae (Leguminosae) that originated in China between 3,000 and 5,000 years ago (Sedivy, Wu, and Hanzawa 2017). During the colonial era, the soybean was introduced to North America and spread through both

North and South American countries (E. J. Anderson et al. 2019). Today, in the

Americas, soybeans are widely grown in the U.S., Brazil, and Argentina, and these three countries represent around 88% of total world soybean exports (Meade 2016).

Soybean growth

Soybean can be grown successfully in temperate regions such as in the North-Central region of the U.S. Here are some general descriptions about soybean growth. The planting date of soybean can range widely from early spring until mid to late-June with harvest in October each year (Hu and Wiatrak 2012). Normally, the soybean seed, planted at 1 inch deep, needs 5 to 21 days to germinate after planting (Purcell, Salmeron, and Ashlock 2014). There are two soybean growth stages: vegetative and reproductive.

The vegetative stages (VE, VC, V1-Vn) start with the seed emergence (VE) and then move to V1 stage when the seedling’s unifoliate leaves are fully expanded (Fehr and

Caviness 1977). In the vegetative stages, the soybean nodes on the main stem develop 2

and leaves grow from the unifoliate node (Fehr and Caviness 1977). In the reproductive stages (R1-R8), soybeans flower at R1-R2, develop pods at R3-R4, fill seeds at R5-R6 and establish maturity at R7-R8 (Purcell, Salmeron, and Ashlock 2014). The soybean yield components include the seed mass, seed number, seeds per pod and pod number

(Bianchi et al. 2019). The major factors that could affect soybean yield are planting date, temperature, nutritional conditions and diseases. More than 300 species of microorganisms and insects worldwide can attack soybeans and cause disease, but the diseases that most severely suppress soybean yield are soybean cyst nematode (SCN), phytophthora root and stem rot, sudden death syndrome, and seedling disease complexes

(T. W. Allen et al. 2017). However, many successful management strategies, including cultural practices, chemical applications and breeding of disease resistant varieties, have played an important role in reducing the impact of these diseases (Allen et al. 2011).

The important role of soybean in solving food security

In the coming decades, the increasing population, farmland loss and climate change could cause severe food insecurity. In fact, it is estimated that by 2050 food production must increase by 60% to feed more than 9 billion people worldwide (Grafton, Daugbjerg, and Qureshi 2015). To achieve this goal, soybean production is increasing across many countries. Soybean, as one of the most important crops, has large productivity, nutrient sufficiency, and the ability to be consumed directly by humans or indirectly by animals

(Hartman, West, and Herman 2011). In terms of rich nutrition, soybean seeds have 20% oil and 40% protein which could be used directly for human and animal consumption

(Masuda and Goldsmith 2009). For example, the soybean protein can be made into 3

common food like tofu, soymilk, natto and many others. Other than the important role of soybean in food security, soybean is a source of biofuels for industrial production

(Hartman, West, and Herman 2011).

Soybean production in the United States and Ohio

The United States (U.S.) produces 84 Mt of soybean annually, which accounts for about 35% of global soybean production (Hartman, West, and Herman 2011) (Figure

1.1). About 85% of this total is produced in the Corn Belt as well as the North-Central region (E. J. Anderson et al. 2019). The North-Central region encompasses parts or all of

Ohio, Indiana, Illinois, Iowa, Minnesota, Missouri, Nebraska, South Dakota, and

Kentucky (Figure 1.2). This region is characterized by rich, deep and well-drained soils and moderate climate, which contribute to the world’s most productive soybean and corn growing land environment (Meade 2016). Therefore, the North-central region is dominated by the 2-year corn-soybean rotation cropping system (E. J. Anderson et al.

2019).

In the past several decades, the soybean’s average yield had increased dramatically.

For example, during the period of 1989-2019, the U.S. average soybean yield increased from 2304 kg/ha to 3376.8 kg/ha (32.0 to 46.9 bushels/acre) (Figure 1.3). This yield increase is due to improved agronomics, precision agriculture, integrated disease management (Masuda and Goldsmith 2009), and especially by the development of breeding technologies (Rincker et al. 2014). In 2018, the soybean planted area exceeded corn to become the most widely planted crop in U.S. (“USDA - National Agricultural

Statistics Service - Charts and Maps - Field Crops” 2018). In Ohio in 2018, soybean 4

production ranked ninth in the U.S. with 2 million hectares (ha) planted, 4032 kg/ha yield and with a return of 2.5 billion dollars (Turner and Morris 2019).

Soybean management practices

To achieve higher soybean production, there are many available management practices, including earlier sowing date combined with nutrient fertilizer, herbicide and fungicide application (Grassini et al. 2015). The timeline of soybean harvest is very important for the yield. The ideal bean moisture content at harvest and for storage is 13%, and delaying harvest could cause soybean yield loss (Philbrook and Oplinger 1989).

When soybean is in the reproductive stages, adequate irrigation should be applied to ensure enough moisture for soybean pods and seeds development (Torrion et al. 2014).

For instance, in very fine sandy loam soil, adding cover crop, applying a no-till system, and narrowing rows can improve soil moisture during the soybean growing season

(Acharya et al. 2019). Beyond that, applying crop rotations that combine different crops such as corn and wheat could reduce soil-borne disease and weed infestation to improve soybean productivity (B. Q. Zhang and Yang 2000).

Crop diversity loss in U.S. land

In the Midwest and North-Central region, the landscape of corn-soybean rotation has expanded largely over the last several decades and reduced area planted on small crops resulting in a loss of crop diversity (Aguilar et al. 2015). Ritvik Sahajpal et al.

(2014) reported that in a wetland dominated area of the Midwest, the grassland area decreased and instead, the monoculture corn and monoculture soybean cropping systems 5

occupied the newly cultivated lands. However, the loss of the grass cropping system could impact pollination, increase greenhouse gas emissions and decrease pest suppression (Landis et al. 2018) . The loss of crop diversity has also been associated with the decline of yield stability, soil organic matter and soil aggregate stability (Gaudin,

Janovicek, et al. 2015). In contrast, a decade-long grassland experiment showed that increasing crop biodiversity could improve temporal stability and provide more reliable, efficient and sustainable food supply (Tilman, Reich, and Knops 2006). Therefore, developing sustainable agriculture requires crop diversification, and these different crop rotation systems could contribute to biodiversity in agricultural production.

Crop rotation

Crop rotation, a recommended agricultural practice, alternates the crops grown in a field based on a rotational crop sequence (Castellazzi et al. 2008). The benefits of applying crop rotation are improving nutrient availability, reducing fertilizer need, and protecting soil structure (Schönhart, Schmid, and Schneider 2011). Also, by diversifying crops the host for soil-borne pathogens is excluded from the field certain years, so crop rotation is an efficient way to reduce pest and disease prevalence (Wezel et al. 2014).

Due to many benefits that crop rotation brings to crops and soil, it is considered a useful strategy to develop sustainable agriculture and also improve soil health (Riedell,

Osborne, and Pikul 2013). With the expansion of the global population, developing sustainable agriculture is key to dealing with a shortage of food and land. Crop rotation, as a way to develop sustainable agriculture, could reduce input consumption (e.g. water,

pesticides, and fertilizers) and improve crop productivity to provide more food worldwide

(Wezel et al. 2014).

Soybean-corn crop rotation

In 2018, soybean was the largest crop in terms of area planted in the U.S. (Grassini et al. 2015). The 2-yr rotation of planting corn after soybean is a common cultural practice for soybean production. The 2-yr rotation practice is proven to improve soil quality

(Karlen et al. 2006) and yield increase (Woźniak and Soroka 2018), compared to monoculture. However, the 2-yr rotation still brings some problems. For example, some plant pathogens, like Pythium spp. can infect the seedlings of both corn and soybean and cause diseases (B. Q. Zhang and Yang 2000). Also, the 2-yr rotation has limitations for reducing the numbers of nematode cysts in the soil, which cause soybean yield decrease, and according to Porter et al. (2001), five or more years are needed to reduce nematode cyst numbers.

Soybean-wheat-corn crop rotation

Because of the limitations of corn and soybean 2-yr rotation, increasing crop diversity in a longer rotation sequence could yield more benefits than the two-year rotation system.

To increase crop diversity in corn-soybean rotation, wheat is suggested to be added as a third crop to the 2-yr rotation. The higher crop rotational diversity has been proven to increase crop productivity and reduce soil-borne pathogens and weeds.

Crop rotation can improve crop productivity by inhibiting the spread of weeds in the field, and these diverse cropping systems increase the ecological stability and resilience 7

which benefits production. In Wozniak and Soroka (2018), the spring wheat grown in a monoculture had a lower grain yield than spring wheat in crop rotations. This difference is because, in the monoculture, weed infestation was 30% higher than in crop rotations

(Woźniak and Soroka 2018). Similarly, adding winter wheat, oat and dry pea into soybean-corn rotation sequence produced higher corn yield and greater weed suppressive ability compared to just soybean-corn (R. L. Anderson 2017). Also under unfavorable environmental conditions, like hot and dry years, adding small grains into soybean-corn rotation improves the resilience of the cropping system and the yield stability, which are the ability of the cropping system to recover functional integrity under environmental stresses (Gaudin et al. 2015). To summarize, based on this research, increasing crop rotational diversity reduces weed pressure and improves field stability to improve plant growth.

Higher crop rotational diversity reduces soil-borne pathogens which can cause plant root and crown rots, wilts and damping-off disease (Weller et al. 2002). Plant diseases can cause 10%-40% loss of crop production, and these plant pathogens include fungi, bacteria and nematodes (Kan-Rice 2019). Previous research indicated that in the crop rotational fields, there are fewer numbers of pathogens in the soil, compare to monoculture. Crop rotation could inhibit soil-borne pathogens because they are unable to find a consistent host in the field. For example, in Rousseau and Rioux (2007), the disease Sclerotinia stem rot of soybean was greatly reduced by rotation compared with monoculture because of the soil and environment factors like climate, chemicals, humidity which change with the crop rotation.

Since higher crop rotational diversity leaves the pathogen without a host in the field for a longer period, it is more effective to reduce the pathogen population than shorter crop rotation sequences. Also, higher diversity in crops’ root system produces more and diverse chemicals which could attract beneficial bacteria. Some of these beneficial bacteria could produce antifungal compounds such as 2,4-diacetylphloroglucinol (DAPG) and pyrrolnitrin (PRN) which inhibit fungal soilborne pathogens (Latz et al. 2012).

Rotenberg et al. (2007), described the impact of farming practices on DAPG producers, which were more abundant in 3-yr rotations of soybean-corn with oat and hay compared to continuous corn plots, less abundant under tillage soils and more abundant in chemical seed treatments. Also, in Peralta et al. (2018), the most diverse cropping system had higher DAPG producing bacteria which suggest that higher crop diversity has higher disease suppressive potential.

Even though, some studies on soybean rotation showed 3-yr rotation improve soybean growth compared to lower crop diversity (R. L. Anderson 2017; Gaudin,

Tolhurst, et al. 2015), other studies, such as Lund et al. (1993) and Mourtzinis et al.

(2017) did not detect significant soybean yield responses to the 3-yr soybean-wheat-corn.

Thus, the effect of 3-yr rotation on crop productivity compared to 2-yr rotation is unclear.

Nevertheless, in a 3-yr corn-soybean-wheat rotation, wheat is an important crop that can increase soil organic matter and mitigate soil erosion (Campbell and Zentner 1993). Also, in the 3-yr rotation, leaving the wheat straw in the field helps soil moisture retention, decrease soil temperature to increase photosynthesis and improve soybean yield (Akhtar et al. 2019). Thus, beyond the increased yield and reduced soil-borne pathogens in rotations longer than 2 years, research indicates that higher crop diversity could benefit 9

soil health and soil microbial community (Dias, Dukes, and Antunes 2015; McDaniel,

Tiemann, and Grandy 2014). The first advantage of higher crop diversity is improving soil fertility and soil organic matter by increasing the quantity, quality and chemical diversity of plant residues (Tiemann et al. 2015). Secondly, high diversity of crops produce different types of root exudates and change the soil environment, which affect the soil microbial community (Berg and Smalla 2009). Currently, there is limited research regarding the difference in soybean seedling establishment, soil health and soil microbial community between a 3-yr rotation and 2-yr rotation, even though soybean yield in 3-yr rotation has been compared to 2-yr rotation multiple studies (Grassini et al.

2015; Chen et al. 2001).

Soil health

Soil is the foundation of life, and soil health is key to the earth ecosystems. Soil health is the capacity of soil to sustain biological productivity, maintain environmental quality and promote plant and animal health (Doran and Zeiss 2000). Soil degradation results from the loss of soil cover, soil erosion, salinification, acidification and compaction, which can negatively affect soil functions in the ecosystem (Gomiero 2016).

For example, Lal et al. (2015) found that soil degradation decreased soil ecosystem services for plants, animals, and microorganisms by 60% between 1950 and 2010. Soil degradation decreases food production and quality and dampens economic growth, especially in agricultural based countries (Lal 2015). Thus, soil degradation is a serious problem in terms of protecting and improving soil health. Many reasons can accelerate soil degradation, like unsustainable agricultural practices, unsuitable land management 10

and increasing agricultural expansion (Pereira et al. 2018). The impact of soil degradation can be reduced by improving soil health, e.g. increasing soil organic carbon, changing soil structure, and enhancing soil fertility (Lal 2015).

In order to recover the soil health and also achieve sustainable agriculture, application of multiple soil management practices is encouraged. Practices such as organic crop fertilization (manure), biological pest control, no-till or crop rotations are widely used agricultural practices (Wezel et al. 2014; R. L. Anderson 2017).

Higher crop rotational diversity improves soil health

Promoting soil health is an important part of sustainable land management (Herrick

2000), and soil health can be evaluated by measuring the content of soil organic matter

(SOM) and its dominant constituent, soil organic carbon (SOC) (Culman et al. 2012).

SOM is the organic portion of the soil, which includes plant and animal residues, microbial cells and structures, and substances synthesized by the soil population (Wander

2004). In fertile soil, the direct function of organic matter is concerned with the provision of plant nutrients via the processes of decomposition and mineralization; its indirect role is associated with its effect on the physicochemical properties of the soil (Wander 2004).

Therefore, plant residues play important roles in the soil formation process.

Soil chemicals and physical structure are the foundation for crops because soil keeps the nutrients, moisture and chemical elements that are necessary for plant growth. The soil chemical and physical attributes could be affected by the crop rotation system in terms of the carbon (C), nitrogen (N), phosphorus (P) concentrations in the soil. For example, soils under higher rotation diversity had significantly greater N concentrations 11

in the soil than in the monoculture (Riedell et al., 2013). Moreover, soil C also increased from a higher diversity crop rotation system (McDaniel et al. 2014). For example, in a meta-analysis of 122 crop rotation studies, researchers found that compared to a monoculture, one or more crops added to a rotational system increased total soil C by

3.6% and total N by 5.3% (McDaniel, Tiemann, and Grandy 2014).

Compared to monoculture corn or soybean–corn, crop rotations that included winter wheat or alfalfa had a higher overall soil health index including soil aggregate stability, carbon, chemical elements and soil texture (Congreves et al. 2015). Specifically,

Tiemann et al. (2015) compared five diverse rotational system’s soil characteristics and found that in a higher diversity of crop rotational system, there were increases in soil aggregate formation, organic carbon and total nitrogen. Thus, crop rotation changes the soil chemical and physical attributes, and higher crop rotational diversity contributes more to the improvement of soil health.

Soil microbial communities

Soil is the basis of the agricultural products and soil health is the ability to sustain biological productivity, support plant, and animal growth and benefit the environmental quality (Doran and Zeiss 2000). The soil microbiome refers to the community of microorganisms present and interacting with each other in the soil environment

(Marchesi and Ravel 2015). A subset of soil microorganisms associates with plants in the rhizosphere. The rhizosphere is the interface between plant roots and soil where there is greater microbial activity and diversity than the bulk soil, and rhizosphere microorganisms can directly influence their plant hosts (Philippot et al. 2013). Plant- 12

associated microbial communities, such as mycorrhizal fungi, nitrogen-fixing bacteria, and plant growth-promoting rhizobacteria (PGPR), may enhance crop productivity and provide stress resistance. The following examples are functions of rhizosphere microorganisms. First, microorganisms like mycorrhiza and rhizobia help plants uptake phosphorus and nitrogen, respectively (Marschner 2001). Second, rhizosphere microorganisms have the ability to degrade recalcitrant organic matter to improve soil quality (Marschner 2001). In addition, rhizosphere microorganisms could improve the plant defense by preventing colonization by pathogens and modulating host immunity

(Berendsen, Pieterse, and Bakker 2012). Furthermore, rhizosphere microbial activity can increase the efficiency of applied chemical fertilizers and manures through interactions and mobilization of applied nitrogen and phosphorous (Adesemoye and Kloepper 2009).

Due to the important roles of microbial communities in plant health, studying the composition and diversity of microbial communities is the key to understanding the mechanisms of plant-microbiome interaction.

Higher crop rotational diversity impacts soil microbial communities

Crop rotation could affect the activity and diversity of the soil microbial community.

Current research indicates that higher crop rotation increases soil microbial activity and improves plant growth. For example, in D’Acunto and Andrade (2018), the most diverse crop rotation presented the highest soil metabolic diversity and activity along with the change of plant biomass and soil pH. This soil metabolic activity includes soil microbes producing cellulose-degrading enzyme (b-glucosidase) and lignin-degrading enzyme

(phenol oxidase) to decompose the soil substrate for plant use. Similarly, in a higher crop 13

rotational diversity system, greater amount of degrading enzyme was produced through enhanced microbial activity (McDaniel et al. 2014). These increasing microbial activities were related to plant nutrient availability carbon (C), nitrogen (N) and phosphorus (P) uptake. For example, in Anderson et al. (2017), the higher crop rotational diversity system had higher microbial activity and helped corn uptake nutrients from the soil and increase N and P use efficiency. Also, in Tiemann et al. (2015), the higher crop rotational diversity system increased the soil microbial activity and decreased the ratio of carbon-to- nitrogen acquiring enzyme activity, which shows soil microbial community helps plant root uptake C and N better.

The impact of crop diversity in microbial communities was evaluated by Venter et al.

(2016). In their study, Venter et al. (2016) concluded that a higher crop diversity had greater soil microbial richness (+15.11%) and diversity (+3.36%). For instance, crop rotation increases the richness of plant growth promoting rhizobacteria (PGPR) like nitrogen-fixing bacteria which can help the plant absorb nitrogen from the environment, promoting plant growth (Dias et al., 2015). Along with the bacterial community changes in higher crop diversity systems, fungal community changes too. Tiemann et al. (2015), described greater fungal abundance positively related to higher abundance of micro- aggregate. Thus, the application of higher crop rotational diversity can change the soil microbial community towards a more diverse and functional community for crop growth.

Soil bacterial community

The soil bacteria are recognized as one of the significant indicators of soil health for they are sensitive to environmental changes like nutrients, humidity, pH, which affect 14

their diversity and abundance (Doran and Zeiss 2000). In turn, soil bacteria contribute to the soil health and plant growth. For example, the Rhizobium, an important nitrogen- fixing bacteria, can interact with the plant roots to form the nodules and transform the atmospheric nitrogen to fixed nitrogen for plant utilization (Odee et al. 2002).

Traditionally, culture-dependent approaches are largely used for the identification of bacteria. However, only 1% of bacteria in soil environment could be described and cultured successfully (Hirsch, Mauchline, and Clark 2010). With the development of the molecular technologies, different culture-independent methods are available for investigating bacterial communities in soil, like Phospholipid fatty acids, 16s rRNA gene sequencing, fluorescent in situ hybridization, which provide more efficient ways to identify bacteria (van Elsas and Boersma 2011). Now the ability to survey and characterize microbial communities in the environment has further increased due to the development and reduced price of high-throughput sequencing platforms. Using high- throughput sequencing platforms allow us to study in greater depth (eg. increased number of sequences per sample) the presence and relative abundance of microorganisms in an environmental sample, targeting those microorganisms which have yet to be cultured in the laboratory (Gonzalez and Knight 2012).

The molecular methods based on soil nucleic acids and high-throughput sequencing platforms have been the most used to understand the diversity and composition of soil bacteria (van Elsas and Boersma 2011). In diversity studies of bacteria, the 16S ribosomal RNA gene is commonly used as a marker gene because it exists universally, is highly conserved in bacteria strains and also has multiple variable regions (Poretsky et al.

2014). 15

High-throughput sequencing platforms have two groups of approaches to study microbiomes, which depend on the template used for the sequencing reactions (Loman et al. 2012). One approach is the metagenome sequencing which is a shotgun sequencing approach without the need to target or amplify specific genes (Poretsky et al. 2014). The second, and widely used approach involves the PCR amplification and sequencing of single taxonomic gene markers, such as the 16S rRNA gene, and is known as amplicon sequencing (Vos et al. 2012). The sequences obtained from 16S rRNA amplicon sequencing can then be compared with reference databases to identify the taxa present in a microbial community (Hugerth and Andersson 2017). Also, many bioinformatics tools have been developed to analyze the diversity and composition of microbial communities between sequencing samples (Lucaciu et al. 2019).

Hypotheses and objectives

During soybean production, the investment of fertilizer and herbicides take up a great part of the cost, however, the economic investment can be reduced by applying agricultural management practices designed to promote key microbial interactions between soil and plants (Dias, Dukes, and Antunes 2015). Currently, the knowledge about how the 3-yr crop rotation affects soybean production, soil health and soil bacterial communities in Ohio is limited. In this research, the hypotheses are that adding winter wheat into corn-soybean rotation will improve the soybean seedling establishment, soybean production, and soil health, compared to the 2-yr rotation. And, that the 3-yr rotation will result in higher diversity of soil bacterial community compared to 2-yr rotation. Therefore, the objectives of this research are as follows: 1) estimate the effects 16

of 3-yr rotation on soybean stand, biomass and yield compared to 2-yr rotation; 2) measure if 3-yr rotation improves the soil health compared to 2-yr rotation; 3) compare soil bacteria community between 2-yr and 3-yr rotation, and their relationship with plant and soil health measures.

In order to investigate how the soybean, soil and bacterial community respond to 3-yr crop rotation compared to 2-yr crop rotation, field research and data analysis under two research sites and two growing seasons, will be described in Chapters 2 and 3. Chapter 2 summarizes the contribution of 3-yr crop rotation on soybean production and soil health compared to 2-yr rotation. Chapter 3 describes how 3-yr crop rotation change the soil bacterial community compared to 2-yr rotation at two research sites.

Figures

(USDA, https://www.nass.usda.gov/Charts_and_Maps/Crops_County/sb-pr.php)

Figure 1.2. Soybean growing region and production estimates in the north central U.S. in 2018. The major soybean growing area in U.S. is north central area, which includes parts or all of Ohio, Indiana, Illinois, Iowa, Minnesota, Missouri, Nebraska, South Dakota, and Kentucky.

(USDA,https://www.nass.usda.gov/Charts_and_Maps/Field_Crops/soyyld.php)

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Chapter 2: The effects of soybean-wheat-corn rotation on soybean production and soil

health compared to soybean-corn crop rotation

Abstract

Crop rotation is a recommended agricultural practice for soybean production to suppress soil-borne pathogens, improve production, and soil health. This chapter explores the effects of three-year (3-yr) soybean-wheat-corn rotation and two-year (2-yr) soybean- corn rotation on soybean production and soil health. The field experiments were established in 2013 at the Northwest Agricultural Research Station (NWARS) and the

Western Agricultural Research Station (WARS) of the Ohio Agricultural Research and

Development Center. This study analyzed soybean seedling establishment, seedling biomass and yield and soil health in 2018 and 2019 growing season at NWARS and

WARS, for a total of four site-years. Higher soybean seedling stand counts, seedling biomass and yield in the 3-yr rotation treatment were observed but these were not consistent and significant across research station and year of study. Soil organic matter in the 3-yr rotation treatment was greater than the 2-yr rotation treatment for most of the site-years. Higher soil carbon, nitrogen and active carbon in the 3-yr rotation compared to

2-yr rotation was detected in only one site per year. In conclusion, 3-yr rotation had 27

higher soybean production and soil health compared to 2-yr rotation but the site environment and weather condition likely affected the results.

Introduction

Soybean production in Ohio

Soybean is a species of legume native to East Asia and now it is widely grown in

Brazil, Argentina and the U.S. (C. Hart 2017). As an important economic crop, soybean contains significant amounts of protein, oil and dietary fiber (Masuda and Goldsmith

2009). In 2018, soybean was the largest crop in terms of planting area in the U.S.

(“USDA - National Agricultural Statistics Service - Charts and Maps - Field Crops”

2018). In Ohio, soybeans accounted for over 2 million hectares (ha) of farmland in 2018

(Turner and Morris 2019). Therefore, studying soybean is meaningful and necessary to support the agricultural development in Ohio.

Soybean-corn rotation and soybean-wheat-corn rotation

The 2-year rotation of planting corn after soybean is a common cultural practice for soybean production in the Midwestern states, including Ohio. The 2-year corn- soybean rotation has been proven to improve soil quality (Karlen et al. 2006) and increase crop productivity (Woźniak and Soroka 2018) compared to monoculture. For farmers, planting corn-soybean instead of small grains can have higher income of cash crop and lower labor cost. For example, Ohio AG Net reported that the projected returns to land (total revenue from the crop and subtracting all expenses except the land expense) 28

for wheat (-5～357.5 dollars/ha) is lower than soybean (210~635 dollars/ha) and corn

(57.5~455 dollars/ha) (Reese 2019). Also, the close harvesting time for corn and soybean in fall is easier for arranging both crops’ labor management together than harvesting winter wheat in spring (USDA 2010). In the Midwest, the landscape of corn-soybean rotation has expanded largely over the last several decades and resulted in reduced planting area of small grain crops and a loss of crop diversity (Aguilar et al. 2015). The loss of crop diversity has been associated with the decline of yield stability, soil organic matter and soil aggregate stability (Gaudin, Janovicek, et al. 2015). However, adding small grains and forage legumes into corn-soybean rotation could sustain the agricultural resources usage and crop productivity (Liebman et al. 2013).

Wheat is an important crop that can increase soil organic matter and mitigate soil erosion (Campbell and Zentner 1993). The addition of winter wheat to the corn-soybean rotation has been investigated to determine whether higher rotation diversity increases crop productivity. However, there is limited research to study the difference in soybean productivity and soil health between a 3-yr rotation that incorporates wheat and 2-yr corn-soybean rotation. In this study, the 3-yr corn-soybean-wheat rotation, as higher crop rotation system, is hypothesized to increase crop productivity and improve soil health compared to the 2-yr corn-soybean rotation.

Higher crop rotation diversity increases crop productivity

Crop rotation can improve growth by inhibiting the spread of weeds in the field

(Woźniak and Soroka 2018). Also, crop rotation is one alternative to achieve crop diversification which increases the ecological stability and resilience of soil to benefit 29

crop production. For example, Anderson et al. (2017) showed that adding winter wheat, oat (Avena sativa), and dry pea (Pisum sativum) into soybean-corn rotation sequence caused higher corn yield and weed suppressive ability compared to the soybean-corn rotation. Also, under unfavorable environmental conditions, like hot and dry years, adding small grains into soybean-corn rotation improves the resilience of the cropping system and the yield stability (Gaudin, Tolhurst, et al. 2015). Based on Gaudin, Tolhurst, et al. (2015), increasing crop rotation diversity reduces weed pressure and improves resilience of the cropping system to improve plant growth.

Higher crop rotation diversity reduces soil-borne pathogen

Higher crop rotational diversity could reduce soil-borne pathogens by breaking the disease cycle. With crop rotation, hosts are excluded from the soil and hence impacting pathogen populations (Garza et al. 2002). Also, the higher diversity of crops root system produces more and diverse root exudates which could attract beneficial bacteria. For example, some of these soil bacteria can produce antifungal compounds such as 2,4-diacetylphloroglucinol (DAPG) and pyrrolnitrin (PRN) which inhibit fungal pathogen growth (Latz et al. 2012). In Peralta et al (2018), for example, the most diverse crop system had higher DAPG producing bacteria; hence higher crop diversity resulted in higher disease suppressive ability.

Higher crop rotation diversity changes soil health

Many soil health indicators are important for sustainable land management (Herrick

2000). Soil health comprises soil chemical, physical, and biological characteristics. These soil chemical, physical and biological characteristics could be affected by the crop rotation system. Crop rotation can impact the concentration of carbon, nitrogen, phosphorus and other nutrients in the soil (Karlen et al. 2006). For example, adding wheat into corn-soybean rotation could improve N use efficiency (NUE; calculated by the yield gains obtained per unit of incremental N rate) in corn and soybean (Gaudin,

Tolhurst, et al. 2015). Moreover, soil carbon (C) also increased from a higher diversity crop rotation system (McDaniel et al. 2014). In a meta-analysis of 122 crop rotation studies, researchers found that compared to a monoculture (corn, soybean, etc.), one or more crops included into a rotation system increased total soil C by 3.6% and total N by

5.3% (McDaniel, Tiemann, and Grandy 2014).

Project background

In 2013, a field experiment was established by Dr. Laura Lindsey, faculty in the

Department of Horticulture and Crop Science, at two research stations in Ohio: the

Northwest Agricultural Research Station (NWARS) and the Western Agricultural

Research Station (WARS) from the Ohio Agricultural Research and Development

Center (OARDC) at The Ohio State University (OSU). This experiment was designed to explore benefits of adding winter wheat into the traditional corn and soybean 2-year rotation in Ohio. Previous studies on crop rotation diversity have been largely focused on a single experimental site with data collected during one growing season. For example, 31

the Kellogg Biological Station (KBS) at Michigan State University (MSU) established a

Long-term Ecological Research (LTER) project and many crop rotational diversity research has been performed there (“KBS LTER | Kellogg Biological Station | Long-

Term Ecological Research” 2020). For example, Tiemann et al. (2015) and Peralta et al.

(2018) researched the effect of crop rotational diversity in KBS LTER during the 2012 growing season. However, their research did not focus on soybean-wheat-corn rotation compared to soybean-corn rotation. Also, Mourtzinis et al. (2017) compared 3-yr soybean-wheat-corn rotation with continuous cropping, and Weyers et al. (2018) compared 4-yr soybean-corn-wheat-alfalfa with 2-yr soybean-corn rotation; both studies performed at one site each in Arlington Agricultural Research Station, Wisconsin and Swan Lake Research Farm, MN, respectively. Therefore, this study on two sites and years combination is unique and meaningful for detecting how different environment and weather each year will affect the impact of 3-yr soybean-wheat-corn on the soybean production compared to 2-yr soybean-corn rotation.

In this study, the soybean yield data in 2016 and 2017, after one round of 3-year rotation was completed, is shown in Figure 2.1 for both sites (NWARS and WARS). The results showed a trend of higher yield in the 3-yr rotation compared to the 2-yr rotation, however not significant. Based on the trend of higher yield in the 3-yr rotation in 2016 and 2017, this project investigated the effects of the 3-yr rotation on soybean production and soil health during the 2018 and 2019 growing seasons at NWARS and WARS. The higher soybean production and soil health were hypothesized to be greater in the 3-yr rotation compared to the 2-yr rotation.

Specific objectives and hypotheses

Objective 1. Determine the effect of 3-yr soybean-wheat-corn rotation on soybean production compared to 2-yr corn-soybean.

Hypothesis 1. Soybean seedling stands, root/shoot biomass, and soybean yield will be higher in the 3-yr rotation treatment than the 2-yr rotation treatment, regardless of site- year.

Objective 2. Determine the effect of 3-yr soybean-wheat-corn on soil health compared to 2-yr corn-soybean.

Hypothesis 2. Soil organic matter, carbon, nitrogen, and active carbon will be higher in the 3-yr rotation treatment than the 2-yr rotation treatment.

Materials and methods

Field Experiment Description

The crop rotation field experiment was established in 2013 by Dr. Laura Lindsey, from the Department of Horticulture and Crop Science at OSU. Replicated experiments were established at both NWARS and WARS from OARDC at OSU. The geography information of NWARS and WARS are described in the Table 2.1. The experimental treatments were 2-yr soybean-corn rotation and 3-yr soybean-wheat-corn rotation (Table

2.2). For 3-yr rotation, winter wheat was added into the soybean-corn rotation; and both experimental treatments and sites were conventionally managed under no-till. The plots at each field site were established as a randomized complete block design, with each crop within the rotation sequence being present each year, and with four replicate plots per treatment (Table 2.3). The blocking criteria was the spatial block location in the field and each plot is an experimental unit. Different cultivars of Roundup Ready soybean were planted at each site-year during May or June and harvested in October (Table 2.4). The precipitation and temperature in each year and growing season at NWARS and WARS were collected by the CFAES Weather System (Table 2.5) (“CFAES Weather System”

2020).

Soybean Biomass and Yield Measurements

The soybean seedling stage is an important stage for soybean establishment and many soil-borne pathogens will cause damping off at the soybean seedling stage. In this project, the soybean establishment and biomass at seedling stage were studied. To evaluate the soil-borne seedling disease prevalence of soybean under the 2-yr and 3-yr rotation, the seedling establishment (number of plants/meter) was counted from each plot.

Also, the stand (plant/meter) was transformed into stands/ha (stands/meter divided by the row width which is 0.19 m, and then multiplied by 100,00 (1 ha = 100,00 m2)) . Also, at each site-year combination, 64 soybean seedlings were sampled from the rotation treatment and dried in the greenhouse for one week. The dried soybean shoot and root biomass were measured. Root biomass is an important growth indicator in plants because roots anchor the plant and absorb and transfer water and mineral nutrients (Mandal, Hati, and Misra 2009). Shoot biomass will provide an indication of overall plant health and vigor. For stand counting and shoot/root biomass measurements were taken at soybean seedling stage (V3-V5), and 8 samples/plot were collected. In total, 64 seedling samples were collected from NWARS and WARS at 2018 and 2019.

Yield can be significantly influenced by environmental factors or cultural practices (Mandal, Hati, and Misra 2009), thus the soybean yield was compared between

2-yr rotation and 3-yr rotation treatments. Yield data in 2018 and 2019 at NWARS and

WARS were collected by Dr. Laura Lindsey and research station field crews; the yield was then adjusted to 13% standard moisture (130g/kg). The adjusted yield unit was

bushel/acre and it was transformed to kg/ha (kg/ha= ((bushel/acre) * 60) *1.2). The yield was collected for each plot with 8 yield measurements for all site-year combinations.

Soil Health Evaluation

Different soil health indicators for both long-term and short-term changes were tested in the rotation experiment. The long-term soil health indicator includes soil organic matter (SOM), total carbon (C) and total nitrogen (N) (D. E. Allen, Singh, and Dalal

2011). The short-term soil health indicator is soil active carbon, which responds sensitively to the effects of soil management (Culman et al. 2012). The soil samples were collected with the whole soybean plant during the soybean seedling stage at top 10-15 cm soil. The collected soil samples were air dried in the greenhouse for one to two months.

Eight samples from one plot were pooled into one plastic bag. Soils were homogenized and sieved through a No.10 mesh (< 2.00 mm size). Around 0.5 kg sieved soil were collected and kept for the soil tests. In total, there were 8 sieved soil samples from

NWARS and WARS for 2018 and 2019.

For soil analysis, 50g of sieved soil sample for each plot at NWARS and WARS for 2018 and 2019 were sent to the Service Testing And Research (STAR) Lab at

OARDC for SOM, total C and N (“Service Testing and Research Laboratory” 2020).

These soil health results were tested by dry combustion method (“Service Testing and

Research Laboratory” 2020).

Soil active carbon was tested in the laboratory, and the protocol was adjusted based on the Permanganate oxidizable C (POXC) method (“Protocol - Procedure for the

Determination of Permanganate Oxidizable Carbon” 2017). Two different sizes of soil 36

particles were initially tested for POXC: 0.6mm and 2mm, which were suggested in Weil et al. (2003) and Culman et al. (2012) separately. Through comparing the coefficient variation of each sample’s POXC value between 0.6mm and 2mm soil samples, we determined that 0.6mm size soil samples had higher coefficient variation than 2mm size soil samples (Table 2.6). Therefore, 2mm size soil samples were used in the subsequent

POXC analysis. For the POXC test, two soil samples/plot were tested and there were 8 plots at NWARS and WARS for 2018 and 2019. All tests were run using a standard soil sample. The standard soil sample was a composite mixture generated from soils of two different locations in Ohio. The standard soil was analyzed for POXC along with the rotation soil samples to make sure the consistency of the POXC experiment across runs.

The POXC procedure was performed as follows. First, the reagent of 0.2 M

KMnO4 stock solution and four solution standard KMnO4 (0.005, 0.01, 0.015 and 0.02

M) were prepared by diluting 0.2M KMnO4 with distilled water. The standard KMnO4 dilutions were used to calculate the standard curve to be used for soil sample POXC estimation. Second, 2.5g dried soil sample was measured into 50ml disposable centrifuge tubes and added 18ml distilled water. Next, 2ml 0.2M KMnO4 was added into the 50ml tube with the soil sample and shook the tube on a shaker for 2 minutes at 120rpm. After shaking, each sample was settled for 10 minutes and the 0.5ml upper liquid was tipped into a pre-filled 50ml tube with 49.5ml distilled water in. Then, the diluted soil sample was vortexed and 1ml sample was transferred into a cuvette for analysis in the spectrophotometer. The absorbance (optical density, Abs) of each sample was read twice at 550 nm using the spectrophotometer (VARIAN, Cary 50 Bio Spectrophotometer;

Simple Read Software). According to the readings of four standard KMnO4 solutions, the 37

standard curve was estimated, and the intercept (a) and slope (b) of the standard curve were calculated. Then, the mass of POXC for unknown soil samples were calculated using the equation: POXC (mg/kg) = [0.02 mol/ L – (a + b × Abs)] × (9000 mg C/mol) ×

(0.02 L solution/Wt). Also, the POXC value of standard soil was checked if within normal range (500-800 mg/kg), and the coefficient variation of POXC estimates from unknown samples from the same plot were expected to be under 10%. Finally, the POXC estimates of two repeated soil samples per plot were averaged and processed as a single data point per plot.

Statistical analysis

Data collected from soybean soil, seedling biomass and yield in 2018 and 2019 at

NWARS and WARS were analyzed in R studio (Version 3.6.0). Prior to testing for treatment effects, the ANOVA assumption of normality was tested with Shapiro-Wilk’s

W test using the R functions of “shapiro.test()” , and the homogeneity was test with

Barlett’s test using R functions of “bartlett.test()”, respectively . For the analysis of variance, the model used was Y= Ground Mean + Rotation treatment effect + Block +

Error, and the rotation treatment and block were considered fixed. The function “anova()” from R base was used to compute one-way ANOVA test and the package “ggplot2” was used to plot the figures. The null hypothesis of ANOVA test is the mean values of different treatment are the same. However, if a p-value of the ANOVA test is less than

0.1, then the mean values of each treatment are different. Because there were two rotation treatments in the comparison, the p-value less than 0.1 indicated that there was soybean production or soil health difference between 2-yr rotation and 3-yr rotation. The 38

nonparametric Kruskal-Wallis H Test was used when the data did not fit ANOVA assumption, and the R function “Kruskal.test()” was used for this.

Results

Soybean Seedling Establishment

In 2018, at NWARS, there was no significant difference in soybean stand, root dry weight and shoot dry weight between the 3-yr rotation treatment and 2-yr rotation treatment (Table 2.7). In 2018, at WARS, there was no significant difference in root dry weight and shoot dry weight between 3-yr rotation treatment and 2-yr rotation treatment

(Table 2.7). However, in 2018, at WARS, there was 23% greater soybean stand (10.23 plants/meter) in 3-yr rotation treatment compared to 2-yr rotation treatment (8.34 plants/meter) (P=0.02).

In 2019, at NWARS, there was no significant difference in soybean stand between

3-yr rotation treatment and 2-yr rotation treatment (Table 2.7). However, in 2019, at

NWARS, soybean in the 3-yr rotation treatment had 19% greater root dry weight

(P=0.04) and 15% greater shoot dry weight compared to soybean in 2-yr rotation treatment (P=0.08) (Table 2.7). In 2019, at WARS, there was no significant difference in soybean stand, root dry weight and shoot dry weight between 3-yr rotation treatment and

2-yr rotation treatment (Table 2.7).

Soybean Yield

In 2018, at NWARS, 3-yr rotation treatment had a 5% higher soybean yield than

2-yr rotation treatment (P=0.01, Figure 2.2). However, in 2019, there was no difference in soybean yield between 2-yr rotation treatment and 3-yr rotation treatment at NWARS

(P=0.40). Additionally, in WARS, both 2018 (P=0.45) and 2019 (P=0.39) soybean yield did not show significant difference between rotations.

Soil Health

Organic matter

At NWARS, 3-yr rotation treatment had higher organic matter than 2-yr rotation treatment in both 2018 and 2019. In 2018, 3-yr rotation treatment had 4.1% higher organic matter than 2-yr rotation treatment at NWARS (P=0.09, Table 2.8). In 2019, 3-yr rotation treatment had 8% higher organic matter than 2-yr rotation treatment at NWARS

(P=0.07, Table 2.8).

At WARS, there was no difference in organic matter between 2-yr rotation treatment and 3-yr rotation treatment in 2018. However, in 2019, 3-yr rotation treatment had 14% higher organic matter compared to 2-yr rotation treatment (P=0.03, Table 2.8).

Soil carbon

In 2018, 3-yr rotation treatment had a 4% higher soil carbon content compared to

2-yr rotation treatment at NWARS (P=0.04, Table 2.8). However, in 2019, there was no 40

difference in soil carbon content between rotations at NWARS. Also, at WARS, there was no difference in soil carbon content between rotations both in 2018 and 2019.

POXC

POXC (Permanganate Oxidizable Carbon) test is a measurement of active carbon in soil. Active carbon can respond to short-term changes in soil management. In

NWARS, POXC means had no significant difference between rotations in 2018 and

2019. Similarly, in 2018, no significant difference between rotations were observed in

WARS. However, in 2019, WARS the 3-yr rotation treatment had a 4% higher POXC compared to the 2-yr rotation treatment (P=0.02, Table 2.8).

Soil nitrogen

In 2019, at NWARS, there was a 4% higher soil nitrogen content in 3-yr rotation treatment compared to 2-yr rotation treatment (P=0.08, Table 2.8). However, in 2018, there was no significant difference in nitrogen content between 3-yr rotation treatment and 2-yr rotation treatment at NWARS. Additionally, in WARS, both 2018 and 2019 there was no difference in soil nitrogen between rotations.

Discussion

Higher crop diversity in a rotation has been shown to promote crop productivity by reducing soil-borne pathogen (Latz et al. 2012), increasing soil nutrient availability

(McDaniel, Tiemann, and Grandy 2014) and promoting diverse soil microbial

communities (Tiemann et al. 2015). In this study, the benefits of 3-yr rotation were compared to 2-yr rotation in terms of soybean production and soil health at two locations and years in Ohio (NWARS and WARS in 2018 and 2019).

Overall, the results from all site-years combination, indicate improvements on soybean seedling stand, seedling biomass, soybean yield, SOM, total C, total N and active carbon in the 3-yr rotation than 2-yr rotation. For example, a trend of higher soybean yield in the 3-yr rotation treatment was observed from 2015 to 2019 yield data at

NWARS and WARS, however these differences were significant on only one site-year combination. Also, SOM in soils from the 3-yr rotation was higher than in the 2-yr rotation in three out of four site-year combinations. However, the benefits of the 3-yr rotation were not consistently significant in all site-year combinations. These indicates that benefits of rotation might be dependent of environmental variables specific to site- year.

Why some site-year combinations had benefits from 3-yr rotation in terms of soybean production and soil health, but other site-year combinations did not? In this study, many environmental parameters were found to be associated with the variations of

3-yr benefits across NWARS and WARS in 2018 and 2019. These environmental parameters include soil drainage, weather conditions (i.e. growing degree days and precipitation) and soil fertility (Table 2.1 and Table 2.8).

Generally, at NWARS, the 3-yr rotation resulted in more significant responses in soybean yield and seedling vigor than the 3-yr rotation at WARS. Compared to WARS soil, and NWARS soil is very deep and poorly drained. The combination of the poorly drained soil with no-till management at NWARS, resulted in slow drying of wet soils in 42

the spring, which delayed the soybean planting date and impacted growing degree day accumulation (Table 2.4) (Eckert 1987). Poorly drained soil and late planting impact soybean production by reducing nutrient availability, increasing soil-borne disease pressure and increasing yield loss due to frost or freeze injury in the fall (Randall and

Vetsch 2005; Mourtzinis et al. 2017). Furthermore, the poorly drained soil and late plating also affected overall length of growing degree days (GDDs) and soil nitrogen (N).

The length of GDDs is important for seedling production and germination through accumulation of heat units required to geminate and emerge (Mourtzinis et al. 2017;

Randall and Vetsch 2005). Soil N is important for soybean growth especially during soybean early establishment, and the availability of soil N affect soybean development, nodulation, and biomass (Schipanski, Drinkwater, and Russelle 2010). Therefore, at

NWARS the significance of the 3-yr rotation in soybean seedling biomass and yield production is observed under conditions that stress soybean plants and are conducive to lower yields. At the same time, soil organic matter at NWARS in 3-yr rotation was higher than 2-yr rotation, which promote the soil health and soybean growth. Based on these, we conclude that, at soybean seedling stage, the 3-yr rotation improved soybean root and shoot biomass through reducing the impact of poor drained soils, late planting and low

GDDs and improving soil fertility. In contrast, at WARS, there was no delayed planting due to wet soil, so there was minimal difference in soybean production between 2-yr and

3-yr rotations.

Soybean seedling damping off disease caused by Pythium spp., and Phytophthora spp., is a major reason for soybean stands reduction (Broders et al. 2007; Schmitthenner

2000). Other factors such as delayed germination due to early planting and moist soil 43

could cause low soybean stands (Robinson, 2011). Here, the soil-borne pathogens differential population in 2-yr and 3-yr rotation is assumed to cause the differential soybean stands between rotation treatments. The 3-yr rotation had lower disease prevalence, as measured by stand counts, at soybean seedling stage compared to the 2-yr rotation. Specifically, at soybean seedling stage, the 3-yr rotation had significantly higher soybean stand compared to the 2-yr rotation treatment at WARS in 2018. In addition, the weather data (Table 2.5) indicate that in 2018 WARS had the highest precipitation level compared to other sites and years combinations. High precipitation can increase disease pressure from oomycete pathogens, in particular Phytophthora which cause root rot of soybean (Schmitthenner 2000) and Phythium spp. which cause damping-off (Broders et al. 2007). This result fit the hypothesis that higher crop diversity could reduce pathogen infestation. Rupe et al. (1997) showed adding non-soybean crops into soybean field reduced soil-borne pathogen from year to year, so crop rotation is an efficient way to reduce pest and disease prevalence. However, at NWARS in 2018 and at both sites in

2019, 3-yr rotation did not show differences in stand, compared to 2-yr rotation. This result indicates that the potential impact on disease suppressive ability in longer crop rotations fields depends on environment and weather patterns. Pathogen pressure, however, was not measured in this study. Also, even though the damping-off disease may cause the seedling soybean stands reduction, the stands rate in all site-year were over

2.47x105 plants/ha (100,000 plants/acre) which won’t limit soybean yield.

There were consistent higher SOM, N and C in the 3-yr rotation compared to the

2-yr rotation at NWARS, in 2018 and 2019. However, at WARS, these parameters did not show higher content in 3-yr rotation compared to 2-yr rotation. At the same time, 44

comparing the soybean productivity at NWARS and WARS, NWARS showed more 3-yr rotations benefits in soybean productivity compared to WARS. Thus, the soil health benefits from 3-yr rotation depends on the site environment, and soil health benefits are related to soybean productivity benefits in 3-yr rotation. Even though, WARS did not have much soybean productivity and soil health benefits from 3-yr rotation in 2018 and

2019, but more benefits are expected in future years. This could be explained by the active carbon test result at WARS. Beginning in 2019, 3-yr rotation treatment had higher

POXC (soil active carbon) than 2-yr rotation treatment, which is the labile soil carbon.

POXC is sensitive to the changes of agricultural management, as a short-term soil health indicator (Culman et al. 2012). Thus, the higher POXC in 3-yr rotation compared to 2-yr rotation at WARS in 2019, indicated the labile carbon in WARS soil responded to different rotations. As Culman et al. (2012) reported, POXC can detect changes in management involving tillage and inputs after two to four years. In this project, the

POXC at WARS detected the difference of rotation treatment in 2019 which was after 2 rounds of a 3-yr rotation since 2013. Therefore, for other long-term soil health indicators such as SOM, C and N, WARS longer time is needed to detect the changes between 2-yr rotation and 3-yr rotation.

In conclusion, at NWARS and WARS, in 2018 and 2019, only few site-year had higher soybean seedling stand counts, seedling biomass and yield in the 3-yr rotation treatment compared to 2-yr rotation. These soybean productivity benefits from 3-yr rotation were presented under unfavorable environmental conditions such as poorly drained soil, late planting, higher precipitation and lower growing degree days. In soil health study, only in one out of four site-year, 3-yr rotation had higher soil carbon, 45

nitrogen and active carbon compared to 2-yr rotation. However, soil organic matter in the

3-yr rotation treatment was greater than the 2-yr rotation treatment for most of site-year data. Therefore, 3-yr rotation soybean-wheat-corn is recommended for increasing soybean productivity under unfavorable environments and improving soil health through adding soil organic matter.

Tables

Table 2.1. Geographic descriptions, soil types and field management at NWARS and WARS. NWARS had much deeper and poorly drained soil compared to WARS.

Field Site Location Latitude Longitude Elevation Soil type management Wood Hoytville NWARS1 County, 41.2847 -83.8444 693 ft 211 m silty clay No-tillage OH loam Clark Kokomo 1124 ft 343 WARS County, 39.8633 -83.6721 silty clay No-tillage m OH loam

1NWARS: Northwest Agricultural Research Station; WARS: Western Agricultural Research Station

Table 2.2. Rotation sequences and crops per year of the experiment since 2013 (Source, Laura Lindsey). Blue shades indicate the sampled soybeans in 2-yr and 3-yr rotation treatments.

Treatment Rotation 2013 2014 2015 2016 2017 2018 2019

3-yr S/W/C Corn Soybean Wheat Corn Soybean Wheat Corn

2-yr S/C Corn Soybean Corn Soybean Corn Soybean Corn

3-yr S/W/C Soybean Wheat Corn Soybean Wheat Corn Soybean

2-yr S/C Soybean Corn Soybean Corn Soybean Corn Soybean

3-yr S/W/C Wheat Corn Soybean Wheat Corn Soybean Wheat

3-yr 2-yr 2-yr 3-yr 3-yr Block4 Soybean Corn Soybean Corn Wheat

3-yr 3-yr 2-yr 3-yr 2-yr Block3 Wheat Soybean Corn Corn Soybean

3-yr 2-yr 2-yr 3-yr 3-yr Block2 Corn Corn Soybean Wheat Soybean

3-yr 2-yr 3-yr 2-yr 3-yr Block1 Wheat Soybean Corn Corn Soybean

Table 2.4. Soybean varieties used in this study and field management characteristics. NWARS had late soybean planting date in both years compared to WARS.

Soybean Relative Planting Sampling Harvest Year Site Spray date1 variety maturity date date date Pioneer 4/24/2018; 2NWARS 3.1 5/29/18 6/18/18 10/1/18 31A22X 6/15/18 2018 Stewarts WARS 3628 RR 3.6 5/12/18 5/14/18 6/7/18 10/1/18 Extend Pioneer 6/12/19; NWARS 2.9 6/27/19 7/25/19 10/24/19 29A25X 7/30/19 2019 Pioneer WARS 3.6 6/3/19 5/29/19 7/4/19 10/10/19 P36T36X

1Spray date: the date of spray herbicide (Glyphosate, Trivence, Medal II etc.) 2NWARS: Northwest Agricultural Research Station; WARS: Western Agricultural Research Station

Air Soil Relative Year Site Precip1/Year GDD2/Year Precip/Season GDD/Season Temp3 Temp4 Humidity(%)

NWARS 82.55 3,544.00 13.26 2,761.00 28.90 25.39 73.00 20185 WARS 118.87 3,855.00 20.00 3,247.70 20.27 20.28 73.00

NWARS 110.23 3,415.00 17.31 2,416.30 21.44 22.17 69.00 2019 WARS 86.11 3,727.00 9.94 2,860.40 25.72 25.44 86.00

1Total liquid precipitation (melted in case of ice or snow) in cm. 2Growing Degree Days: how many degrees above 50 Fahrenheit the average temperature was for the day, calculated using the modified sine wave method (F).(“CFAES Weather System” 2020) 3The average of the 24 hourly air temperature for the sampling day (°C) 4The average of the 24 hourly soil temperature at 10 cm depth for the sampling day (°C) 5The weather data was collected from CFAES weather system, OSU (“CFAES Weather System” 2020).

Sample2 < 2 mm < 0.6 mm

NWARS102 0.61% 5.24%

NWARS105 2.26% 5.39%

NWARS203 1.23% 2.96%

NWARS205 0.45% 0.99%

∑ |푋−μ| 1 The coefficient variation = √ ∗ 100% ; X is a value of the sample, μ is the mean value, N is the number of repeated sample 푁 (N=4). 2 The sample are from NWARS (Northwest Agricultural Research Station), 102, 105, 203 and 205 are the plot names.

Table 2.7. Soybean seedling establishment and biomass in 2018 and 2019 at NWARS and WARS. 3-yr had higher stand (/meter), root and shoot biomass only in one site-year.

Shoot Dry Year Site Rotation Stand (/meter)3 Stand (/ha)4 Root Dry Weight (g)5 Weight (g)6 2018 NWARS1 2-yr2 6.16 ± 0.697 3.23x105 0.14 ± 0.05 0.30 ± 0.04 3-yr 5.66 ± 0.98 2.97x105 0.12 ± 0.01 0.31 ± 0.03 WARS 2-yr 8.34 ± 0.84a 4.37x105 0.11 ± 0.02 0.31 ± 0.06 3-yr 10.23 ± 0.50b 5.37x105 0.12 ± 0.03 0.36 ± 0.03 2019 NWARS 2-yr 5.84 ± 0.49 3.65x105 0.46 ± 0.05a 0.64 ± 0.10a 3-yr 5.22 ± 0.80 2.74x105 0.55 ± 0.05b 0.74 ± 0.17b WARS 2-yr 9.00 ± 0.67 4.72x105 0.22 ± 0.05 0.45 ± 0.12 3-yr 9.09 ± 0.62 4.77x105 0.18 ± 0.05 0.42 ± 0.06

1NWARS: Northwest Agricultural Research Station; WARS: Western Agricultural Research Station 22-yr: Corn-Soybean rotation; 3-yr: Corn-Soybean-Wheat rotation 4 Averaged stand(/ha). Stands(/meter) were adjust into stand(/ha) based on the field row space. The stand/ha= stand(/meter) / 0.1905*10000 3 Applying one-way ANOVA analysis 5,6 Applying non-parametric method - Kruskal Wallis test 7 The mean value of samples within a rotation treatment ± the standard deviation of samples; the significant difference between rotation/year/site shown as small case letters if P<0.1, N=8

Table 2.8. Soil health results from soybean grown under different rotations in 2018 and 2019 at NWARS and WARS. Soil organic matter had higher content in three out of four site-years in 3-yr rotation, and soil nitrogen, carbon or active carbon had higher content in only one site-year 3- yr rotation compared to 2-yr rotation. Organic C:N Year Site Rotation Nitrogen(%)3 Carbon(%)4 POXC(mg/kg)6 Matter(%)2 Ratio5 9.39 ± NWARS1 2-yr a 0.24 ± 0.017 a 538.95 ± 59.36 5.03 ± 0.33 2.28 ± 0.19 0.43 9.95 ± 3-yr b 0.26 ± 0.01 b 589.33 ± 95.94 5.23 ± 0.32 2.61 ± 0.20 0.37 2018 6.98 ± WARS 2-yr 5.24 ± 0.26 0.40 ± 0.13 2.55 ± 0.18 576.95 ± 88.11 2.58 7.60 ± 3-yr 5.35 ± 0.36 0.41 ± 0.19 2.68 ± 0.19 524.13 ± 26.72 3.06 8.45 ± NWARS 2-yr a a 2.44 ± 0.15 705.85 ± 87.33 4.89 ± 0.30 0.29 ± 0.01 0.43 8.44 ± 3-yr b b 2.53 ± 0.03 700.08 ± 55.12 2019 5.26 ± 0.16 0.30 ± 0.01 0.16 8.75 ± WARS 2-yr a 0.29 ± 0.03 2.57 ± 0.29 a 4.78 ± 0.65 0.21 610.88 ± 22.06 8.86 ± 3-yr b 0.30 ± 0.04 2.65 ± 0.41 b 5.46 ± 0.53 0.37 635.10 ± 20.39

1NWARS: Northwest Agricultural Research Station; WARS: Western Agricultural Research Station; soil sampled during soybean phase. 2,3,5,6Applying one-way ANOVA analysis 4Applying non-parametric method - Kruskal Wallis test 6Permanganate Oxidizable Carbon 7The mean value of samples within one rotation treatment ± the standard deviation of samples; the significant difference between rotation/year/site shown as small case letters if P<0.1, N=8

Figures

NWARS WARS 5000

4000

6 3000

) P=0.23 P=0.12 a

h 2000

/ Treatment g

K 2−yr

( d

l 5000 3−yr

i Y

4000

7 3000

2000 P=0.30 P=0.76

2−yr 3−yr 2−yr 3−yr Rotation

Figure 2.1. Soybean yield at NWARS and WARS from 2016 to 2017. 3-yr rotation had a higher averaged soybean yield compared to 2-yr rotation, however the yield difference is not significant (P>0.1). NWARS: Northwest Agricultural Research Station; WARS: Western Agricultural Research Station; 2-yr: Corn-Soybean rotation; 3-yr: Soybean-Wheat- Corn rotation

NWARS WARS 5500

5000

4500

4000 8

3500

) P=0.01 P=0.45 a 3000

h Treatment

/ g

k 2500

( 2−yr

5500

d l

e 3−yr

i 5000 Y 4500

4000 1

3500

3000 P=0.39 P=0.39

2500 2−yr 3−yr 2−yr 3−yr Rotation

Figure 2.2. Soybean yield at NWARS and WARS in 2018 and 20191. At NWARS, in 2018, 3-yr rotation had higher soybean yield compared to 2-yr rotation (P<0.10). At other site-years, the yield was same in both rotation treatments. NWARS: Northwest Agricultural Research Station; WARS: Western Agricultural Research Station; 2-yr: Corn-Soybean rotation; 3-yr: Soybean- Wheat- Corn rotation; P value was calculated by one-way ANOVA and P was marked at NWARS, in 2018 which shown a significant increase in 3-yr rotation soybean yield.

List of References

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Chapter 3: How soybean-wheat-corn rotation changes the diversity and composition of

soil bacterial community compared to soybean-corn rotation

Abstract

Higher crop rotational diversity can improve crop productivity, soil health and soil microbial diversity. This research hypothesized that a three-year (3-yr) rotation of soybean-wheat-corn would have higher diversity of soil bacterial, greater abundance of beneficial bacteria and less plant-pathogenic bacteria compared to a 2-year (2-yr) soybean-corn rotation. A rotation experiment was established in 2013 at two research sites in Ohio and the soybean bacterial community was compared between 2-yr and 3-yr rotation treatments. Soils surrounding soybean seedlings were collected in 2018 and

2019. To study the diversity and composition of soil bacterial community, soil total DNA was extracted and the 16S rRNA gene region (V4-V5) was sequenced in the Illumina

MiSeq platform. The sequence data was analyzed with bioinformatics tools to assign sequence taxonomy and compare these sequences between rotation treatments. Results

showed that the bacterial communities were different across sites. Overall, the diversity of soil bacteria communities at soybean seedling stage, was not significantly different between the 2-yr and 3-yr rotation. Also, throughout this experiment at four site-year combinations, the main bacterial phyla belonged to the Proteobacteria, Acidobacteria,

Actinobacteria, Bacteroidetes, Gemmatimonadetes, Verrucomicrobia and Chloroflexi.

There were nine core bacterial sequence variants found at all sites and year, which are nitrogen and carbon cycling related taxa. Moreover, six core taxa were uniquely found in

3-yr rotation treatment but not the 2-yr rotation treatment. Even though the overall diversity and composition of bacterial communities were not different by rotation treatments, there were specific genera differentially abundant between rotations. For example, the 3-yr rotation had higher relative abundance of the genus Pseudomonas and lower relative abundance of the genus Ralstonia at each North Western and Western sites, respectively. In total, there were 17 genera at NWARS and 19 genera at WARS differentially abundant between rotation treatments. In conclusion, the bacterial community in 2-yr and 3-yr treatment had same diversity and major phyla, but 2-yr and

3-yr rotation had different core ASVs and certain genera were differentially abundant in

2-yr and 3-yr rotations.

Introduction

The soil environment, including its parent material, chemical composition, moisture, and pH, affect the diversity and abundance of soil microbial communities (Doran and

Zeiss 2000), or the soil microbiome. The soil microbiome is defined as a community of microorganisms present and interacting with each other in the soil environment

(Marchesi and Ravel 2015). The soil microbiome carries out key ecosystem functions in plant growth by promoting cycling of carbon (C) and nitrogen (N), nutrient transport and disease suppression (Jansson and Hofmockel 2018). The beneficial soil microbial community includes mycorrhizal fungi, nitrogen-fixing bacteria, and plant growth- promoting rhizobacteria (PGPR), which enhance crop productivity and provide stress resistance (Noah Fierer 2017) through different mechanisms.

Soil is a favorable niche for bacteria, and the soil bacterial density can reach up to 106 to 107 cells cm-2 (Lakshmanan, Selvaraj, and Bais 2014). At the same time, plant root exudates recruit different cohorts of soil bacteria (Haichar et al. 2008). Soil bacteria contribute to soil health and plant growth. For example, bacteria within the genus

Rhizobium, can interact with the plant roots to form nodules in legumes and transform the atmospheric nitrogen to fixed nitrogen for plant utilization (Odee et al. 2002). Also, bacteria such as Bacillus spp. and Pseudomonas spp. produce antifungal compounds in the rhizosphere which can suppress the fungal pathogen and reduce crop disease (Swain and Ray 2009; Maurhofer et al. 2004). Due to the important roles of bacterial community in plant and soil health, studying its composition and diversity is key to improving agricultural sustainability.

Over the last decade, the ability to survey and characterize microbial communities in the environment has improved largely due to the development and reduction in price of

high-throughput sequencing platforms (van Elsas and Boersma 2011). The 16S ribosomal

RNA genes are widely used as marker genes for studies of bacterial diversity because it exists universally and is highly conserved in bacteria species, and also has variable regions (Poretsky et al. 2014). Research has found that the soil bacterial communities are different across diverse biogeography, ecological processes, soil types and soil pH

(Baogang Zhang et al. 2018; N. Fierer and Jackson 2006). In agricultural farmland, the soil bacterial community could also be affected by cropping practices, such as crop rotation and tillage intensity (K. Hartman et al. 2018; Wattenburger, Halverson, and

Hofmockel 2019). Also, the fertilizer or herbicide applications and plant genotypes could impact the soil bacterial community (Ai et al. 2018; Newman et al. 2016; Liu et al. 2019).

Understanding how the soil microbiome are affected by plant, chemical applications and cropping practices could improve the agricultural production system (Lakshmanan,

Selvaraj, and Bais 2014).

Plants can influence soil and rhizosphere microbial communities by secreting various metabolites as root exudates (Sugiyama 2019). Crop rotation increases the diversity of root exudates, through the presence of different plants species in a temporal sequence, which then affect the activity and diversity of the soil microbiome (Ai et al. 2018; Bin

Zhang et al. 2014). Recent evidence indicates that having more than one crop in a rotation increases soil metabolic activity and improves plant growth (McDaniel,

Tiemann, and Grandy 2014b). The soil metabolic activity includes soil microbes producing cellulose-degrading enzymes (e.g. b-glucosidase) and lignin-degrading

enzymes (e.g.DyP-type peroxidases and laccases) to decompose the soil organic residues into plant usable nitrogen (Bin Zhang et al. 2014; de Gonzalo et al. 2016). Similarly,

D’Acunto and Andrade (2018), observed that a four species crop rotation (pea-corn- wheat-soybean) presented the highest soil metabolic diversity and activity along with changes in plant biomass and soil pH compared to soybean-wheat or barley-wheat- soybean rotation. Higher microbial activity has been correlated with higher plant nutrient uptake from the soil and increased nitrogen(N) and phosphorus (P) use efficiency (R. L.

Anderson 2017).

Crop rotational diversity also impacts soil microbial diversity. A recent meta-analysis analyzed results of crop rotations effects on soil microbial diversity compared to monoculture from 20 studies. This meta-analysis concluded that a higher crop diversity

(three or more crops in the rotation) had greater soil microbial richness (+15.11%) and diversity (+3.36%) compared to monoculture (Venter, Jacobs, and Hawkins 2016). Crop rotation can also increase the richness of plant growth promoting rhizobacteria (PGPR) such as nitrogen-fixing bacteria (Dias et al., 2015). For example, a field under a nine- year crop rotation sequence had higher counts of Azotobacter spp., a nitrogen-fixing bacteria, compared to a 2-yr rotation field (Mikanová, Friedlová, and Šimon 2009).

Another mechanism through which crop rotational diversity impacts crop species and microbial community composition, is through its impact in soil-borne pathogens. Adding a non-host crop into crop rotation will break the soil-borne disease cycle and reduce soil- borne pathogen populations (Ratnadass et al. 2012). Thus, the application of higher crop

rotational diversity can change the soil microbial community towards a more diverse and functional community to support crop growth.

In this study we aim to determine the impact of crop rotation in soil bacterial diversity in soybean production. For this, a field experiment evaluating the impact of soybean- wheat-corn rotation compared to soybean-corn was sampled. The field experiment was established in 2013 at two research sites in Ohio and the bacterial community data was collected during the 2018 and 2019 growing seasons. Detailed information about the experimental sites is described in Chapter 2. For the 2018 and 2019 growing season, a trend of higher soybean yield, lower yield stability and soil health were detected in the 3- yr rotation treatment compared to the 2-yr rotation treatment. We hypothesized that these soybean and soil health benefits were related to the underground soybean bacterial community responding to the 3-yr rotation.

The specific objectives and hypotheses are to:

Objective 1. Determine 3-yr crop rotation effect on soil bacterial diversity compared to 2-yr crop rotation.

Hypothesis 1. Soil bacterial diversity will be higher in the 3-yr rotation treatment than the 2-yr rotation treatment, regardless of site and year.

Objective 2. Determine 3-yr crop rotation effect on soil bacterial composition compared to 2-yr crop rotation.

Hypothesis 2. Soil bacterial community will have more beneficial bacteria and less pathogenic bacteria in the 3-yr rotation treatment than the 2-yr rotation treatment.

Methods and materials

Sample collection

The field experiment, coordinated by Dr. Laura Lindsey from the Department of

Horticulture and Crop Science at The Ohio State University, was established in 2013 at

NWARS and WARS from the OARDC. The experimental treatments were 2-yr soybean- corn rotation and 3-yr soybean-wheat-corn rotation (Table 2.2). The plots at each field site were established as a randomized complete block design, with each crop within the rotation sequence being present each year, and with four replicate plots per treatment

(Table 2.3). For the purpose of this dissertation, samplings were performed in 2018 and

2019 growing season, in soybean plots.

At soybean seedlings stage (V3-V5), the soybean plants and soil around the root region were sampled with a shovel at an approximate depth of 10-15 cm. Eight samples were collected in each plot and the shovels were cleaned with 70% ethanol between plots.

Each sample was kept in a plastic bag and the collected samples were kept cool in the cold box when transferring from field to the lab. Once in the lab, samples were stored at

8°C until processing. Within 48 hours after sampling, the soil samples were separated from the root tissue in a two-step process. First, large pieces of soil were removed from the root area. These soils were used for the soil health analyses described in Chapter 2.

After that, the soil closer to the root (collected after shaking the roots), as well as

rhizosphere soil (soil tightly attached to the root), were collected and mixed. These soybean soil samples represent the soybean rhizosphere and bulk soil and were stored at -

80°C. Soil samples were dried prior to DNA extraction. For this process, soils were put into sterile 2ml centrifuge tubes and dried under vacuum at 60°C for 30 minutes. The dried soil samples were then used for DNA extraction.

DNA extraction and quantification

A total of 256 DNA samples from soybean-associated soils, corresponding to two treatments, 4 plots per treatment, and 8 samples per plot for two locations in Ohio, and two growing (2018 and 2019) seasons were analyzed.

The total DNA of soil samples were extracted from 0.25 g dried soil samples. The extraction materials were provided by the SurePrepTM Soil DNA Isolation Kit (Fisher

BioReagents, NJ, USA) and the extraction process followed manufacturer recommendations. The DNA extraction procedure consisted of two main steps:

1) Preparing lysate: First, 0.25 g of soil samples, 700 μL of Lysis Solution and

100 μL of Lysis Additive were added into a 2 ml tube with beads, and then the tube was vortexed for 30 seconds at 4 M/S high speed by a high-speed benchtop homogenizer (

FASTPREP-24™ 5G, MP BIO). Second, the mixture was centrifuged, and 450 μL of supernatant was transferred into a new tube with 100 μL of Binding Solution and then incubated for 5 minutes on ice. After that, the lysate was centriifuged to pellet any protein and soil particles, and 450 μL of supernatant was transferred into a new tube with 50 μL

of OSR Solution, incubated for 5 minutes on ice and centrifuged for 2 minutes to pellet any remaining contaminants.

2) Purifying DNA from soil sample lysates: First, the supernatant from lysates (with

OSR solution) was transferred to the spin column, and DNA was bound to the membrane and washed using Wash Solutions I and II. After washing, 100 μL of Elution Buffer were added to the spin column to elute the DNA. Finally, the DNA samples were stored at -

20°C.

After extracting the DNA from soil samples and prior to amplicon sequencing, samples were treated with ribonuclease A (RNAse A) to remove any residual RNA and quantified. Removal of RNA allowed for a more accurate DNA quantification. RNAse A treatment and quantification for DNA samples from 2018 and 2019 were processed in different ways.

For 2018 DNA samples, the RNase A treatment was performed with the following procedure: 20 μL of soil DNA extract was mixed with 80 μL of TE buffer (pH

8.0) and 5 μL of RNase A (Thermofisher, 10 mg/mL), and then incubated at 37°C for 1 hour in a water bath. Next, 21 μL of 3 M sodium acetate and 577.5 μL of 100% ethanol were added to precipitate the DNA in solution. Samples were centrifuged at 4°C, at

12,000 rmp for 20 minutes to pellet the DNA. After that, the supernatant was poured off, and the pellet was washed with 70% ethanol. Finally, the DNA pellet was resuspended with 30 μL of TE buffer, and the RNA-free DNA samples were stored at -20°C.

Spectrophotometer measurements, using a Nanodrop, were used to quantify the DNA

concentration in 2018. If >10 ng/μL, the DNA was diluted with nuclease-free water into a 10 ng/μL concentration. For quality control, all the DNA samples were amplified with primers 515F and 806R in the PCR reaction (Parada, Needham, and Fuhrman 2016;

Apprill et al. 2015). The 25-μL PCR reaction contained 1X Pro GO TAQ Flexi Buffer,1.8 mM/L MgCl2, 0.2 mM/L dNTPs, 0.03 µM/L Pro GO TAQ Flexi Polymerase

(PROMEGA, USA), 0.2 µM/L 515F primer, 0.2 µM/L 806R primer, water and 2 μL

DNA template. Cycling conditions included an initial cycle of 94°C for 5 min, and 35 cycles of 94°C for 30 s, 55°C for 30 s and 72°C for 1 minute. After finishing the PCR reaction, a 1.2% gel electrophoresis for the PCR Products with a 100 bp marker was run to verify if the DNA could be amplified in the PCR reaction.

For DNA samples from 2019, the RNase A treatment was performed by RNA treatment followed by cleanup and concentration with ZR-96 Genomic DNA Clean &

ConcentratorTM-5 (ZYMO RESEARCH CROP) columns. For this process, 10 μL

RNase A were added to 50 μL DNA samples, and then 120 μL DNA Binding Buffer were added and sample mixtures were transferred to the wells in the Zymo-Spin™ I-96-

XL Plate2 provided by the kit and mounted on a Collection Plate. Cleaning and elution followed manufacturer recommendations. DNA samples were eluted in a final volume of

30 μL.

After the RNase A treatment, all 128 DNA samples from 2019 were loaded into a 1% agarose gel for quantification and quality control. Test samples were run for 45 minutes next to a GeneRuler 1kb Plus DNA ladder (Thermoscientific, 0.5μg/μL). The gel image

was taken in Kodak software and processed in ImageJ (Schneider, Rasband, and Eliceiri

2012) to quantify the DNA concentration based on the band brightness using the

GeneRuler ladder as a reference. The DNA samples that were lower than 5 ng/μL were used in a PCR reaction with primers 515F and 816R to test if the DNA could be amplified (same PCR reaction protocol as above) to evaluate amplification success under low DNA concentration.

Sample sequencing

In 2018, the following samples were submitted to the Molecular and Cellular Imaging

Center (MCIC, OARDC) for 16S amplicon sequencing: 127 DNA samples, 6 negative controls (TE buffer, water) and 8 positive controls (commercial community

(ZymoBIOMICS Microbial Community Standards, USA), homemade community and a soil DNA technical replicate (sample from another experiment in the lab)). In 2019, the following samples were submitted to the Molecular and Cellular Imaging Center (MCIC,

OARDC) for 16S amplicon sequencing: 128 DNA samples, 2 negative controls (TE buffer, water), 5 positive controls (commercial community, homemade community and soil DNA sample from another experiment in the lab, one technical replicate from 2018 rotation experiment and one technical replicate from 2019 rotation experiment ), and 9 repeated samples from 2018 rotation experiment, which had low sequencing reads when sequenced in 2018.

At MCIC, the DNA samples were sequenced based on bacterial ribosomal markers.

The 16S ribosomal RNA genes were used as genetic marker because they exist universally in all bacteria, are highly conserved in most bacterial species, and have multiple variable regions, which are informative for taxa identification (Case et al. 2007).

The primers used in the amplification were 515F and 806R (Parada, Needham, and

Fuhrman 2016; Apprill et al. 2015), which are also used in the Earth microbiome project

(EMP; http://www.earthmicrobiome.org/emp-standard-protocols/16s/). These primers targeted the 16S ribosomal RNA V4-V5 with an expected amplicon size of 400 bp. The amplicon library preparation was performed using a two-step amplification protocol. The individual samples were indexed using a double-indexing approach. Samples were pooled in equimolar concentration, and the amplicon libraries were sequenced in the

Illumina MiSeq 2x300 sequencing platform with one run for 2018 and 2019 samples, respectively.

Sequencing data analysis

Individual fastq files for the sequencing runs of 2018 and 2019 DNA samples were downloaded from Illumina BaseSpace. The processing of these sequencing data included three major bioinformatic steps: 1) preprocessing to remove primers and adapters and sequence quality control; 2) filtering, denosing, merging pairs, chimera checking and assigning of amplicon sequence variants (ASVs) using the DADA2 pipeline (Version 1.14.1) (Callahan et al. 2016); 3) using phyloseq (Version 1.22.3)

(McMurdie and Holmes 2013) to create a metadata object for analysis, which included the taxonomic assignment of ASVs data, sample treatment information, sample-ASV table, ASV-DNA sequence table. This metadata object was analyzed and graphically displayed to show complex sequencing data between different samples. Further, metagenomeSeq package (Version 1.28.2) was used to normalize data (Paulson et al.

2013) based on the cumulative sum scaling (CSS) approach. Other R packages were also used to analyze or visualize sequencing data, such as the microbiome package (Version

3.6) for core ASV analysis (Leo Lahti 2019), and the ampvis2 package (Version 2.3.3) to visualize the most abundant taxa (Andersen et al. 2018). A graphical summary of the different pipelines used in this work is shown in Figure 3.1, and the numbers of sequencing reads and ASVs at each different step in DADA2 and other processing pipelines were recorded in Table 3.1 and Table 3.2. Specific details for each step are provided below.

a) Filtering and trimming primers and adapters in command line:

The sequencing data from 2018 and 2019 were preprocessed to remove primers and adapters separately. The downloaded files were unzipped into individual fastq files, and then specific primers used for sequencing were searched as an initial sequencing quality control. For this, the “search_oligodb” command from

USEARCH(https://www.drive5.com/usearch/) (Edgar 2010) was used to search the gene specific primers (515F/806R) in each sequence. 95-100% of the reads in 2018 and 2019

had the primers. The primer and adapter sequences were then removed using cutadapt

(Martin 2011). When finishing these preprocessing steps, the sequencing files from 2018 and 2019 were pooled into one folder for the rest of the bioinformatic workflow. The numbers of sequencing reads at each of the processing steps were recorded in Table 3.1.

b) Inspecting quality, denosing, removing chimeras and assigning taxonomy in

DADA2 pipeline:

After trimming the primers and adapters, the Illumina MiSeq reads were processed through the DADA2 pipeline (Callahan et al. 2016) in R (Version R 3.4.1) to remove low quality reads, denosing, chimera removal and taxonomic assignment. The sequences were processed through quality inspection, trimming, denoising, paired merging, and chimera removal. The aim for quality control is the removal of DNA sequences that have unexpected length, long homopolymers, ambiguous bases, do not align to the correct 16S rRNA gene region and chimeras generated during PCR (Schloss,

Gevers, and Westcott 2011; Callahan et al. 2016). The total reads at each step through the pipeline are shown in Table 3.1. After quality control and trimming, all sequences are clustered to amplicon sequence variants (ASVs) based on single-nucleotide differences

(Callahan, McMurdie, and Holmes 2017). In DADA2 true biological sequence are compared within samples to sequence variants at one nucleotide level (Callahan,

McMurdie, and Holmes 2017). Taxonomy of ASVs was assigned by matching sequences against the SILVA 16S rRNA gene reference database (Version 132) (Quast et al. 2013),

with 100% identity. As a result of the DADA2 pipeline, a sample by ASV table, an ASV- taxonomy table, and ASV-DNA sequences were generated and imported into phyloseq to create the metadata framework for statistical analysis.

c) Importing, analyzing, and graphically displaying bacterial sequencing

data using phyloseq and other packages:

The phyloseq R package was used to import and create the metadata framework

(the phyloseq object), analyze diversity and composition of bacterial community between samples and display the results of sequence analysis. The first step of the phyloseq pipeline is creating the metadata framework which combine sample-ASV table, ASV- taxonomy table, and ASV-DNA sequences generated from the DADA2 pipeline with the sample information table. Specific details of data analysis and display using phyloseq are described in the following eight steps:

(1) Removing undesired samples and contaminant ASVs

Multiple filtering steps were applied to remove undesired samples and ASVs from the phyloseq object. First, non-bacterial ASVs were filtered from the dataset (i.e. ASVs assigned to Chloroplast which comes from plant, Mitochondria, other eukaryotic sequences, and undefined taxa). Second, through analyzing sequences from negative and positive controls, control samples and some ASVs were filtered out from the dataset based on the following description. The positive control samples had the expected ASV

results, which indicated that the sequencing process was accurate. Through comparing the ASVs in the positive samples (Commercial community, homemade community and soil DNA sample from another experiment in the lab), the positive samples had similar

ASVs results from the 2018 sequencing and 2019 sequencing process. The consistent sequencing results of positive control samples implied the reliability of 2018 and 2019 sequencing process. Also, for the commercial community sample, there was no difference between the ASV results and its known community composition. Thus, all the three positive control samples including commercial, homemade and one soil sample in the lab, had the expected ASVs, and were different from the rotation experiment samples (and

ASVs), and these positive samples were deleted from the phyloseq object. Third, the negative control samples were checked and found six contaminant ASVs and then were filtered out of the phyloseq object. The evidence for defining these six ASV as contaminants was the following: there were no ASVs recovered in the 2018 negative control samples, but there were six ASVs which had 69~7296 reads in the 2019 negative control sample (TE buffer from DNA extraction kit). From these, six ASVs in the 2019

TE buffer sample did not show in any of the samples from 2018, but these ASVs were present in up to 90% of 2019 samples. Based on the inconsistent results of these ASVs, these six ASVs found in the 2019 negative control sample were defined as contaminant

ASVs and filtered out from the phyloseq object for the downstream analysis of diversity and composition in bacterial community.

(2) Estimating diversity within each rotation sample (Alpha diversity)

The bacterial diversity differences between crop rotation treatments were examined by estimating sample alpha diversity indices including Observed richness, Shannon and

Inverse Simpson. These three alpha diversity indices are commonly used in microbiome studies (e.g. Hussain et al. (2018) and Weisleitner et al. (2019)). Observed richness is the total number of different ASVs present in a sample (Wagner et al. 2018); Shannon diversity measures how evenly the microbes are distributed in a sample which equally weighs richness and evenness (Wagner et al. 2018); Inverse Simpson is the inverse of the

Simpson test, and it indicates species dominance and reflects the probability of two individuals that belong to the same species being randomly chosen (Lemos et al. 2011).

Before estimating diversity, nine samples with a low number of ASVs (<100) which represent low quality DNA samples were filtered out of the phyloseq object. The

“estimate_richness” function was used under the phyloseq package to calculate sample alpha diversity. Analysis of variance was used in R to compare the mean differences of alpha diversity indices among rotation treatments. For this analysis, the alpha diversity values of the eight samples in each plot per treatment were averaged with n=4 plots per treatment (and 8 subsamples per plot). The function “anova()” from R base was used to compute one-way ANOVA test, as described in Chapter 2. The averaged alpha diversity in 2-yr rotation and 3-yr rotation at four site-year combinations is shown in Table 3.3.

(3) Data normalization for comparison between samples

In order to compare the bacterial community composition and relative abundance of individual taxa across treatments, the number of sequences in each sample was normalized. Normalizing the sequencing data from different samples can eliminate the effect of uneven sampling depth caused by variations in sample collection, library preparation and sequencing process (Weiss et al. 2017). Before the normalization, the extremely low abundant ASVs which were only present in one sample were filtered out from the phyloseq object. The MetagenomeSeq package in R was used for normalization, applying the cumulative sum scaling (CSS) normalization method (Paulson et al. 2013).

In CSS, the within-sample variance across samples are standardizing and force each sample to have the same distribution of reads (McKnight et al. 2019).

(4) Relative abundance of phyla across samples and treatments

In order to compare the composition of bacteria community in each rotation treatment, the relative abundance of bacterial phyla at the 2-yr and 3-yr rotation samples in four site-year combinations were investigated. Before the abundance analysis, low abundant ASVs with a relative abundance < 5% of sequencing reads were further removed from the phyloseq object. To plot the relative abundance of each phylum at 2-yr and 3-yr rotation all ASVs were first merged at the phylum level by using “tax_glom” function. Then, samples were grouped by rotation treatment by using the

“merge_samples” function. After that, the normalized ASV counts were transformed into percentage by using “transform_sample_counts” function. The “plot_bar” function was used to graphically display the relative abundance of each phylum in the 2-yr and 3-yr rotation samples analyzed by site and year. The relative abundance of each phylum was also calculated by “levels” function in phyloseq package, and all phyla’s relative abundance in rotations are shown in Table 3.4.

(5) Core ASVs across all rotation samples

In agricultural systems, the core microbiome refers to the set of microorganisms that are consistently found across plant-associated microbial communities (Toju et al. 2018).

The existence of a core microbiome in soil, or in association to plants, suggests that these might carry essential microbes that potentially promote growth and survival (Kim and

Lee 2019). Thus, determining the core microbiome, or the core ASVs between the 2-yr and 3-yr rotation treatments at NWARS and WARS in 2018 and 2019 is important to know how soybean soil bacteria interacts with soybean growing in diverse ecosystems.

In this study, the core microbiome is defined as ASVs that were detected in >90% of the samples. The Microbiome package (Leo Lahti 2019) was loaded and the “core_members” function was used to obtain the core ASVs in 2-yr and 3-yr rotation samples at NWARS and WARS. After having the core ASVs in NWARS-2yr, NWARS-3yr, WARS-2yr and

WARS-3yr, a Venn diagram was created to detect the common core ASVs between rotation groups (Oliveros 2007-2015) using the Venny tool (Version 2.1).

(6) Estimating bacterial community composition between rotation samples

In order to compare how community composition vary between 2-yr and 3-yr rotation, differences in bacterial communities were estimated using multivariate analyses.

Ordination plots were generated from the normalized and filtered phyloseq object from step (4) using “plot_ordination” function in phyloseq package. Specifically, ordination plots were generated based on non-metric multidimensional scaling (NMDS) of the Bray-

Curtis similarities between individual samples analyzed per site and year. Also, the effects of sample rotation treatment and site on the bacterial community Bray-Curtis similarities was tested following a non-parametric permutational multivariate analysis of variance (PERMANOVA) (M. J. Anderson 2017), implemented through the “adonis” function in the vegan package (Oksanen et al. 2017) with 999 permutations.

(7) Visualizing the most abundant taxa/ASVs

In order to compare the dissimilarity between bacterial communities at each site, the ten most abundant ASVs were investigated at NWARS and WARS each year. For this, the ampvis2 package (Version 2.5.9) (Andersen et al. 2018) was loaded and the

“amp_boxplot” function was used to visualize the abundance of the most abundant ASV in 2-yr rotation and 3-yr rotation at two sites.

(8) Differential abundance testing of genera between rotation samples

In order to detect which genera were affected by the rotation treatments, a fisher test adjusted for multiple comparisons, and implemented through phyloseq “mt” command was used. For this, the analyses were performed on the data corresponding to each year and site (NWARS-2018, NWARS-2019, WARS-2018,WARS-2019). Then, within each site-year dataset, subsamples per plot were merged using the

“merge_samples” function. After that, ASVs were merged to genus level by using the

“tax_glom” function. Finally, the “mt” function (McMurdie and Holmes 2013) was used to test differential abundance at the genus level between the 2-yr and 3-yr rotation at each site-year combination The genera differently abundant between rotation treatments at p

<0.05 were recorded, and the abundance of these genera were calculated by plot and then averaged by rotation treatment.

Results

Description of sequencing data and quality

A summary of sequencing reads, ASVs and filtered data in this work are recorded in Table 3.1 and Table 3.2. The total raw reads for the 2018 samples were 13,382,635 and after removing chimeras and merging paired reads, the average reads for the 2018 samples were 33,235/sample. The total raw reads for the 2019 samples were 22,772,032

and after removing chimeras and merging paired reads, the average reads for the 2019 samples was 50,788/sample. There were a total of 30,547 Bacteria and Archaea ASVs.

The number of ASVs after decontamination, removal of low reads samples and low

(<5%) prevalence ASVs was 4,446.

Bacterial diversity and community structure across sites, treatments and years

There was no significant difference observed in bacterial richness, Shannon diversity index and Inverse Simpson diversity index between 2-yr rotation samples and 3- yr rotation samples at NWARS and WARS in 2018 and 2019 (Table 3.3)

No significant differences were observed in bacterial community composition, as evaluated through non-metric multidimensional scaling (NMDS) ordination and

PERMANOVA between 2-yr rotation and 3-yr rotation at each site and year (Figures 3.2 and 3.3). However, there was a significant separation (PERMANOVA, P=0.001) of bacterial communities between NWARS and WARS each year of study (Figure 3.2). The bacterial communities of 2-yr and 3-yr samples from the same site were similar, but the samples in 2018 had greater variation across the same treatment than samples in 2019

(Figure 3.3).

The relative abundance of bacterial phyla between rotation samples

The relative abundance at the phylum level of bacteria found in the 2-yr rotation samples and the 3-yr rotation samples at NWARS and WARS in 2018 and 2019 is shown

in Figures 3.4 and 3.5. The 2-yr and 3-yr rotation samples had common groups of phyla at NWARS and WARS in 2018 and 2019. Also, there was no significant difference

(p>0.05) in the relative abundance of major phyla between 2-yr rotation and 3-yr rotation samples. The major phyla present on these samples were Proteobacteria, Acidobacteria,

Actinobacteria, Bacteroidetes, Gemmatimonadetes, Verrucomicrobia and Chloroflexi.

The relative abundance of these phyla in the 2-yr and 3-yr rotation at each site-year combination is shown in Table 3.4. Overall, at the phylum level, there were around 30%-

35% Proteobacteria, 16%-20% Acidobacteria, 11%-15% Actinobacteria, 5%-10%

Bacteroidetes, 4%-10% Gemmatimonadetes, 5%-8% Verrucomicrobia and 5%-8%

Chloroflexi. Other phyla present at lower abundance includes Planctomycetes,

Nitrospirae, Spirochaetes, among others.

Core ASVs across all samples

The core ASVs (ASV presented in at least 90% samples) are the ASV presented within rotation treatments at NWARS and WARS in 2018 and 2019. 33 core ASVs were found in NWARS 2-yr rotation samples, 39 core ASVs in NWARS 3-yr rotation samples,

23 core ASVs in WARS 2-yr rotation samples and 75 core ASVs in WARS 3-yr rotation samples. According to the Venn diagram (Figure 3.6), there were nine core ASVs in all the samples and their taxonomic information is shown in Table 3.5.

Moreover, there were 11 core ASVs when comparing the 2-yr rotation across two sites, and 23 core ASVs in the 3-yr rotation samples across two sites. Furthermore, a

unique set of six ASVs were found in 3-yr rotation samples only (Table 3.6), and include the taxa Skermanella, Nocardioides and Gemmatimonadetes.

The most abundant bacterial ASVs

The most abundant (top 10) ASVs in the 2-yr rotation and 3-yr rotation samples at

NWARS and WARS in 2018 and 2019 are shown in Figures 3.7 and 3.8. At NWARS, there were eight common ASVs in the 10 most abundant ASVs in 2018 and 2019 (Figure

3.7). Other than these eight common ASVs, in 2018, the other 2 ASVs belonged to the genus Acidibacter (phylum Proteobacteria) and Gaiella (phylum Actinobacteria); and in

2019, the other two different ASVs belonged to genus Sphingomonas (phylum

Proteobacteria) and Terrimonas (phylum Bacteroidetes).

At WARS, there were eight common ASVs among the ten most abundant taxa in

2018 and 2019 (Figure 3.8). Other than these eight common ASVs, in 2018, the members of the genera Nitrospira (Phylum Nitrospirae) and Chthoniobacter (phylum

Verrucomicrobia) were abundant; and in 2019, the other two abundant taxa belong to the

Pedosphaeraceaefamily (phylum Verrucomicrobia) and the genus Pseudolabrys (phylum

Proteobacteria).

Differential abundant genera in 2-yr rotation and 3-yr rotation samples

A subset of genera were differentially abundant between the 2-yr rotation and 3-yr rotation samples at NWARS (17 genera) and WARS (19 genera) for each 2018 and 2019

(Tables 3.7 and 3.8). Most of these genera belong to the phyla Proteobacteria,

Actinobacteria, Acidobacteria, and Verrucomicrobia. The genera differentially abundant were different across NWARS and WARS in 2018 and 2019.

There were two genera showing differential abundance between the rotation treatments in more than one site’s or year’s samples. They are Pseudomonas and

Ralstonia. NWARS and WARS in 2018 had higher abundance of Pseudomonas in the 3- yr than 2-yr rotation (183% and 73% respectively) (Tables 3.7 and 3.8). At NWARS in

2018 and WARS in 2019, the 3-yr rotation sample had lower abundance of the genus

Ralstonia than the 2-yr rotation samples (1437% and 1643% respectively). (Tables 3.7 and 3.8). Also, the relative abundance of genus Pseudomonas (the only genus recovered from the family Pseudomonadaeceae) within class Gammaproteobacteria was higher in 3- yr rotation compared to 2-yr rotation at NWARS and WARS in 2018 (Figure 3.9). The relative abundance of genus Ralstonia within family Burkholderiaceae was lower in 3-yr rotation compared to 2-yr rotation at NWARS in 2018 and at WARS in 2019 (Figure

3.10).

Discussion

Higher crop rotational diversity could change the soil bacterial diversity and composition compared to lower crop rotational diversity (Venter et al. (2016) and Dias et al. (2015)). In turn, the soil bacterial community can contribute to the crop growth and soil health (Philippot et al. 2013). In this project, the soil bacterial communities at 3-yr

rotation of soybean-wheat-corn was compared to 2-yr rotation of soybean-corn. This study was performed at two sites during two growing seasons, which provides the opportunity to evaluate the consistency of soybean-associated bacterial community responses to rotation treatment across sites, years and environmental conditions.

Sampling site was the strongest driver of bacterial community composition in this study. The NMDS ordination analysis, coupled with PERMANOVA, showed the differences in bacterial community composition between NWARS and WARS (Figure

3.2). Also, the analysis of core microbiome and the most abundant ASVs revealed differences between sites. Moreover, at each site different genera were differentially abundant in response to rotation treatment. Thus, NWARS and WARS soybean field had different bacterial community composition and also responded differently to 2-yr and 3- yr rotations. The site effect on bacterial community composition has also been found in other studies. For example, Zhang et al (2018) showed that the soybean soil bacteria community composition was highly related to different environmental and spatial variables such as climatic and latitude variables. In this study, NWARS and WARS had different latitude, precipitation and temperature which could cause the difference in their bacterial community. Other than these variables, the soil type, soil moisture, dry- rewetting stress and soil nutrient content can also cause the different bacterial community under diverse soil environments (Preece et al. 2019; Evans and Wallenstein 2012; Xun et al. 2015). NWARS and WARS have different soil types, Hoytville silty clay loam and

Kokomo silty clay loam, respectively. NWARS soil is poorly drained which caused

moisture stress compared to WARS. Also, WARS had higher total carbon, total N and active carbon compared to NWARS (Table 2.8). Thus, the differences in soil characteristics and nutrient content likely cause differences in bacterial communities between two sites.

No differences were detected in diversity and overall community composition between rotation treatments at both sites. We hypothesized that the 3-yr rotation could increase the soil bacterial diversity compared to 2-yr rotation because diverse root exudates from the wheat crop could result in recruiting of a different bacterial community

(Zhalnina et al. 2018). However, based on the different diversity indices applied in this study, there was no significant difference between 2-yr and 3-yr rotation at NWARS and

WARS in 2018 and 2019. At the phylum level, the major taxa recovered in both rotation treatments were Proteobacteria, Acidobacteria, Actinobacteria, Bacteroidetes,

Gemmatimonadetes, Verrucomicrobia and Chloroflexi. The presence and proportion of these phyla in the studied soil samples are similar to other studies results and

Proteobacteria is the most abundant phylum in soils (Hussain et al. 2018; Zhang et al.

2018).

One reason that could explain why no difference in diversity between 2-yr and 3- yr rotation was detected, could be that the soil analyzed in this study is a mixture of bulk soil and rhizosphere soil. Rhizosphere is the narrow zone of soil that surrounding the plant root, and rhizosphere is greatly influenced by root secretions (Berendsen, Pieterse, and Bakker 2012). Bulk soil is away from the root and its exudates, thus the bacterial

diversity in bulk soil is less affected by diverse root exudates compared to rhizosphere

(Zhang et al. 2018). Previous studies found that soybean rhizosphere communities changed significantly during soybean growth compared to bulk soil communities. For example, plant growth promoting rhizobacteria (Bacillus, Bradyrhizobium and

Rhizobium) increased in rhizosphere soil compared to bulk soil (Bianchi et al. 2019).

Thus, in this study the mixture of rhizosphere and bulk soil made it hard to detect the response of soybean root associated bacterial community to rotation treatments.

In this study, for most of site-year combinations soil carbon and nitrogen content did not differ between 2-yr and 3-yr rotations. Soil carbon and nitrogen content impact soil bacterial diversity. For instance, Qian et al. (2015) found that the soil bacterial diversity was positively correlated to soil total organic carbon and total nitrogen in apple orchard soils. Therefore, the lack of differentiation in bacterial communities could be related to the lack of difference between major nutrients in soil. Even though most of the measured soil nutrients did not respond to rotation treatments, a difference was observed in soil active carbon, with greater POXC in the 3-yr rotation soils in in 2019. This suggests that by 2019 the soil changes induced by rotation were measurable. Therefore, the effects of 3-yr rotation on soil bacterial community diversity could potentially be detected in the future growing seasons. Venter et al. (2016) suggested that study trails longer than 15 years can produce larger increases in microbial richness in higher crop rotational soil. In this trial, the treatments have been in place for six years therefore these number of years might not be enough to detect differences in these two sites.

Nine core ASVs were present at both 2-yr rotation and 3-yr rotation across four site- year combinations. These ASVs were classified as genus Bradyrhizobium, Gaiella and

Bryobacter, family Nitrosomonadaceae, Chthoniobacteraceae, Burkholderiaceae and order Betaproteobacteriales, Rokubacteriales and Gaiellales. Some of these ASVs belong to taxa with potential roles in nitrogen and carbon cycling. For example, Bradyrhizobium is a genus of nitrogen-fixing bacteria (Wadisirisuk et al. 1989), and Bradyrhizobium japonicum is a nitrogen-fixing bacterial species that forms root nodules specifically on soybean (Mullen, Israel, and Wollum 1988). Also, genera of Bradyrhizobium are known to commonly interact with soybean rhizosphere (Meena et al. 2018) and form nodules, thus Bradyrhizobium in this study is an important core ASV, as expected. Bacteria in the family Nitrosomonadaceae can oxidize ammonia to nitrite (Prosser, Head, and Stein

2014); Rokubacteriales play roles in nitrogen respiration (Becraft et al. 2017); Gaiella can reduce nitrogen and perform CO2 fixation (Severino et al. 2019); Bryobacter was reported as CO2 cycling (Yu et al. 2016). Thus, soybean can recruit taxa important for nitrogen and carbon processes across different sites and years. However, through the correlation analysis between soybean productivity, soil health and these core ASVs abundance, no consistent and clear correlation were found between core microbiome and other soybean or soil parameters. Further studies will determine the importance of these

ASVs in 3-yr rotation and soybean production.

Specific bacterial ASVs were unique in the 3-yr rotation compared to the 2-yr rotation. The 3-yr rotation had six unique core ASVs compared to the 2-yr rotation. In

this study, the six unique ASVs in the 3-yr rotation were classified as genus of

Skermanella,Nocardioides , Aetherobacter; family of Pedosphaeraceae and

Gemmatimonadaceae; phylum of Acidobacteria. Bacteria within these genera have been described soil inhabitant in other ecosystems. For example, soil bacteria of the family

Gemmatimonadaceae have been found in cotton soil with constant and low moisture

(Fawaz 2013). Also, bacteria from the genus Skermanella have been described for their potential for biological control activity against insect pests (Panneerselvam et al. 2018).

And Nocardioides are soil dwelling bacteria with the ability to degrade atrazine based herbicides (Topp et al. 2000)). The functions of the core bacteria in the 3-yr rotation is still unknown.

Thirty-six bacterial genera were differentially abundant between the 2-yr and 3-yr rotation treatment. Although, each site-year combination had different genera differentially abundant per rotation treatment, there were two ASVs which responded consistently to rotation across multiple sites and years, Pseudomonas and Ralstonia. The genus Pseudomonas had higher abundance in 3-yr rotation treatment at two sites, and

Pseudomonas are commonly found in soybean soils with plant pathogenic and beneficial species (Kuklinsky‐Sobral et al. 2004). Plant pathogenic Pseudomonas, such as

Pseudomonas syringae cause important diseases on a variety of crops and symptoms include cankers, leaf and stem spots, blight, and it could cause soybean bacterial blight disease (Höfte and De Vos 2006). Also, certain species of Pseudomonas are known for their beneficial activities, including species that could produce antibiotics such as 2,4-

diacetylphloroglucinol(DAPG), pyrrolnitrin (PRN), Hydrogen Cyanide(HCN) (Validov et al. 2005) to inhibit fungal activity. For example, Pseudomonas fluorescens was reported as antagonistic to Rhizoctonia solani and Pythuim oryzae which cause seedling damping off disease (Saharan and Nehra 2011). In addition, beneficial Pseudomonas can be recovered from wheat rhizosphere (Okubara and Bonsall 2008; Maurhofer et al. 2004).

Thus, adding wheat into soybean-corn rotation could potentially increase the populations of beneficial Pseudomonas in 3-yr rotation bacterial community compared to 2-yr rotation. Peralta et al. (2018), recovered greater abundance of DAPG-producing bacteria in soybean-wheat-corn rotations compare to only corn or corn-soybean rotation, and associated with potentially improved soil disease suppression. Future analyses are required to test for functionality of Pseudomonas in the 3-yr rotation. For instance, qPCR test on certain beneficial Pseudomonas group is suggested to test the hypothesis that the

Pseudomnas in the 3-yr rotation have characteristics which could promote plant health.

The genus Ralstonia had lower abundance in the 3-yr rotation treatment at two sites. Within the genus Ralstonia, Ralstonia solanacearum is a soil-borne pathogen that causes vascular wilt disease in more than 200 crop species, including tomato, potato, tobacco (Adhikari and Basnyat 1998), and soybean. Ralstonia solanacearum can cause soybean wilt disease but it is uncommon in soybean production (Harveson and Vidaver

2007). Also, corn and wheat have not been reported as the host of Ralstonia solanacearum. Other research has proposed the use of corn and wheat in crop rotation as a management strategy to reduce the population of Ralstonia solanacearum (Yuliar,

Nion, and Toyota 2015; Adhikari and Basnyat 1998; Terblanche and de Villiers 1998).

For example, corn-tomato rotation could reduce tomato wilt severity, and potato-wheat- corn and other four species rotation could reduce the incidence of potato wilt and increase potato yield. Therefore, in this study, 3-yr soybean-wheat-corn rotation had less abundant

Ralstonia than 2-yr soybean-corn rotation, which indicates that higher crop rotational diversity could reduce the population of potential pathogenic Ralstonia in the soil.

In summary, in this study, site is the strongest driver of soil bacterial communities. Within a site, no differences were detected in the soybean soil bacterial community composition and common core taxa between the 2-yr and 3-yr rotation treatments. However, the 3-yr rotation treatments had a unique core microbiome and differentially abundant genera compared to 2-yr rotation. Bacteria associated with the 3- yr rotation treatment could be further studied for potential beneficial effects in soybean growth and soil health.

The overall conclusions of this research, as they relate to soybean production, soil health and soil bacterial communities are that: 1) 3-yr rotation can provide benefits to soybean seedling establishment and yield under unfavorable conditions. 2) 3-yr rotation increases soil organic matter compared to 2-yr rotation. 3) Certain group of bacteria in soybean soil had different abundance between 2-yr and 3-yr rotations which could contribute to soybean health and soil nutrient cycling. Based on these results, for soybean production and soil health, 3-yr soybean-wheat-corn rotation is recommended as a better strategy compared to 2-yr soybean-corn rotation.

Tables

Table 3.1. Summary of sequence reads per sample at each processing step for 2018 and 2019 samples1

After Quality Denoised Denoised Merged Nonchimeric Year Total reads2 Reads/sample Forward/Revers primer/adapte filtered4 F5 R6 pairs7 8 e sequence r removal3

2~10,077 Range 82~141,684 79~141,677 12~108,600 2~98,754 2~89,399 2~89,087 2018 13,382,635 0

Average 52,277 52,688 39,795 36,773 37,233 33342 33235

1~130,98 1~138,88 0~12,506 Range 96~177,757 95~177,755 6~154,490 0~124,831 2019 22,772,032 9 8 0

Average 83,110 83,110 71,259 59,149 63,262 52,452 50,788

1 Summary of sequencing reads recovered and processed for 2018 and 2019 samples

2The total reads of all samples in 2018 and all samples in 2019 before processing and filtering 3Primer and adapter removal performed in Cutadapt (Martin 2011) 4-8According to DADA2 pipeline (Callahan et al. 2016), the low-quality reads of sequences were filtered out, forward and reverse reads were denoised and merged, chimeric reads were removed 9In 2018, 127 samples were sequenced; in 2019, 137 samples were sequenced (including 9 repeated samples from 2018)

Table 3.2. Number of ASV1 at each step in the data analysis pipeline

Processing step Number of ASV

The original number after DADA2 31,444

Bacteria/Archaea 30,547

After removal of contaminant ASVs2 29,477

After filtering low reads samples and ASVs only present in 13,016 one sample3

After remove <5% prevalence ASVs 4,446

Core ASVs4 9

1 ASV: amplicon sequence variant 2 The contaminant ASVs include ASVs present in positive control samples and negative control samples 3 Low reads samples < 100 ASVs 4 The core ASVs are the ASV present in all samples from both 2-yr and 3-yr treatments in 2018 and 2019.

Observed Year Site Rotation Shannon Inverse Simpson Richness1

2-yr 1073.09 ± 139.55 6.26 ± 0.21 288.84 ± 51.64 NWARS 3-yr 1075.87 ±270.79 6.09 ± 0.58 315.99 ± 129.50 2018 2-yr 1031.60 ± 148.95 6.39 ± 0.16 402.18 ± 40.15 WARS 3-yr 1217.98 ± 231.10 6.49 ± 0.17 412.46 ± 50.79

2-yr 1073.68 ± 165.34 6.28 ± 0.18 316.50 ± 49.71 NWARS 3-yr 1040.27 ± 232.99 6.23 ± 0.19 283.69 ± 35.13 2019 WARS 2-yr 1227.56 ± 231.70 6.45 ± 0.15 367.91 ± 63.38

3-yr 1314.09 ± 239.31 6.54 ± 0.16 393.70 ± 37.93

1The alpha diversity indices includes observed richness, Shannon diversity and Inverse Simpson diversity. Observed richness is the number of different ASVs in a sample; Shannon diversity measures how evenly the bacteria are distributed in a sample; Inverse Simpson is the inverse Simpson test, and it indicates the richness in a community with uniform evenness (Wagner et al. 2018).

Year Site Rotation Proteobacteria1 Acidobacteria Actinobacteria Bacteroidetes Gemmatimonadetes Verrucomicrobia Chloroflexi Others2

2-yr 31.70 19.40 15.38 5.77 6.68 5.14 6.74 9.19 NWARS 3-yr 30.12 16.44 14.94 5.59 6.23 5.44 5.24 15.99 2018 2-yr 35.32 17.83 12.35 8.72 5.08 6.37 5.95 8.38 WARS 3-yr 35.14 17.71 11.73 9.96 4.51 7.11 5.96 7.88

2-yr 31.62 19.89 13.45 5.92 10.18 6.77 4.97 7.20 NWARS 3-yr 32.53 20.88 12.50 6.68 8.11 7.08 5.27 6.95 2019 2-yr 34.86 19.21 11.91 7.40 4.84 7.46 7.62 6.70 WARS 3-yr 35.18 20.45 10.46 6.96 4.68 8.13 5.83 8.31

1 The relative abundance of phylum Proteobacteria in bacteria community in 2-yr and 3-yr rotation. 2 Others are the sum of low relative abundant phyla including Planctomycetes, Nitrospirae, Spirochaetes etc.

Table 3.5. The taxonomy of nine core ASVs in all analyzed samples at NWARS and WARS in 2018 and 2019. These core ASVs were reported in previous studies that play important roles in soil nitrogen and carbon cycling, thus they are important core ASVs for soybean growth.

Core1 Kingdom Phylum Class Order Family Genus ASV ASV5 Bacteria Proteobacteria Alphaproteobacteria Rhizobiales Xanthobacteraceae Bradyrhizobium ASV10 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Nitrosomonadaceae mle1-7 ASV15 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales TRA3-20 NA ASV16 Bacteria Verrucomicrobia Verrucomicrobiae Chthoniobacterales Chthoniobacteraceae Candidatus_Udaeobacter ASV26 Bacteria Rokubacteria NC10 Rokubacteriales NA NA ASV29 Bacteria Proteobacteria Gammaproteobacteria Betaproteobacteriales Burkholderiaceae NA ASV33 Bacteria Actinobacteria Thermoleophilia Gaiellales Gaiellaceae Gaiella ASV36 Bacteria Actinobacteria Thermoleophilia Gaiellales NA NA ASV37 Bacteria Acidobacteria Acidobacteriia Solibacterales Solibacteraceae_(Subgroup_3) Bryobacter

1 Core ASV: ASV shows in at least 90% samples (Eyre et al. 2019).

Core1 Kingdom Phylum Class Order Family Genus ASV ASV30 Bacteria Acidobacteria Subgroup_5 NA NA NA ASV32 Bacteria Verrucomicrobia Verrucomicrobiae Pedosphaerales Pedosphaeraceae ADurb.Bin063-1 ASV53 Bacteria Proteobacteria Alphaproteobacteria Azospirillales Azospirillaceae Skermanella ASV69 Bacteria Proteobacteria Deltaproteobacteria Myxococcales Polyangiaceae Aetherobacter ASV83 Bacteria Actinobacteria Actinobacteria Propionibacteriales Nocardioidaceae Nocardioides ASV90 Bacteria Gemmatimonadetes Gemmatimonadetes Gemmatimonadales Gemmatimonadaceae NA

1 Core ASV: ASV shows in at least 90% samples (Eyre et al. 2019).

Table 3.7. 17 differentially abundant genera between 2-yr rotation and 3-yr rotation at NWARS in 2018 and 20191. Seven genera had higher abundance in 3-yr rotation; ten genera had lower abundance in 3-yr rotation compared to 2-yr rotation.

Site Year ASV Phylum Family Genus 2-yr2 3-yr ASV6178 Firmicutes Clostridiaceae_1 Clostridium 0.00 1.10 ASV3662 Proteobacteria Sphingomonadaceae Novosphingobium 1.83 0.00 ASV889 Actinobacteria Geodermatophilaceae Geodermatophilus 18.15 8.57 ASV287 Proteobacteria Pseudomonadaceae Pseudomonas 36.82 104.52 ASV114 Proteobacteria Caulobacteraceae Phenylobacterium 125.33 86.63 ASV386 Proteobacteria Polyangiaceae Pajaroellobacter 335.12 233.52 2018 ASV4868 Chloroflexi Ktedonobacteraceae 1959-1 1.92 0.00 ASV1242 Actinobacteria Nocardiaceae Williamsia 19.73 6.41 NWARS ASV1682 Proteobacteria Xanthomonadaceae Stenotrophomonas 0.48 8.15 ASV1651 Proteobacteria Acetobacteraceae Acidiphilium 26.83 1.41 ASV831 Bacteroidetes Sphingobacteriaceae Mucilaginibacter 148.36 23.21 ASV460 Proteobacteria Burkholderiaceae Ralstonia 59.66 3.88 ASV184 Proteobacteria Unknown_Family Acidibacter 297.02 424.39 ASV383 Proteobacteria Aeromonadaceae Aeromonas 3.08 0.27 2019 ASV134 Actinobacteria Micromonosporaceae Luedemannella 68.72 143.82 ASV2093 Actinobacteria Micromonosporaceae Micromonospora 1.13 8.89 ASV459 Proteobacteria Reyranellaceae Reyranella 44.60 80.34

1The differential abundance between 2-yr and 3-yr rotation was tested for each genera, and the genus differentially abundant ( P value < 0.05 in mt test) was recorded in this table 2 Averaged abundance for the 2-yr samples and 3-yr samples, N=8

Table 3.8. 19 differentially abundant genera between 2-yr rotation and 3-yr rotation at WARS in 2018 and 20191. 11 genera had higher abundance in 3-yr rotation, and eight genera had lower abundance in 3-yr rotation compared to 2-yr rotation.

Name of Site Year Phylum Family Genus 2-yr2 3-yr ASV ASV1553 Verrucomicrobia Xiphinematobacteraceae Candidatus 8.55 24.97 ASV89 Proteobacteria Pseudomonadaceae Pseudomonas 161.41 280.10 ASV878 Proteobacteria Beijerinckiaceae Methylobacterium 17.79 6.37 2018 ASV3938 Fibrobacteres Fibrobacteraceae Undefined 12.13 3.61 ASV1794 Actinobacteria Pseudonocardiaceae Kibdelosporangium 0.87 9.29 ASV435 Bacteroidetes Chitinophagaceae Parafilimonas 58.34 103.04 ASV1801 Firmicutes Paenibacillaceae Ammoniphilus 4.43 0.69 ASV460 Proteobacteria Burkholderiaceae Ralstonia 21.10 1.21 ASV666 Proteobacteria Hyphomonadaceae SWB02 32.33 58.28 WARS ASV20 Actinobacteria Micromonosporaceae Rugosimonospora 283.51 99.07 ASV2747 Proteobacteria Rhodocyclaceae Candidatus 0.00 10.54 ASV299 Proteobacteria Rhizobiaceae Undefined 69.72 103.67 2019 ASV3082 Proteobacteria Legionellaceae Legionella 0.61 7.40 ASV977 Proteobacteria Burkholderiaceae Noviherbaspirillum 12.54 24.20 ASV540 Proteobacteria Burkholderiaceae Massilia 57.78 29.88 ASV882 Actinobacteria Actinospicaceae Actinospica 36.30 0.88 ASV104 Acidobacteria Blastocatellaceae JGI_0001001-H03 135.54 285.50 ASV333 Proteobacteria Chromobacteriaceae Pseudogulbenkiania 17.22 1.42 ASV520 Verrucomicrobia Pedosphaeraceae Pedosphaera 24.49 45.20

1The differential abundance between 2-yr and 3-yr rotation was tested for each genera, and the genus differential abundant (P value < 0.05 in mt test) was recorded in this table. 2 Averaged abundance in the 2-yr samples and 3-yr samples, N=8

100

Figures

2018 2019

M N

−2

−4

−3 −2 −1 0 1 2 −3 −2 −1 0 1 2 NMDS1

Site NWARS WARS Treatment 2−yr 3−yr

102

NWARS WARS

M N

−2

−4

−3 −2 −1 0 1 2 −3 −2 −1 0 1 2 NMDS1

Year 2018 2019 Treatment 2−yr 3−yr

Figure 3.3. At each site, 2018 and 2019 samples had similar bacterial community, but 2018 samples had more variations compared to 2019 samples based NMDS ordination by treatment and year in NWARS and WARS

103

2018 2019 100 100

75 75

)

(

n u

50 u 50

R R

25 25

0 0

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r Treatment Treatment

Acidobacteria Cyanobacteria FBP Nanoarchaeaeota Spirochaetes Actinobacteria Dadabacteria FCPU426 Nitrospirae Tenericutes Armatimonadetes Deinococcus−Thermus Fibrobacteres Omnitrophicaeota Thaumarchaeota Phylum Bacteroidetes Dependentiae Firmicutes Patescibacteria Verrucomicrobia BRC1 Elusimicrobia Gemmatimonadetes Planctomycetes WPS−2 Chlamydiae Entotheonellaeota Kiritimatiellaeota Proteobacteria WS2 Chloroflexi Epsilonbacteraeota Latescibacteria Rokubacteria

Figure 3.4. The relative abundance (%) of each bacteria/archaea phyla within the bacteria community is similar in 2-yr and 3-yr rotation samples at NWARS in 2018 and 2019.

104

2018 2019 100 100

75 75

) )

% %

( (

e e

c c

n n

a a

d d

n n u

u 50 50

b b

A A

e e

v v

i i

t t

a a

l l

e e

R R

25 25

0 0

2 3 2 3

− − − −

y y y y

r r r r Treatment Treatment

Figure 3.5. The relative abundance (%) of bacteria/archaea phyla within the bacteria community is similar in 2-yr and 3-yr rotation samples at WARS in 2018 and 2019.

105

Figure 3.6. The Venn diagram of core ASVs in 2-yr and 3-yr samples from NWARS and WARS. The core ASVs presented in >90% samples in each rotation-site were recorded, and the core ASVs names in four groups were imported into Venny 2.1 website to draw the diagram (Oliveros 2007-2015). 11 core ASVs were presented in 2-yr rotation, and 23 core ASVs were presented in 3-yr rotation. Nine core ASVs were shared between four rotation-site groups, and six core ASVs were unique presented only in 3-yr rotation.

106

2018 2019

Proteobacteria; Haliangium Gemmatimonadetes; Gemmatimonas

Gemmatimonadetes; Gemmatimonas Acidobacteria; Bryobacter

Acidobacteria; Bryobacter Proteobacteria; Haliangium

Acidobacteria; Candidatus_Solibacter Verrucomicrobia; ADurb.Bin063−1

Proteobacteria; Pseudolabrys Acidobacteria; Candidatus_Solibacter

Verrucomicrobia; ADurb.Bin063−1 Verrucomicrobia; Candidatus_Udaeobacter

Proteobacteria; Ellin6067 Proteobacteria; Pseudolabrys

Proteobacteria; Acidibacter Proteobacteria; Ellin6067

Verrucomicrobia; Candidatus_Udaeobacter Proteobacteria; Sphingomonas

Actinobacteria; Gaiella Bacteroidetes; Terrimonas

0 2 4 6 8 0.0 2.5 5.0 7.5 10.0 Read Abundance (%) Read Abundance (%) Group 3−yr 2−yr

107

2018 2019

Proteobacteria; Haliangium Proteobacteria; Haliangium

Gemmatimonadetes; Gemmatimonas Gemmatimonadetes; Gemmatimonas

Acidobacteria; RB41 Verrucomicrobia; Candidatus_Udaeobacter

Verrucomicrobia; Candidatus_Udaeobacter Acidobacteria; Bryobacter

Acidobacteria; Bryobacter Verrucomicrobia; ADurb.Bin063−1

Proteobacteria; Ellin6067 Acidobacteria; RB41

Proteobacteria; Acidibacter Proteobacteria; Ellin6067

Nitrospirae; Nitrospira Proteobacteria; Acidibacter

Verrucomicrobia; Chthoniobacter Acidobacteria; Candidatus_Solibacter

Acidobacteria; Candidatus_Solibacter Proteobacteria; Pseudolabrys

0 2 4 6 0 2 4 6 Read Abundance (%) Read Abundance (%) Group 3−yr 2−yr

NWARS in 2018 WARS in 2018

100 100 )

) 75 75

% %

( (

e e

c c

n n

a a

d d

n n u

u 50 50

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t t

a a

l l

e e R R 25 25

0 0

2 3 2 3

− − − −

y y y y

r r r r Treatment Treatment

Family Moraxellaceae Pseudomonadaceae

Figure 3.9. Relative abundance of families within Class Gammaproteobacteria in 2-yr and 3-yr rotation at NWARS and WARS in 2018. Relative abundance presented as percent of normalized sequence reads. The green colored group stands for family Pseudomonadaceae. Family Pseudomonadaceae had higher relative abundance in 3-yr rotation compared to 2-yr rotation at NWARS and WARS in 2018.

109

NWARS in 2018 WARS in 2019 100 100

75 75

) )

% %

( (

e e

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y y y y

r r r r Treatment Treatment

AAP99 Castellaniella Leptothrix Pseudoduganella Variovorax Achromobacter Comamonas Massilia Ralstonia Xylophilus Actimicrobium Duganella Noviherbaspirillum Ramlibacter Genus Azohydromonas Glaciimonas Oxalicibacterium Rhizobacter Burkholderia−Caballeronia−Paraburkholderia Herminiimonas Paucibacter Rhodoferax Caenimonas Ideonella Piscinibacter Roseateles

Figure 3.10. Relative abundance of genera within family Burkholderiaceae in 2-yr and 3-yr rotation at NWARS in 2018 and WARS in 2019. Relative abundance presented as percent of normalized sequence reads. The red circle highlight the genus Ralstonia. Genus Ralstonia had lower relative abundance in 3-yr rotation compared to 2-yr rotation at NWARS in 2018 and WARS in 2019.

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