Microbial Biostimulants in Organic Farming Systems: Patterns of Current Use and an Investigation of Their Efficacy in Different Soil Environments

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree of Master of Science in the Graduate School of The Ohio State University

By

Julie Laudick

The Environmental Science Graduate Program

The Ohio State University

2017

Master’s Examination Committee:

Dr. Matthew Kleinhenz, Advisor

Dr. Enrico Bonello

Dr. Richard Dick

Dr. Michelle Jones

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Copyrighted by

Julia Ann Laudick

2017

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Abstract

Microbial biostimulants (MBS) contain microorganisms that have the capacity to promote plant growth by one or more mechanisms. Decades of research on plant- beneficial microorganisms have demonstrated their potential role in sustainable agriculture, but the extent to which they are effectively applied remains unclear. The main objectives of this study were (1) to survey the current use of MBS products in organic farming systems, (2) to review the existing literature on the performance of plant growth promoting rhizobacteria (PGPR) in different soil environments, and (3) to test the effects of commercially available MBS products on plant growth under different soil conditions in on-farm and controlled settings. Factors associated with MBS use, along with the type and frequency of MBS products, were determined over a six-year period by aggregating and analyzing data from the organic certification records of 86 Ohio organic vegetable farms. MBS products accounted for 5% of all inputs by number, and 51% of farmers reported using them. The diversity and popularity of products used in Ohio is consistent with what is available on the market nationally. Mixed inoculants were most popular, but little research-based information is available on their efficacy. Thus, trials of six MBS products were conducted at four organic farms across Ohio. The products did not produce any significant, or even numerically consistent yield increases when applied

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to tomato under normal organic nutrient management practices. An on-farm factorial experiment was conduced with broccoli as a test crop, and had the following treatments: plus and minus two kinds of organic soil amendments, and plus and minus four MBS products. MBS inoculants increased yield by 13%-65% where no soil amendments were added. In the presence of both soil amendments, no additive effects of inoculation and compost amendment were observed. In order to better understand plot-to-plot variation in response to inoculation, the yield of the non-inoculated control was used an integrative proxy for background soil quality and other factors that affect plant growth. Plotting the percent yield increase due to inoculation against the control yield revealed a clear negative linear relationship between plant growth response to inoculation and the quality of the soil environment. Similar results were obtained in a full factorial experiment testing the effects MBS inoculants on spinach growth. These results are consistent with the PGPR literature. A majority of studies found in the literature with a full factorial design of high and low soil nutrient environment with and without inoculation indicated that plant growth response to inoculation was greatest in low to intermediate soil nutrient levels. Finally, a full factorial greenhouse study was designed to test the effects of a mixed inoculant on lettuce under low and high recent and historical compost application.

The inoculant had no effect on plant growth under any of the test conditions, possibly due to low inoculum concentration. MBS products have many potential ecological and economic benefits, but more systematic research into factors that explain variability in their performance is needed for farmers to benefit from them more reliably.

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Acknowledgements

Much of this work would have been impossible without help from the Vegetable

Production Systems Lab. I am thankful to have worked with Zheng Wang, Susie Walden,

Bizhen Hu, Mahmoud Soltan, Tylar Fisk, Stephanie Short, Sonia Walker and Jenny

Moyseenko, our lab helpers, and of course Logan Walter and the OARDC Farm Crew.

My advisor, Matt Kleinhenz, has shared a wealth of experience with me and given me ample opportunities to learn in a variety of environments. I am also thankful for guidance from my committee members: Enrico Bonello, Richard Dick, and Michelle Jones. Each of you has contributed to my understanding of what it means to be a good scientist.

It’s been a joy to live in a community where I personally know the farmers who grow my food. I am especially thankful for Mary and Joseph Gnizak, Peter Boyer, and Marcus

Ladrach for keeping me well nourished. Local Roots farmer’s market has given me hope in the revival of the food system. Also, I don’t know how I would have survived long days of fieldwork and long nights in the lab without Linda’s cookies.

Finally, I’m thankful for my friends and family keeping me inspired. Fellowship with folks at St. Mary’s Church in Wooster, the OSU Newman Center in Columbus, and with fellows of the Au Sable institute for Environmental Studies has kept my faith alive.

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Vita

2010 ………….…………..…………….. Upper Arlington High School

Columbus, Ohio

2014 ………….…………..….…………. B.A. Philosophy and B.S. Biology

Emory University

Atlanta, Georgia

2014 to present …………...... ………… Graduate Fellow and Administrative Assistant

The Environmental Science Graduate Program

The Ohio State University—OARDC

Wooster, Ohio

Fields of Study

Major Field: Environmental Science

Minor Field: Agroecology

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Table of Contents Abstract ...... ii

Acknowledgements ...... iv

Vita ...... v

List of Tables ...... ix

List of Figures ...... xi

Chapter 1: Introduction ...... 1

Microbial Bioproduct Terminology ...... 2

Summary of Work ...... 6

Figures...... 7

Chapter 2: Patterns of Microbial Bioproduct Use Among Ohio Organic

Vegetable Farmers ...... 8

Abstract ...... 8

Introduction ...... 9

Materials and Methods ...... 10

Results ...... 13

Discussion ...... 16

Tables ...... 19

Figures...... 23

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Chapter 3: Literature Review of Plant Growth Promoting Rhizobacteria in

Different Soil Environments ...... 26

Abstract ...... 26

Introduction ...... 27

Interactive Effects of PGPR and Organic Soil Amendments ...... 27

A Conceptual Model for Plant Growth Response to PGPR ...... 28

Systematic studies of Azospirillum ...... 30

Summary ...... 33

Tables ...... 34

Figures...... 37

Chapter 4: Interactive Effects of Soil History, Compost Amendments, and

Microbial Biostimulant Products ...... 40

Abstract ...... 40

Introduction ...... 41

Materials and Methods ...... 43

Results ...... 49

Discussion ...... 51

Tables ...... 58

Figures...... 64

Chapter 5: Conclusions ...... 72

Tables ...... 77

Figures...... 78

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References ...... 79

Appendix A: The Effects of Microbial Biostimulants on Organic Tomato

Growth and Yield Under Normal Fertility Management Practices ...... 88

Objective ...... 88

Materials and Methods ...... 88

Results and Discussion ...... 89

Tables ...... 91

Appendix B: The Interactive Effects of Microbial Biostimulants and Compost

Amendments on Organic Broccoli ...... 94

Objective ...... 94

Materials and Methods ...... 94

Results and Discussion ...... 95

Tables ...... 98

Figures...... 100

Appendix C: The Interactive Effects of Microbial Biostimulants and Nitrogen-

Rich Amendments on Organic Spinach ...... 102

Objective ...... 102

Materials and Methods ...... 103

Results and Discussion ...... 104

Figures...... 106

Appendix D: Soil Test Results from Preliminary Experiments ...... 109

Appendix E: Microbial Biostimulant Product Label Information ...... 110

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List of Tables

Table 2.1 A categorical breakdown of all 2403 inputs reportedly used on all Ohio organic vegetable farms between 2009-2014 ……...... …………………..……………….... 19

Table 2.2 Five most popular microbial biostimulants and microbial biopesticides .…... 20

Table 2.3 ANOVA factors predicting the number of microbial inputs used on farms .... 21

Table 2.4 Regression models for predictors of the number of microbial biostimulant and biopesticide inputs .………………………..………………………..………………….. 22

Table 3.1 Summary of literature comparing plant growth response to PGPR inoculation under different fertility conditions ………...……………..…………………………….. 34

Table 3.2 Possible explanations for plant growth response to inoculation or lack thereof in environments of low, intermediate, and high resource availability ………………..... 36

Table 4.1 Standard soil test results and organic matter by loss on ignition for candidate plus and minus compost history soils …………………………………..…………..….. 58

Table 4.2 Properties of the compost amendment (average of 3 samples) ………….….. 59

Table 4.3 Results of microbial analysis of compost and biofertilizer amendments ...... 60

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Table 4.4 Results of MANOVA with fresh root mass, dry root mass, fresh leaf mass, dry leaf mass, and leaf area as variables ………………………..…….………………….… 61

Table 4.5 Results of MANOVA with WinRHIZO data (root length, average root diameter, root surface area, and root volume) as variables .………………………..….. 62

Table 4.6 Estimated CFU delivered based on two application methods ……...……..... 63

Table 5.1 Review articles published between 2014-2016 on biofertilizers, biostimulants, and/or PGPR …………….…………………………………………………………..…. 77

Table A.1 Biofertilizer treatments applied to each farm site ………………….….….... 91

Table A.2 Serial dilutions of each product were plated on 1/10 strength tryptic soy agar, and the number of colony forming units (CFU) was calculated... …………………….. 92

Table A.3 Early season data and yield data for each tomato site and variety combination

…………………………………………………....……………………….....…………. 93

Table B.1 Details of products used in the broccoli study ………………..……………. 98

Table B.2 Comparison of the average costs and benefits of the four inoculants and two fertilizers tested ...…………………………………………………………….………... 99

Table D.1 Soil test results from preliminary experiments ...... 109

Table E.1 Label information for microbial biostimulant products under study ...... 110

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List of Figures

Figure 1.1 Organic input categories organized by function and composition …...... 7

Figure 2.1 Trends in the percent of Ohio organic vegetable farms that reported using microbial inputs between the years of 2009-2014 …….……………………………….. 23

Figure 2.2 Percent of all reported uses of microbial biostimulants (a) and microbial biopesticides (b) between the years of 2009-2014 by subcategory ……..……...……… 24

Figure 2.3 Map of Ohio eco-regions included in this study, as defined by the US EPA, and locations of microbial bioproduct distributors .……………………….………….... 25

Figure 3.1 Venn diagram depicting microbial (left) and plant (right) conditions that must overlap in order for a plant growth response to inoculation to occur ……..…………… 37

Figure 3.2 Interaction of soil nutrient availability and mycorrhizal growth .…………. 38

Figure 3.3 Percent yield increase of wheat due to inoculation with Azospirillum brasilense was based on mean values reported for 10 soil types, and charted against the yield of the non-inoculated control for each soil type ……..……………………….….. 39

Figure 4.1 Sketch of the photo chamber used to track canopy growth over time .…..... 64

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Figure 4.2 Microbial analysis of bulk soil and rhizosphere of ryegrass growing in soil with low and high compost application history ..…………………………..………….. 65

Figure 4.3 Change in canopy cover over time for all treatments .….………….…….... 66

Figure 4.4 Fresh and dry root and shoot mass of plants under historical compost, recent compost, and inoculation treatments for run 1 (a) and run 2 (b) …….……..….….…… 67

Figure 4.5 Root parameters estimated in WinRHIZO for plants under historical compost, recent compost, and inoculation treatments for run 1 (a) and run 2 (b) ……..….…….... 69

Figure 4.6 Change in canopy cover over time for two inoculation (inoc) treatments, and two rates of historical compost amendment (hist) for run 1 and run 2 ……....…….….. 71

Figure 5.1 A framework for evaluating research productivity ...………………………. 78

Figure B.1 Broccoli yield for different fertilizer and inoculant treatments ….….....…. 100

Figure B.2 Broccoli yield response to inoculation with four products across a soil quality gradient ………...…………...…………………………………..…………...……..…. 101

Figure C.1 Fresh leaf and root mass for n-rich fertilizer by inoculant treatments ……. 106

Figure C.2 MPN of culturable microbial populations in the spinach rhizosphere ...... 107

Figure C.3 Spinach yield and root mass response to inoculation vs. the mass of non- inoculated control the four products. ………………………………………………….. 108

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Chapter 1: Introduction

Organic agriculture is a method of food production that “relies on ecological processes, biodiversity, and cycles adapted to local conditions rather than the use of inputs with adverse effects.” (“Definition of Organic Agriculture | IFOAM” 2016).

According to the United States Department of Agriculture (USDA), organic production systems “integrate cultural, biological, and mechanical practices that foster cycling of resources, promote ecological balance, and conserve biodiversity” (“Organic Production

& Handling Standards | Agricultural Marketing Service” 2016). While the importance of using natural processes rather than external inputs is emphasized in organic food production, there are many natural inputs that play important roles.

Organic farmers must control pests, weeds, and diseases, manage plant growth rates and nutrient status, and maintain the quality of their soils. Some of these objectives can be achieved by good management practices, including diversified cultivar selection, cover cropping, and crop rotation. However, most organic farmers utilize external inputs to maintain the productivity of their systems. Inputs approved for use in organic systems include live organisms and naturally occurring substances that have undergone little to no chemical processing (Figure 1.1).

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Live plants such as leguminous cover crops can be used to bring nitrogen into the soil and improve soil structure. Other cover crops are selected for pest, weed, and disease control properties. Thus, cover cropping is an example that is both a management practice and an input. Live insects can be released into fields to consume pest insects and weeds, or to pollinate crops. Manure from livestock and earthworms also play important roles in maintaining soil nutrient availability and soil quality in organic systems. Non- living substances that are derived from plants or animals, and mined substances that have been minimally processed are also used in organic systems to perform a variety of functions.

Microbial Bioproduct Terminology

Microbial bioproducts are commercially available inputs that contain live or dormant microorganisms and are applied to seed, soil, and/or plants to perform a variety of functions. There are three commonly recognized classes of microbial bioproducts: biopesticides, which control pests and diseases; biofertilizers, which improve plant nutrient status; and biostimulants, which promote plant growth by the production of plant growth regulators, improving tolerance to abiotic stresses, and other mechanisms (Reddy

2014).

Microbial Biopesticides. The United Sates Environmental Protection Agency (US EPA) defines biopesticides as “certain types of pesticides derived from such natural materials as animals, plants, bacteria, and certain minerals” (US EPA 2016). Specific biopesticide products have been developed for insect control, weed control, and disease control.

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Microbial biopesticide (MBP) products contain living or dormant organisms which have the capacity to control pests and diseases. Bacillus thuringiensis (BT) is one of the most widely used microbial biopesticides. It produces substances that are toxic to insect pests.

Microbial biopesticides can control disease by three main mechanisms (1) direct antagonism by hyper-parasitism or predation of pathogens, (2) mixed-path antagonism by the production of antibiotics, enzymes, and other compounds that kill or inhibit pathogens, and (3) indirect antagonism by induction of host immunity, and competition with pathogens for access to the host plant (Pal and McSpadden Gardener 2006).

Microbial biopesticides are generally considered to be safer for human and environmental health than chemical pesticides, and they tend to present a lower risk of developing resistant pest strains (Holt and Hochberg 1997). Furthermore, they are often the only option for disease control among organic farmers, and a growing number of biopesticides are approved for use in organic systems. When used correctly, they can perform equal to or better than conventional products. While MPB products represent a rapidly growing sector of the pesticide industry, major barriers to their adoption include risk-averse customers reluctant to try new products, and mixed perceptions of their efficacy (Marrone 2007).

Microbial Biofertilizers. Vessey (2003) defined biofertilizers as products that contain microorganisms that promote plant growth by increasing the supply and/or availability of nutrients to plants. Biofertilizers can do this by three major pathways: (1) improving nutrient supply by fixing atmospheric nitrogen, (2) improving nutrient availability by

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solubilizing or mineralizing soil nutrients that are already present in the soil, and (3) improving access to nutrients through extension of the host plant’s root zone (Vessey

2003).

Since biofertilizers consist of living organisms that multiply and colonize plant roots as the plant grows, they are usually applied in small quantities (about 1 kg ha-1). As a result, they are typically less expensive and labor intensive to purchase, transport, and apply than other organic soil amendments. However, it is important to note that biofertilizers should be viewed as a supplement to —rather than a full replacement of— a sound nutrient management approach. MBS products, with the exception of nitrogen- fixing bacteria, do not bring any new nutrients into a production system. Instead, they enhance the availability of existing nutrients and/or the plant’s ability to acquire those nutrients.

The term biofertilizer is sometimes used more broadly to describe microorganisms that promote plant growth by means other than improving plant nutrient status:

“Biofertilizers are the substances, prepared from living microorganisms which, when applied to the seeds or plant surfaces adjacent to soil can colonize rhizosphere or the interior parts of the plants and thereby promotes root growth.” (Bhattacharyya and Jha

2012). Growers and industry members often use the term even more broadly to include non-living substances that promote plant growth. However, keeping the definition narrow has potential advantages for product regulation and quality control, and more widespread adoption (Malusá and Vassilev 2014).

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Microbial Biostimulants. In contrast to biofertilizers, biostimulants are almost always defined more broadly to include living and non-living substances that enhance plant growth by a variety of mechanisms. Non-living biostimulants include humic acids, fulvic acids, protein hydrolysates and amino acids, and seaweed extracts (Calvo, Nelson, and

Kloepper 2014). The main modes of action of microbial biostimulant (MBS) products include (1) improving plant nutrient status (2) the production of plant growth-promoting substances, and (3) improved plant tolerance to abiotic stresses (Calvo, Nelson, and

Kloepper 2014). Some MBS inoculants also act as microbial biopesticides. Common examples of MBS organisms include mycorrhizal fungi, rhizobia, pseudomonads, bacillus species, and asymbiotic nitrogen fixing bacteria.

Biostimulant definition and regulation. Du Jardin (2015) offers a thorough review of the history of biostimulant definitions and regulations. Biostimulants were first defined in the literature as “materials, other than fertilizers, that promote plant growth when applied in low quantities.” (Kauffman, Kneivel, and Watschke 2007). Du Jardin proposes to define biostimulants as follows: “A plant biostimulant is any substance or microorganism applied to plants with the aim to enhance nutrition efficiency, abiotic stress tolerance and/or crop quality traits, regardless of its nutrients content” (du Jardin 2015).

In this body of work, the term microbial biopesticide (MBP) will be used to refer to the class of products whose main active ingredient is a microorganism that controls insects or diseases. Similarly, the term microbial biostimulant (MBS) will be used to refer to the class of products whose main active ingredient is a microorganism that promotes

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plant growth by one or more mechanisms. Biofertilizers will be considered as a subset of

MBS products.

Summary of Work

The three main objectives of this body of work were to (1) improve our understanding of current patterns of microbial bioproduct use among farmers, (2) summarize and synthesize the existing literature on the environmental conditions under which microbial biostimulants are most likely to be effective, and (3) and investigate their effects on plant growth in a variety of soil environments. A survey was conducted to determine the prevalence of microbial bioproducts usage and patterns in their adoption among organic vegetable farmers in Ohio (Chapter 2). Given information from the survey, a representative suite of six MBS products was selected for on-farm testing

(Appendices A, B, and C). In these studies, MBS inoculants produced significant effects on only one farm site where fertilizer application rates were reduced. Thus, the literature on the interactive effects of soil quality and biofertilizers was reviewed (Chapter 3). An experiment was conducted to test the hypothesis that plant growth response to inoculation is greatest at intermediate soil quality levels (Chapter 4). In Chapter 5, a framework for evaluating research progress is presented, along with perspectives on the most promising pathways for future MBS research.

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Chapter 2: Patterns of Microbial Bioproduct Use Among Ohio Organic Vegetable Farmers

Abstract

Despite the widespread interest in microbial applications to agriculture, little information is available regarding their current practical use. Data were collected from organic certification records of Ohio vegetable farms that were submitted between the years of 2009-2014. Approximately half of farms had used one or more microbial biostimulant (MBS) products at least once during the six-year period. Mixed inoculants were the most popular category (40%), followed by inoculants with whose active ingredients were unknown microorganisms (28%). A wide variety of bioproducts are commercially available to growers, but nearly than half of MBS usage in Ohio was accounted for by just a few manufacturers. Analysis of variance revealed that region, duration of certification, and fertility monitoring approach were significant predictors of the number of bioproducts used per farm. Regression analysis revealed that the total number of inputs per farm and the number of strategies used to monitor soil quality were both positively correlated with the number of MBS products used. Thus, growers who use MBS products are already in the habit of using data to make management decisions.

More research-based information about MBS products would likely be well received by the organic farming community.

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Introduction

Organic farmers have a wide variety of products available to them commercially.

Over four thousand commercial products have been reviewed and listed for use in organic systems by the Organic Materials Review Institute (OMRI) (“OMRI Exceeds

4,000 Products” 2016). A growing number of these inputs are microbial bioproducts; as of 2016, there were 57 OMRI-listed microbial biopesticides (“Biopesticide” 2016), and

174 OMRI microbial biostimulant products (“Microbe-Containing Products | Vegetable

Production Systems Laboratory” 2016).

Biopesticides are defined and regulated by the US EPA. However, there is no legal definition of biofertilizers or biostimulants in the US, and hence, no regulatory framework. India is one example with a complete legal framework for the production and regulation of biofertilizers (Malusá and Vassilev 2014). In the European Union (EU), different countries have more or less rigorous data requirements for a product to be marketed as a biostimulant or biofertilizer. Malusá and Vassilev (2014) propose a rigorous framework for the registration, evaluation, and labeling of biofertilizers in the

EU. Alternatively, Du Jardin (2015) proposed to classify and regulate biofertilizers as a subcategory of microbial biostimulants. Along these lines, La Torre et al. (2016) include microbial biofertilizer biostimulants in a list of products that will be classified and regulated in the EU as biostimulants (La Torre, Battaglia, and Caradonia 2016).

In North America, the Biostimulant Coalition is an interest group that addresses regulatory issues regarding “biological or naturally-derived additives and / or similar

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products, including but not limited to bacterial or microbial inoculants, biochemical materials, amino acids, humic acids, fulvic acid, seaweed extract and other similar materials” (“Biostimulant Coalition :: About” 2016). However, there are currently no formal definitions or regulatory guidelines in place for microbial biostimulants.

Furthermore, little information is available on patterns of their use. Quilty and Cattle

(2011) review trends in the use of organic amendments in Australia, including composts, vermicasts, humic substances, meat, blood and bone meal, fish hydorlysate, seaweed extracts, and bio-inoculants. The authors note that popularity of these amendments has increased with the demand for organic produce (Quilty and Cattle 2011). To our knowledge, no studies have systematically studied the use of bioproducts in US organic framing systems.

The main objective of this study was to establish a baseline assessment of microbial bioproduct use among Ohio organic vegetable growers and guide directions for further research. In this study, all inputs on 86 Ohio organic vegetable farms were documented and categorized by surveying the organic certification records submitted to the Ohio

Ecological Food and Farming Association (OEFFA) over a six year period.

Materials and Methods

Survey sampling. Survey data were collected from Organic System Plans (OSPs) submitted for organic certification to the OEFFA. IRB approval to conduct this survey was requested and granted. A total of 104 Ohio organic vegetable farm records were searched. Data were taken from the OSPs of 86 farms between the years of 2009-2014.

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The remaining 18 farms were not included in this survey because their certification had been terminated or suspended, or their files were missing. To remain certified, farmers are required to fill out a full organic system plan every 3 years, with less thorough renewals for the two intervening years. Only full OSPs were surveyed, and not the renewals. Thus data from two time points were collected on farms that have been certified since before 2012.

A wide variety of data were collected from each OSP, including background information on each farm, practices for managing and monitoring fertility, pests, weeds, and diseases, and a comprehensive list of all inputs reported. All inputs were recorded by hand directly from organic systems plans, and entered into a database later. A master input list consisting of every distinct input was constructed. The trade name, manufacturer, active ingredient, application method, and functional categories were listed for each input. The functional categories were as follows: cover crop, compost, manure, fish and slaughterhouse by-product, seaweed, mined mineral fertilizer, mined mineral pesticide, microbial biostimulant, microbial biopesticide, potting soil, soil conditioner, and mulch. A product was defined as a microbial biostimulant (MBS) if the main active ingredient was a microorganism and the product was advertised to promote plant growth, while a product was defined as a microbial biopesticide (MBP) if the main active ingredient was a microorganism and the product was advertised to reduce pest or disease pressure. In this study, MBS and MBP products are jointly referred to as microbial bioproducts.

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Statistical Methods. Statistical tests were only performed on data from the 2012-2014

OSPs. Thus data from the most recent time point is included for all 86 farms surveyed, with no repeated measures. Analysis of variance and regression analysis were used to investigate the predictive value of nominal factors for the number of microbial bioproducts used on a given farm. Factors investigated by ANOVA include the following: region, duration of certification, fertility monitoring approach, fertility satisfaction rating, pest, weed and disease monitoring approach, frequency of pest, weed and disease monitoring, and pest control satisfaction rating. The five regions used in this survey were eco-regions designated by the United States Environmental Protection

Agency (US EPA), and listed in Table 2.3. Farms were divided into two groups based on duration of certification: established farms have been certified before 2012, and recently certified farms received certification in or after 2012. Farms were also divided into three groups based on the reported intensity of their soil fertility monitoring approaches. These categories were determined based on information available on the OSPs, and the groups are as follows: observation alone, soil tests alone, or multiple methods that may also include tissue testing or crop quality testing. After factors were individually tested with

ANOVA, a three-factor model was tested using the three most significant factors.

Bartlett’s and Levene’s tests for homogeneity of variance were performed using year as a model factor, and neither resulted in significant P-values. All statistical tests were run in

Minitab Version 16 (Minitab Inc., USA).

Similarly, linear regression analysis was used to evaluate relationships between the value of cardinal factors and the number of MBS and MBP products used on a given

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farm. Cardinal factors investigated by regression include the following: years certified, certification year, total acres, total number of inputs, number of strategies for managing fertility, number of strategies for managing pests, weeds, and diseases, number of strategies for monitoring fertility, number of strategies for monitoring pests, weeds, and diseases, and the acreage of 14 different crop categories. Questions on management and monitoring strategies were written as “check all that apply” questions, so the sum of boxes checked for each question was used as a proxy for the intensity of management or monitoring.

Results

Descriptive Summary. A total of 412 distinct inputs were reportedly used in a total of

2403 instances on 86 vegetable farms in Ohio between the years of 2009-2014. Mined mineral fertilizers were the most frequently used by number, accounting for over 20% of all inputs (Table 1.1). MBS products accounted for 4.8% of all inputs on all farms by number, and MBP products account for 4.3%. Those numbers do not include products such as potting soils or fertilizers that list microorganisms as ingredients, so this is a lower estimate of their prevalence. Between 2009 and 2014, the overall percentage of farms using microbial bioproducts changed from 30 to 76%. However, this change was not steady, but fluctuated from year to year. When microbial bioproducts are broken down into MBP and MBS categories, it becomes clear that MBP adoption rates have risen from 20% to about 60%, while MBS adoption rates have hovered around 30-40%

(Figure 2.1).

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A total of 22 MBS products from 12 different manufacturers, and 10 biopesticide products from 8 manufacturers were reportedly used over the six year period. The five most popular microbial biostimulants and biopesticides are listed in Table 2.2 (a) and (b), respectively. Nearly half of all reported cases of MBS use was accounted for by a single manufacturer. Similarly, a single manufacturer accounted for nearly half of all reported biopesticide uses. In accordance with US EPA regulations, all labels of all MBP products used included both the species and strain of the active ingredients, whereas products whose ingredients were not labeled at the species level accounted for most cases of MBS use.

The active ingredients of all MBS and MBP products recorded in the survey were used to classify them further. Among MBS products, mixed inoculants were the most popular, followed by products with unknown microorganisms, then rhizobia, then mycorrhizal fungi, and lastly, asymbiotic nitrogen fixers (Figure 2.2a). For the purposes of this study, species level identification either means that the label clearly lists the genus and species of the active ingredient, or it claims to contain proprietary strains of a particular genus. More than a quarter of biofertilizer uses were accounted for by products with labels simply claiming to contain “beneficial bacteria” or “beneficial fungi,” without any further identification. In contrast, all of the biopesticide ingredients were identified at the strain level. Biopesticides fell into just 3 categories, BT (Bacillus thuringiensis), other insecticides, and fungal antagonists. Bacillus thuringiensis was the active ingredient in most reported instances of use (Figure 2.2b).

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Patterns of Use. To evaluate the impact of nominal and ordinal factors on the use of microbial bioproducts, analysis of variance was conducted on each factor. Region, duration of certification, and fertility monitoring approach were all identified as significant factors affecting the number of microbial inputs used on farms. Thus, the number of microbial inputs was modeled as a 3-factor ANOVA. The model was significant, but the adjusted R2 was low, and the normal probability plot showed some step functions, indicating that important factors were missing (Table 2.3). The Huron/Erie

Lake Plains, and the Erie Drift plain regions had the highest average use of microbial bioproducts. On average, established farms used slightly more microbial inputs than did recently certified farms. Farmers who used multiple methods for monitoring fertility also tended to use more bioproducts than did farmers who just observed the soil. Farmers who only did soil tests tended to use the fewest bioproducts.

Since biostimulants and biopesticides are used for different purposes, different factors would be expected to influence their on-farm usage. Thus, a regression model was used to determine factors that correlated with the number of MBP and MBS products used, and two separate regression models were made with the top five correlations in each model. The model details are summarized in Table 2.4. For both of the latter regression models, the histogram of the residuals was symmetrical, and the normal probability plot showed a good fit. The number of fertility monitoring strategies had the largest positive and significantly (P=0.037) related to the number of MBS products used on a given farm, and it had the largest positive correlation coefficient of all factors investigated. Similarly, the number of strategies that growers used for managing and

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monitoring pests, weeds and diseases had the largest positive and significant (p=0.042) relationship with the number of MBP products used. Finally, the total number of inputs was highly significantly correlated (p<0.001) with both the number of MBS and MBP products used.

Discussion

A wide variety of MBS and MBP products were commercially available to growers in Ohio over the 2009-2014 study period. However, most of the reported uses for both classes of products are accounted for by a few specific manufacturers (Table 2.2).

Most commercially available MBS products were mixed inoculants, and many products had imprecisely labeled ingredients and unclear application instructions. These products were also the most popular among Ohio organic vegetable growers. Furthermore, there is virtually no publically available, research-based information to substantiate claims of product efficacy. The percentage of organic farmers using MBP inputs tripled over the six-year study period, whereas the percent of farmers using MBS inputs remained stagnant (Figure 2.1). This contrast could be explained by the fact that the MBP industry is ahead of the MBS industry in terms of efficacy testing, quality control and the availability of information on product performance.

In order for market forces to improve the quality of commercial products, several conditions must be met: (1) the market must consist of a large number of sellers with (2) freedom to enter and exit the marketplace, (3) product information must be available and accurate, and (4) consumers must be free and independent (Ikerd 2008). The number of

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buyers and sellers of microbial bioproducts appears to be increasing, but just a few manufacturers currently dominate a large fraction of the market.

The second condition, freedom to enter and exit the market, is limited in the biopesticide industry due to the extensive testing and large costs involved in registering a product as a pesticide with the EPA. Harman et al. (2010) have estimated that this process takes 3 to 6 years and cost approximately $8 million. In contrast to biopesticides, there are no federal regulations for MBS products, and barriers to market entry are comparatively minor. MBS production at the local level can be achieved in less than a year, and start-up costs have been estimated at $100,000 or less (Harman et al. 2010).

The third condition of the availability of accurate product information is currently stronger in the biopesticide industry than it is in the MBS industry. In this study, the number of strategies used to monitor pests and soil fertility had a significant positive relationship with the number of MBP and MBS products used, respectively (Table 2.4).

This suggests that farmers who use both classes of bioproducts are already in the habit of using data to make farm management decisions. Thus, research-based information about the efficacy of bioproducts, especially MBS products, would likely be well received. The expansion of university research, extension, and education efforts has the potential to play an important role in improving both the quality and availability of information about the efficacy of microbial bioproducts. These efforts are particularly important and challenging given that the MBP and MBS inoculants perform in a far more ecologically context-dependent manner than their chemical counterparts.

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It is also important to consider the fact that crop consultants and distributors probably influence the adoption of microbial bioproducts. In this survey, farm region was a significant predictor of the number of bioproducts used per farm (Table 2.3), and the region with the largest number of bioproduct distributors had a higher rate of adoption than 3 of the 4 other regions (Figure 2.3). This suggests that the fourth condition of free and independent consumers is not currently met. Thus, in addition to educating farmers, university researchers and extension agents must collaborate with bioproduct manufacturers and distributors to improve the successful use of microbial products.

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Tables

Table 2.1 A categorical breakdown of all 2403 inputs reportedly used on all Ohio organic vegetable farms between 2009-2014.

Input Type Percent of all inputs reported Mined Mineral Fertilizer 20.7 Cover Crop 11.4 Biochemical Pesticide 11.0 Manure 10.1 Fish and Slaughterhouse by-product 6.2 Potting Soil 5.8 Mulch 5.7 Microbial Biostimulant 4.8 Soil Conditioner 4.5 Mined Mineral Pesticide 4.4 Microbial Biopesticide 4.3 Seaweed 2.6 Compost 2.0 Other 6.5

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Table 2.2 Five most popular microbial biostimulants (a) and microbial biopesticides (b) by number of uses across all Ohio organic vegetable farms over the period of 2009-2014.

(a) Percent of Microbial Biostimulant Rank Name Manufacturer Ingredients Uses Myco Seed Agri-Energy 1 Treat Resources unspecified mycorrhizal fungi and bacteria 26%

Agri-Energy 2 SP-1 Resources unspecified bacteria, fungi, algae, enzymes, 22%

INTX 3 N-Dure Microbials Rhizobia (species depends on the crop) 11%

Mycorrhizal Glomus intraradices, G. mosseae, G. aggregatum, G. 4 MycoApply Applications etunicatum 7%

Arthrobacter globiformis, Azospirillum lipoferum, Azotobacter chroococcum, Azotobacter vinelandii, Bacillus amyloliquefaciens, Bacillus cerus, Bacillus megaterium, Bacillus subtilis, Micrococcus leteus, Tainio Pseudomonas fluorescens, Pseudomonas putida, Technology & Streptomyces griseus, whey protein, diatomaceous 5 BioGenesis Technique earth, leonardite 5% I NP

(b) Percent of Microbial Distributor Biopesticide Rank Name /Manufacturer Ingredients uses Valent Bacillus thuringiensis subsp. Kurstaki, strain 1 DiPel Biosciences ABTS-351 49%

Valent Biosciences/ 2 Actinovate Novozymes Streptomyces lydicus WYEC 108 17%

Bayer Crop 3 Serenade Science QST 713 strain of Bacillus subtilis 8%

Laverlam International 4 Mycotrol O Corporation Beauveria bassiana strain GHA 8%

Bacillus thuringiensis subspecies kurstaki strain 5 Deliver BT Certis USA LLC. SA-12 solids, spores, Lepidopteran active toxins 7%

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Table 2.3 ANOVA factors predicting the number of microbial inputs used on farms. The five regions are defined by US EPA Eco-regions. Established farms were defined as having been certified since before 2012, while recently certified farms were defined has having received certification in or after 2012. Farms were grouped into fertility monitoring approaches based on whether they reported using soil tests alone to monitor fertility, visual observation alone, or multiple methods. The adjusted R! for the model was 17.13%.

Number Mean number Standard P- ANOVA factor predicting of farms of bioproducts error of value microbial input use per farm the mean Eco-region 0.024 Eastern Corn Belt Plains 17 1.12 0.30 Huron/Erie Lake Plains 3 2.67 1.45 Erie Drift Plain 48 2.16 0.30 Western Allegheny Plateau 16 0.88 0.22 Interior Plateau 1 0 - Duration of certification 0.021 Established 47 1.98 0.29 Recently certified 38 1.37 0.27 Fertility monitoring approach 0.021 Soil test only 8 0.50 0.27 Observation only 15 1.00 0.28 Multiple methods 63 2.03 0.25

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Table 2.4 Regression models for predictors of the number of microbial biostimulant and microbial biopesticide inputs. Regression equations are as follows: Biofertilizer Inputs per farm = -1.290 + 0.078 TI + 0.269 FM + 0.0248 AV - 0.0185 AL + 0.0340 AH; Biopesticide inputs per farm = -1.02085 + 0.0288 YC + 0.0573 TI - 0.0283 AC + 0.224 PM + 0.0970PC

Regression model coefficients P-value Adjusted !! Microbial biostimulant use regression model 57.53% Total inputs (TI) < 0.001 Number of fertility monitoring strategies (FM) 0.037 Area in vegetable production (AV) 0.184 Area in hay (AH) 0.028 Area in legumes (AL) 0.017 Microbial biopesticide use regression model 52.17% Total inputs (TI) < 0.001 Years Certified (YC) 0.003 Number of pest, weed and disease control strategies (PC) 0.042 Number of pest, weed and disease monitoring strategies (PM) 0.028 Area in cover crops (AC) 0.001

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Figures

90 80 70 60 All Microbial 50 Bioproducts

40 Microbial 30 Biopesticides 20 Microbial

% of farms using product product farms %of using 10 Biostimulants 0 2009 2010 2011 2012 2013 2014 Year

Figure 2.1 Trends in the percent of Ohio organic vegetable farms that reported using microbial inputs between the years of 2009-2014.

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(a)

(b)

Figure 2.2 Percent of all reported uses of microbial biostimulants (a) and microbial biopesticides (b) between the years of 2009-2014 by subcategory. A microbial biostimulant product was defined as mixed if it contained species or genera from two or more of the other categories. Products were defined as unknown if the label did not specify the contents beyond a claim to include beneficial microorganisms Microbial biopesticide categories include Bacillus thuringiensis (BT), other microbial bio- insecticides, and microbial biopesticides that control fungal pathogens.

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Figure 2.3 Map of Ohio eco-regions included in this study, as defined by the US EPA, and locations of microbial bioproduct distributors. No farms in the study were located in the Eastern Great Lakes Lowlands.

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Chapter 3: Literature Review of Plant Growth Promoting Rhizobacteria in Different Soil Environments

Abstract

Plant growth-promoting rhizobacteria (PGPR) are an important class of microbial biostimulants. Many studies demonstrate the potential for microorganisms to improve plant nutrient use efficiency and compensate for reduced fertilizer application. However, how their performance varies across gradients in soil quality and soil nutrient availability has not been extensively studied. Much of the literature appears to be consistent with the idea that PGPR have the greatest potential to promote plant growth at intermediate levels of nutrient availability. Out of 9 studies that performed full factorial experiments with and without PGPR in different soil environments, 7 showed a greater response to inoculation in lower to intermediate levels of soil quality. More meta-analyses and/or multi-site studies are needed to further elucidate trends in the relationships between soil characteristics, fertilization, and inoculation. Further research into factors that explain variability in the performance of plant growth promoting rhizobacteria could improve our understanding of when and where farmers can use MBS products most reliably.

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Introduction

Plant growth-promoting rhizobacteria (PGPR) are an important class of microbial biostimulants that often work by multiple modes of action (Podile and Kishore 2007).

PGPR inoculants can act as biostimulants by nitrogen fixation, nutrient mobilization, hormone production, and other pathways (Kennedy, Choudhury, and Kecskés 2004).

Despite the increasing popularity of applying microbial products to crops, there is a limited understanding of their performance in different soil environments.

Multiple studies have demonstrated the potential for PGPR to enhance plant nutrient use efficiency (Adesemoye, Kloepper, and Torbert 2008; Shaharoona et al. 2008).

Studies have also demonstrated that PGPR can partially or fully compensate for reduced rates of fertilizer application (Adesemoye, Torbert, and Kloepper 2009; Dobbelaere et al.

2002; Kapulnik et al. 1981; Yildirim et al. 2011). However, since PGPR are living organisms, many factors affect their survival and efficacy in the rhizosphere.

Furthermore, the mode of action of the inoculant must match a factor that limits plant growth in a particular context in order for a plant growth response to inoculation to be observed (Figure 3.1). Indeed, there are many soil environments in which no substantial plant growth response to inoculation would be expected.

Interactive Effects of PGPR and Organic Soil Amendments

Organic soil amendments, such as compost, can dramatically alter soil physical, chemical, and biological properties, but the effects of amendment history on PGPR performance are not well understood. A wide range of conflicting conclusions have been

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offered about soil characteristics and fertilizer application rates under which inoculants are most effective. Some studies have shown a greater effect of inoculation in low organic matter soils (Egamberdiyeva 2007; Imran et al. 2015), while others have suggested the opposite (Çakmakçi et al. 2006). Additive, antagonistic, and synergistic interactions between PGPR and fertilizers have all been described in the literature, though not always using those terms. Some studies conclude that PGPR can be used effectively on top of normal fertilization practices (Díaz-Zorita and Fernández-Canigia

2009), others conclude that organic fertilizers mask the beneficial effects of PGPR (T.

Krey et al. 2013), and others suggest that PGPR performance is enhanced by organic fertilizer inputs (Song et al. 2015).

A Conceptual Model for Plant Growth Response to PGPR

These observations could be explained by extending to PGPR a conceptual model of patterns observed in the growth of arbuscular mycorrhizal fungi (AMF). Treseder and

Allen (2002) hypothesized that background soil quality would modulate the effects of nitrogen and phosphorus additions on growth of AMF (Figure 3.2). They expected plants to allocate more carbon to fungal symbionts at intermediate nitrogen and phosphorus levels, where plants would benefit most from the nutrients supplied by the fungi.

Accordingly, they found that AMF abundance increased when N and P were added to N and P deficient sites, and decreased when P was added to a fertile site. In a different study of AMF in contaminated soils, N fertilization has been shown to improve plant growth response to AMF by alleviating nutrient limitation (Hungria et al. 2010). Similar patterns the response of ectomycorrhizal fungi (EMF) to soil environments have been reported in

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the literature (Kleczewski, Herms, and Bonello 2010; Kleczewski, Herms, and Bonello

2011). Thus, the model seems to work well for both endophytes (such as AMF), which penetrate the host cell, and with microorganisms that colonize the host cell surface (such as EMF).

As it is with mycorrhizal fungi, the relationships between PGPR and their host plants vary in intimacy. Vessey (2003) distinguishes between endophytic and rhizospheric

PGPR. Rhizospheric PGPR dwell in the root zone, and often attach themselves to the root surface, whereas endophytic PGPR actually reside within the apoplastic space inside of plant roots. In the endophytic symbiosis between rhizobia and legumes, a specialized structure is formed that assists in the exchange of nutrients (Vessey 2003). Azospirillum is one of the best-known genera of rhizospheric bacteria, although it is known to act as an endophyte as well. It derives its energy from both root exudates and carbon compounds in the soil (Kennedy, Choudhury, and Kecskés 2004).

Population levels of Azospirillum brasilense decline rapidly below detection levels when inoculated into certain soils, but they remain stable in the rhizosphere for extended periods of time regardless of soil type (Bashan et al. 1995). It has been estimated that, on average, approximately 17% of all carbon derived from photosynthesis is exuded by plants into the rhizosphere (Nguyen 2009). In order to explain this high carbon cost, the soil microbial loop hypothesis proposes that root exudates stimulate the growth of the microbial biomass whose turnover improves nutrient availability to the plant (Clarholm

1985; Coleman 1994). Because of this energy-rich food source, microbial populations in

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the rhizosphere tend to be a few orders of magnitude higher than those in the bulk soil.

However, it is unclear whether roots typically exude more (Kobe, Iyer, and Walters 2010) or less (Henry et al. 2005) soluble carbon under low nutrient conditions. In studies of

EMF, mycorrhizal growth is not always correlated with the concentrations of root starches or soluble sugars, as optimal allocation and partitioning theory would predict

(Kleczewski, Herms, and Bonello 2010; Kleczewski, Herms, and Bonello 2011).

In any case, rhizospheric microorganisms do play important roles in mineral nutrient acquisition (Clarholm 1985; Dakora and Phillips 2002). Thus, whether or not host plants directly stimulate microbial growth by root exudation, the Treseder and Allen model could be generalized to model the benefit that all rhizosphere microorganisms confer to plants. This adapted model would predict that plant growth response to inoculation with

PGPR should have a quadratic relationship with soil nutrient availability. Consistent with this model, the majority of studies that investigate the role of soil quality and nutrient availability on PGPR inoculant performance have suggested that inoculants have the greatest impact on plant growth at low to intermediate levels of soil quality (Table 3.1).

Systematic studies of Azospirillum

Okon and Labandera-Gonzalez (1994) conducted a review of dozens of studies on the application of Azospirillum inoculants to agronomic crops that were conducted around the world over the span of 20 years. The authors concluded that inoculation produces positive, significant increases in yield 60-70% of the time, and that yield is consistently increased by 5-30% under field conditions. One study reported that the greatest yield

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increases were observed in plots that were not excessively fertilized. Azospirillum inoculants consistently compensated for reduced fertilizer application. However, no studies systematically evaluated plant growth response across a range of soil quality.

Overall, a strong need for more field experiments with careful documentation of soil condition, soil history, cropping history, and microclimates was suggested (Okon and

Labandera-Gonzalez 1994).

A meta-analysis of 59 journal articles documenting the effects of Azospirillum spp. inoculation on wheat revealed an average 8.9% increase in grain yield. For instances in which no nitrogen fertilizer was applied, this average percent yield increase due to inoculation rose to 14.13% (Veresoglou and Menexes 2010). However, no information on soil type, soil quality, or soil nutrient status was included in the analysis, so it is impossible to know whether this difference is due to antagonist effects of the fertilizer on the inoculant, or if the inoculant was simply more beneficial to plants under low nutrient conditions.

A multi-site study of Azospirillum brasilense on wheat was conducted at 297 experimental locations representing 10 different soil types in the Pampas region of

Argentina (Díaz-Zorita and Fernández-Canigia 2009). They found a similar average 8% increase in grain yield due to inoculation, and this increase was not affected by N fertilizer application. It is important to note that fields in this study were fertilized according to standard practices based on soil test results. Thus fields that received fertilizer were lower in soil nutrient content to begin with, so it is unclear whether

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fertilized fields would be more nutrient rich than unfertilized fields. These circumstances suggest that total soil nutrient status may be a more important factor than fertilizer application.

In the same study, Díaz-Zorita and Fernández-Canigia (2009) reported a significant yield increase due to inoculation for most soil types, and the magnitude of the yield response varied slightly from soil type to soil type. The authors did not report on soil characteristics, but the yield of the non-inoculated control in a given soil type could be interpreted as a proxy for soil quality. Fields in which the control yield is higher likely had higher soil quality.

When plotted against the yield of the control, the yield increase due to inoculation in a given soil type appears to follow a parabolic curve (Figure 3.3). This can be interpreted to mean that the relative yield response to inoculation is greater at intermediate levels of soil quality. This could be explained by the concept of diminishing returns; if the plant is not resource-limited, it may be near its maximum potential for growth, and hence, unresponsive to the mode of action of the inoculant (Table 3.2).

Response to inoculation in environments of extremely low soil quality has not been extensively studied. In one study of the effects of phosphorus fertilizer and rhizobium inoculation on soybean yields for smallholder farmers in Africa, Ronner et al. (2016) found that absolute yield response to inoculation was lowest for lower yielding plots. In some cases, the lowest yielding plots doubled in response to inoculation, but the yield was still so much lower that the absolute increase remained relatively small. Charting out

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percent yield response due to fertilizer and inoculant treatments revealed that the relative response decreased with increasing control yield (Ronner et al. 2016).

Summary

More meta-analyses and/or multi-site studies are needed to further elucidate trends in the relationships between soil characteristics, fertilization, and inoculation. Much of the existing literature appears to be consistent with the model proposed by Treseder and

Allen (2002), suggesting that rhizosphere microorganisms have the greatest potential to promote plant growth at intermediate levels of nutrient availability. Further research into environmental factors that explain variability in the performance of PGPR could improve our understanding of when and where farmers can use MBS products most reliably.

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Tables

Table 3.1 Summary of literature comparing plant growth response to PGPR inoculation under different soil quality and fertilizer conditions Optimum Setting/ growing Crop Inoculant Conclusions Citation soil quality medium/ /fertilization fertilizer level treatments Intermediate Greenhouse/ Foxtail Azospirillum Significant (Kapulnik et al. sterilized quartz millet brasilense strain growth response 1981) sand/ five cd at suboptimal N concentrations of fertility, NH4NO3 (0- potential to use 0.2g/L) A. brasilense for fertilizer savings Low to Greenhouse/ Wheat, Azospirillum Effect of (Dobbelaere et intermediate potting compost maize brasilense Sp245, inoculation was al. 2002) (12% organic A. irakense KBC1 most matter)/ 150, 200, pronounced at 270, 400 mg low to N/plant intermediate fertilization levels High Field/ Low Sugar Bacillus OSU- Response to (Çakmakçi et al. (2.4%) and high beet 142, inoculation was 2006) (15.9%) organic Paenibacillus slightly more matter sandy polymyxa, pronounced in loam/ NA Pseudomonas the soil of higher putida, organic matter Rhodobacter capsulatus Low Greenhouse/ low Corn Pseudomonas Inoculants (Egamberdiyeva (calcisol, 1.16% alcaligenes exhibit a much 2007) OM) and high PsA15, Bacillus greater (loamy sand, polymyxa BcP26, stimulatory 4.07% OM) Mycobacterium effect in quality soil/ NA phlei MbP18 nutrient-poor soils than nutrient-rich soils Low Field and Wheat Pseudomonas Negative linear (Shaharoona et greenhouse/ fluorescens (two relationship al. 2008) Haplocambids ACC-deaminase between soil, (0.69% producing strains) fertilizer OM)/ N, P and K application and applied at 0%, % increase of 25%, 50%, 75%, growth and yield and 100% due to recommended inoculation, rates potential for fertilizer savings continued

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Table 3.1 Continued.

Low Semi-controlled/ Corn, Pseudomonas PGPR can (Thomas Krey loamy sand rape- fluorescens strain mobilize P and et al. 2011) (2.43% OM)/ seed DR54, improve plant compost and cow Enterobacter nutrient uptake manure radicincitans sp. in P-deficient nov. strain DSM soils, but 16656 application of organic fertilizer may mask these effects Low Field/ loamy Corn Pseudomonas Growth (T. Krey et al. sand/ long-term fluorescens, promotion was 2013) application of Enterobacter most effective in compost, manure, radicincitans non-amended TSP, and an a soils, PGPR and control P fertilizers should be applied separately rather than in combination Low Field/ Two sandy Kabuli, Ochrobactrum Yield response (Imran et al. clay loam soils, chickpea ciceri Ca-34T, to inoculation 2015) one 0.66% OM, Mesorhizobium was higher on the other 0.99% ciceri TAL-1148 average in the OM/ NA low OM soil than the high OM soil Intermediate Field/ Sandy Tomato, Bacillus subtilis, PGPR had no (Song et al. to high loam, (OM spinach B. mucilaginosus effect without 2015) 11.8%)/ No, low fertilizer, and (70.5 kg/ha N), effects on and high (150 tomato and kg/ha N) vermi- spinach were compost greatest at high application to intermediate fertilization rates, respectively

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Table 3.2 Possible explanations for plant growth response to inoculation or lack thereof in environments of low, intermediate, and high resource availability.

Resource Low: Intermediate: High: availabilityà Diminished yield Positive yield response Diminished yield response response Inoculant stimulates NA Inoculant stimulates plant Inoculant stimulates root plant growth growth by N-fixation, growth at the expense of hormone production, etc. shoot growth and/or yield

Inoculant survives Factors other than the NA Plant is not resource- and functions, plant mode of action of the limited and/or plant has is not responsive inoculant limit plant reached its maximum growth potential for growth

Inoculant does not Both plant and NA Inoculant is out-competed survive inoculant are resource- by native limited, and/or the microorganisms, and/or plant suppresses the plant suppresses microbial growth microbial growth

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Figures

Figure 3.1 Venn diagram depicting microbial (left) and plant (right) conditions that must overlap in order for a plant growth response to inoculation to occur.

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Figure 3.2 Interaction of soil nutrient availability and mycorrhizal growth. At high nutrient availability levels, plants allocate more carbon aboveground than belowground, limiting fungal growth. When nutrient availability is extremely low, both plant growth and fungal growth is limited, regardless of plant C allocation. Thus, fungal growth will be highest at intermediate levels of nutrient availability, where it will be most effective at improving plant nutrient acquisition. Adapted with permission from Treseder and Allen (2002).

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Figure 3.3 Percent yield increase of wheat due to inoculation with Azospirillum brasilense was based on mean values reported for 10 soil types, and charted against the yield of the non-inoculated control for each soil type. Yield response to inoculation appears to have a quadratic relationship with the control yield for a given soil type. This graph was generated using data from a study of dryland wheat at 297 field sites including 10 soil types (Díaz-Zorita and Fernández-Canigia 2009)

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Chapter 4: Interactive Effects of Soil History, Compost Amendments, and Microbial Biostimulant Products

Abstract

The interactive effects of soil quality and microbial inoculation are not well understood. Microbial biostimulants (MBS) products did not produce any significant, or even numerically consistent yield increases in an on-farm study of tomato under normal nutrient management practices. In an on-farm factorial experiment with and without compost and inoculation, inoculants increased broccoli yield by 12.9%-64.6% in plots where not compost was added. No additive effects of inoculation and compost application were observed. Plotting the percent yield increase to inoculation against the control yield revealed a strong negative linear relationship between plant growth response to inoculation and background soil quality. Finally, a full factorial experiment was performed in the greenhouse with historical compost application, recent compost application, and inoculation as factors. However, the inoculant had no significant effect on plant growth under any soil condition, and numerical differences were inconsistent between runs. Thus, the conditions were not adequate to test the hypothesis that the relationship between fertilization and plant growth response to inoculation is mediated by background soil quality. Low inoculum concentration is a likely explanation for the lack of effect. Inoculum concentration may require fine-tuning for different environments.

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Introduction

Background on Compost and Biofertilizer Application. In organic vegetable production, compost is a common soil amendment that alters soil quality and changes soil nutrient status in a time-dependent manner. The application of compost at different period (i.e. historical vs. short-term) may have interactive effects on the efficacy of microbial biostimulants and growth responses of host crops. The ecologically context-dependent performance of microbial biostimulants was discussed in Chapter 3. The literature appears to suggest that biofertilizers have the greatest effect under low to intermediate levels of soil quality, or under conditions that are moderately stressful to plants.

While many reports suggest that inoculants are less effective at higher rates of fertilization, less research has been done at extremely low levels of soil quality. At extremely low levels of soil quality, plant growth is likely limited by factors for which the inoculant cannot adequately compensate, and thus plant growth response to inoculation would be diminished. Thus, there may be some intermediate range of soil quality in which PGPR inoculants are maximally effective.

To our knowledge, no studies have directly investigated whether soil amendment history and soil quality modulate the interactive effects of fertilizers and PGPR inoculants. If the relationship between recent amendment application and yield response to inoculation does depend on background soil quality, this could explain some of the apparently conflicting reports in the literature.

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A set of experiments was designed to test two main hypotheses: (1) that plant growth response to inoculation is most effective at intermediate levels of soil quality, and (2) that background soil quality modulates the interactive effects of inoculation and recent soil amendments. Short-term compost application stimulates microbial activity and significantly increases microbial biomass carbon (Jannoura, Bruns, and Joergensen

2013). However, the effects of organic amendments on the populations of soil bacteria and enzyme activities are typically transient, and may become insignificant after several years (Carlson et al., 2015). Thus, both the rate and timing of organic amendments would be expected to influence the survival and efficacy of PGPR. In this study, plant growth response to inoculation across three levels of recent compost application was compared between soils of low and high compost application history.

Before testing theses specific hypotheses in a controlled environment, preliminary experiments on the effectiveness of MBS products were conducted. These experiments are described in detail in Appendices A-C. In the first experiment, four commercially available products and a control were tested on tomato growth and yield under normal nutrient management practices at four different Ohio organic farms and a field station at

OARDC in Wooster, Ohio. A second experiment was conducted on broccoli at a farm in

Freemont, Ohio to test the combined effects of PGPR inoculants and compost amendments. Four products and a control were tested in a full factorial design with two compost-based fertilizers, and a control, for a total of 15 treatments. A third experiment was conducted on spinach in a high tunnel at the OARDC in fall of 2015. A full-factorial design was used with there microbial treatments: two of the best performing products

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from previous experiments, and three soil treatments: a control, and two sources of N- rich fertilizer. Environoc 401 was a mixed inoculant that seemed to perform most consistently across trials. Thus, it was chosen for use in this study.

Materials and Methods

Experimental Design. This experiment utilized a randomized complete block design with

12 treatments and 12 replications. The treatments were a 2x3x2 factorial consisting of two soil histories (low and high compost application), 3 recently added compost (none, medium, and high), and two microbial levels (control and inoculated). The compost history treatments will be abbreviated as LowH for low historical, and HighH for high historical compost treatments. The recently added compost treatments will be abbreviated as NoR, LowR, and HighR, for no, low, and high recent compost amendments, respectively. Microbial will be abbreviated as I for inoculated, and C for control. Thus treatments range from LowH-NoR-C to HighH-HighR-I.

Soil History and Acquisition. Soil samples were taken from low and high and historical compost application sites at Fry Farm and West Badger Farm at the OARDC in Wooster

Ohio. Soil taxonomy for each site was determined using the USDA National Resources

Conservation Service Web Soil Survey. West Badger had the most dramatic difference in soil properties due to compost amendment, so it was chosen for the experiment. Soil samples for both runs of the experiment were collected in 1 square meter quadrats at a six-inch soil depth from four separate areas randomly located in the high compost history side of the field, and in four areas randomly located in the low compost history side of the

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field. The soil used for the first run of the experiment was sieved, amended and potted up within 48 hours of collection, whereas the soil used for the second run was stored in a cooler at 5 °C for several weeks before sieving, amending and potting.

Compost Amendments. The experiment was blocked on zone of soil collection to ensure that results were not confounded with zone by amendment interactions. Soil collected from each zone was sieved and amended with a high rate, low rate, or no compost, and filled into 1 L pots to the brim. The low rate was chosen based on applying half of the recommended rate of N per hectare for lettuce production, which in this case was 120 kg

N ha. This translated to 2.3 g dry compost L-1 of soil. The high rate was chosen to increase the organic matter content in the low compost history soil up to the level of organic matter in the high compost history soil, which translated to 23.1 g dry compost L-

1 of soil. The compost application rates were roughly equivalent to 5.6 T/ha for the low rate, and 56 T ha-1 for the high rate on a fresh weight basis. The day before amendment, the moisture content of a sample of compost was calculated, and the fresh amendment rate was adjusted for moisture for each run.

Microbial Analysis of Soil, Compost and PGPR. A baseline analysis of the microbial population sizes was performed for the low compost history soil and the high compost history soil. Four bulk soil samples and four samples of soil in the rhizosphere of ryegrass growing in the W. Badger plots were collected from both the high and low-compost history sections. Serial dilutions of soil from each sample were performed, and 10µL of the highest dilutions were inoculated onto solid tryptic soy agar (TSA) and solid nitrogen

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free (NFb) media using the drop-plate method. These media were chosen to estimate the size of the general microbial population, and the N-fixing population, respectively. TSB plates were incubated at room temperature for 48 hours, and NFb plates were incubated at room temperature for 72 hours before counting. Counts were obtained from drops that had between 2 and 20 colonies, and average population estimates of four technical replicates for each of the four zones were calculated on a basis of g dry matter. Results are displayed in Figure 4.2. Microbial population estimates were obtained by the same method for the compost amendment and the inoculant. Average population estimates from four technical replicates of the inoculant and the compost are displayed in Table 4.3.

Greenhouse setup. Seedlings and plants were grown in an acrylic greenhouse at the

OARDC in Wooster, Ohio. For the first run, seedlings were transplanted on May 26,

2016, and plants were harvested from June 24th-25th. For the second run, seedlings were transplanted on July 3, 2016, and plants were harvested from August 1st-2nd. Greenhouse set points were 21-24 °C during the daytime, and 15-18 °C during the nighttime. Actual temperatures fluctuated between 15°C and 30°C.

A bottom-water irrigation system was set up with one tray per pot containing a dripper head and a capillary action mat. This bottom-water system was chosen to avoid the soil erosion and compaction that would occur if drippers were placed in the pots on top of the soil. For the first run, a total of 90 mL of city water was delivered by drip irrigation in 3 doses of 30 mL Due to technical problems, the irrigation system was not

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functional for the second run, so the same total volume (90 mL) was delivered into the bottom-water trays once daily by hand-watering.

Seed and transplant production. Organic Parris Island lettuce (Johnny’s Selected Seeds,

Winslow, ME), Lactuca sativa, was used as a test crop for this experiment. Pro-mix MP

Mycorrhizae Organic (Premier Tech Horticulture, Quakertown, PA) was used, and it was autoclaved (120 °C for 20 minutes) to ensure that the mycorrhizal fungi did not interfere with the PGPR inoculation.

Transplantation and Inoculation. Lettuce seedlings were transplanted into 1L pots of dry soil 3-4 weeks after seeding. Plants were inoculated with Environoc 401 (BioDyne

Midwest, Fort Wayne, IN) directly after transplant and re-inoculated 2 weeks later in accordance with the application instructions. It is a mixed inoculant of 29 proprietary strains of bacteria with the capacity to fix nitrogen, solubilize phosphorus, and produce plant-growth-promoting hormones. Product details can be found in Appendix E. The label recommended product application rate is 16 oz. acre-1, or 1.17L/ha, applied twice.

One hectare contains 1,523,000 L of soil in the top 15 cm of depth. In a typical production environment, about half of this land area will be moistened by drip irrigation or by foliar spray. Thus the product will be spread over 762,000 L of soil. Each pot in this experiment contained 1 L of soil, therefore (2340 mL product) /(762,000 L soil) =

0.00307 mL product per pot. The inoculant was diluted into 50 mL water and delivered as a root drench by pouring directly onto the root zone. The product is labeled to contain

108 cells mL-1, so approximately 3.1*105 cells were delivered to each root zone.

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Shoot Analysis. Photos of each plant were taken every two days and analyzed using

WinCAM software (Regent Instruments Inc., Ville de Québec, Canada). In order to obtain consistent images for rapid analysis, a portable lightproof photo chamber was constructed using two black plastic pots, a black 3-ring binder, and a small, battery- powered LED light (Figure 4.1). The pots were stacked on top of each other, top end down, and a small hole was cut in the top one for a camera lens. An opening for the lettuce plants was cut into the cardboard of the black binder, and the binder was attached to the rim of the large pot on the bottom. During each photo-session, leaves were gently passed through the opening at the bottom of the chamber, and the chamber rested on the rim of the lettuce pot. Due to the fixed distance from the camera, constant lighting, and good contrast between the green leaves and black background, the images were suitable for batch analysis, saving many hours of cropping and color calibration. The absolute canopy coverage from photos was estimated by placing a green 1cm2 calibration square that was photographed in the photo chamber and analyzed. Sample photos were calibrated accordingly.

Lettuce leaves were harvested after 29 days of growth, and fresh weight was taken immediately after harvesting. Whole plants were divided into two samples. One sample was frozen at -20°C for later analysis. The other sample was weighed fresh, leaf area was determined using an LI-3100 Area Meter (LI-COR, inc. Lincoln, Nebraska).

The second sample was then placed in brown paper bags and oven-dried for 2 weeks before obtaining a dry weight.

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Root analysis. Roots and adhering soil were gently separated from the bulk soil. Root zone soil was portioned into two sections: one sample was placed in 50 mL Falcon tubes and frozen at -20°C for later analysis. A second 50g sample of fresh soil was placed in plastic bags. The bags of soil were left open at room temperature to air dry for two weeks, and then weighed again to determine soil moisture. The dry samples were stored for later analysis. Once root-zone soil was removed, roots were gently scrubbed in a water bath and placed into 50 mL cups of water for later analysis with WinRHIZO software (Regent Instruments Inc., Ville de Québec, Canada). After roots were scanned and analyzed, they were blotted dry, and weighed to measure root fresh weight. Roots were oven dried for one week before dry weight measurements were taken.

Statistical Analysis. Statistical tests were performed in RStudio (Version 0.99.891 – ©

2009). The sample size was large (n=144), so Lilliefor’s test (nortest package) was used to test for normality for the following variables: leaf area, fresh leaf mass, fresh root mass, dry leaf mass, dry shoot mass, root length, average root diameter, and root volume.

Leven’s test (car package) was used to evaluate the homogeneity of variance for all variables. The tests revealed that the conditions of normality and homoscedasticity were both violated (p<0.05) for all variables except for average root diameter. However, visual inspection of the histograms did not reveal extreme skew or extremely abnormal kurtosis for any of the variables. Nevertheless, Multivariate Analysis of Variance (MANOVA) was performed with run, soil history, recent compost application, and inoculation as factors, since ANOVA is robust to non-normal data. There was a significant experimental run x recent compost application interaction for most variables, so the data from the two

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runs of the experiment were analyzed and graphed separately. Graphs were generated using the ggplot2 package in R.

Results

Soil and Compost Chemical Analysis. Soil at the Fry Farm site was classified as Wooster

Riddles silt loam, and the soil taxonomy at the W. Badger site was Canfield silt loam.

Standard soil test results from each site are listed in Table 4.1. At Fry farm, composted dairy manure had been applied to plots in strips for over 12 years. The West Badger site was chosen due to the most dramatic difference in organic matter and other soil properties due to the amendment history. Approximately 67 t/ha of hay compost was added to one half of the field, but not the other in 2014, resulting in an increase in % organic matter of 1.58. Physical and chemical properties of the compost amendment used are listed in Table 4.2.

Soil and Compost Microbial Analysis. The measured CFU of Environoc 401 (Table 4.3) was higher than advertised (Appendix E). This means that approximately 1.30*106 CFU per plant were actually delivered to each plant. Both the high and the low compost amendments delivered a much higher concentration of bacteria than did the inoculant.

Concentration of bacteria in were higher in the high compost history soil for both bulk and rhizosphere samples (Figure 4.2).

Main Effects on Growth and Biomass. Figure 4.3 depicts a time-series of canopy coverage for all treatments for 23 days after transplanting. Results of MANOVA on biomass (fresh root mass, dry root mass, fresh leaf mass, dry leaf mass, and leaf area)

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suggest that soil history and recent compost application had highly significant overall effects in both runs. Inoculation had no significant effect in either run (Table 4.4). As expected, lettuce grown in soils with the higher rate of historical compost had a greater area of canopy coverage (Figure 4.3), and greater fresh and dry root and shoot mass

(Figure 4.4). Also as expected, recent compost application affected most parameters in a rate dependent manner, with a larger increase in growth observed for the higher application rate.

Main Effects on Root Growth. Consistent with the effects on shoot growth, the results of

MANOVA on root characteristics (root length, average root diameter, root surface area, and root volume) suggest that historical and recent compost application had significant effects for both runs. The effect of inoculation was not significant for either run (Table

4.5). Both recent and historical compost addition resulted in higher values for most root characteristics (Figure 4.5).

Interactive Effects of Soil History and Compost Amendment. The soil history × compost application interaction had a significant effect on biomass characteristics in run 1, but not in run 2 (Table 4.4). Root growth was not significantly affected by the soil history × compost interaction fore either run (Table 4.5). Lettuce growth response due to recent compost application, especially for the higher rate, was greater in magnitude in the low compost history soil than it was in the high compost history soil for most parameters.

This soil history × compost interaction is apparent in run 1 for canopy cover (Figure 4.3), fresh root and shoot mass (Figure 4.4), and root characteristics (Figure 4.5). However, the

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effects of recently added compost were not as dramatic or consistent in the second run as they were in the first run. No clear numerical trends regarding its effects on lettuce were observed for shoot or root variables (Figures 4 and 5).

Discussion

This experiment was designed to test the hypothesis that the magnitude of plant growth response to inoculation is affected by compost application history and recent compost addition. Compost application history, recent compost addition, and their interaction had highly significant (p < 0.001) effects on most parameters (Tables 4.5 and

4.5), but inoculation had no statistically significant, or even numerically consistent effects. Overall, trends in numerical differences due to inoculation were inconsistent between runs. Since the inoculant had no discernible effect on plant growth under any of the six soil environments, the hypothesis remains untested.

Sometimes, the effects of inoculation can diminish as plants age (Kazi et al. 2016), so

WinCAM photos tracking canopy development were used to investigate the possibility of effects of the inoculant in earlier stages of plant growth. A graph of the change in canopy coverage over time with inoculation and soil history as factors showed that inoculated plants had a slightly higher area of canopy coverage than non-inoculated plants for the low compost history soil, but not for the high compost history soil (Figure 4.6). This observation is consistent with the idea that inoculants tend to perform better in soils of low to intermediate quality. This result could also be explained by the fact that the microbial populations in the rhizosphere of ryegrass growing in the soil at the West

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Badger site with a low compost application history were observed to be lower than those of plants growing in the high compost application history soil (Figure 4.2). Thus, the inoculant may have been able to colonize the host plant more easily due to decreased competition with native bacteria. However, the same trend was not observed in the second run (Figure 4.6), so the result may be spurious and/or mitigated by experimental or environmental conditions.

Comparison with results of previous trials. Preliminary trials were performed with multiple inoculants, including the one tested in this study. In those experiments, marginally significant effects of the same inoculant were seen on broccoli yield (Figure

B.1), and significant effects were observed on the fresh root mass of spinach (Figure

C.1). The results of these trials were consistent with the idea that biofertilizers perform best in soil environments of low to moderate quality.

In the first experiment, four commercially available products (Azospirillum brasilense, Pseudomonas fluorescens, Bacillus amyloliquefaciens, and a 29-strain mix) and a control were tested on tomato growth and yield under normal nutrient management practices at four different Ohio organic farms and a field station at OARDC in Wooster,

Ohio (Appendix A). Slight increases in early season growth, fruiting, and flowering were observed for some inoculants on several farms. However, these differences did not lead to significant or even numerically consistent yield increases.

The second experiment tested the combined effects of nitrogen fixing inoculants and compost amendments on an organic broccoli farm. Four products (Azospirillum

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brasilense, Azotobacter spp., a 12-strain mix, and a 29-strain mix) and a control were tested in a full factorial design with two amendments (composted chicken manure, and a mixed compost amendment), and an untreated control. Yield was lowest in plots that received neither compost nor inoculation, highest in plots that received compost, and intermediate in plots that were inoculated, but not amended with compost. No additive effects of inoculation and fertilization were observed. Both compost amendments significantly (P < .05) increased yield in comparison to the control. When no amendment was added, the inoculants increased yield by 12.9%-64.6%, with only the best performing biofertilizer resulting in a significant yield increase over the control (See Appendix B for details).

The third experiment tested the combined effects of N-rich fertilizers and inoculation on spinach. A full factorial design of three fertilization levels (no fertilizer, bloodmeal, and Chilean nitrate) with three microbial levels (control, Azospirillum brasilense, and a

29-strain mix of PGPR) was used. Neither the fertilizers nor the inoculants significantly increased yield, and leaf tissue nitrate values were high. Thus, conditions were not conducive to addressing the question due to the fact that background soil quality was already very high. To get a clearer picture of plot-to-plot variation in plant growth response to inoculation, the percent increase yield due to inoculation was graphed against the control yield for each plot, irrespective of fertilizer treatment. Using the control yield as the x-axis provides an integrative proxy for background soil quality and other factors that affect plant growth. This method revealed a clear negative relationship between the control yield and the percent yield increase due to inoculation for both roots and shoots,

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and for both inoculants tested (Figure C.3). The same pattern was observed for the broccoli data (Figure B.2).

In summary, plant growth response to inoculation was not detected when plants were grown under fully fertilized conditions. Response to inoculation was generally greater when the control yield was lower, possibly due to lower soil quality. These findings are consistent with the idea that plant growth response to inoculation is greatest at intermediate levels of soil quality. However, two conditions must be satisfied in order to test this hypothesis directly: (1) it would be necessary to establish a range of soil quality from very high to very low, and (2) successfully apply and inoculant that produces a significant effect on plant growth. The first condition was not met in the preliminary trials, and the second condition was not met in the greenhouse study.

Several reasons may explain the lack of plant growth response to inoculation in this greenhouse experiment. Well water was used as a diluent in all three experiments, so it is unlikely that the carrier for inoculant delivery would explain the discrepancy in performance. Application timing is a possible explanation. The inoculant was applied as a seed treatment in the spinach experiment, but not the lettuce experiment. However multiple methods of Azospirillum inoculation have shown to have similar efficacy in other studies (Fukami et al. 2016).

Inoculum concentration. Inoculum concentration is, perhaps, the most compelling reason for the lack of plant growth response to inoculation. In the broccoli experiment, the inoculant was applied at twice the recommended rate. Furthermore, doses were delivered

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directly to the base of each plant. Since the plant density for lettuce is much higher, the same rate per area translates to a smaller dose of inoculant for each lettuce plant. The dosage for this experiment was chosen to simulate real growing conditions. The label- recommended rate for drip irrigation foliar spray in commercial agricultural applications was followed. In this case, approximately 106 cells of inoculum were delivered to each plant (Table 4.3), which is typical of the label recommendations for commercial products, and consistent with some application rates reported in the literature (Fukami et al. 2016).

Some research has even suggested that the colonization success of inoculation is independent of initial dose (Normander and Hendriksen 2002).

However, other studies have shown that higher rates are more effective. Zhou et al.

(2014) found that an intermediate concentration of 107 CFU/mL was most effective for three common methods of application: seed treatment, soil drench, and root drench. In that same experiment, seed treatment at a high concentration (108 CFU/mL) actually lead to a significant decrease in biomass, and the high concentration of root and soil drench was not as effective as the intermediate concentration. Overall, the intermediate concentration was most effective, but significant increases in growth parameters were observed at the lower concentration (106 CFU/mL) as well (Zhou et al. 2014). In a study on the effects of Bacillus amyloliquefaciens on Chinese cabbage, inoculation was more effective at a rate of 106 cells per seedling than it was at a rate of 108 cells per seedling

(Ramírez and Kloepper 2010). These results are consistent with a synthesis of older

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research on Azospirillum, indicating that the optimum application concentration is 107 cells per seed or seedling (Okon and Labandera-Gonzalez 1994).

The strength of suspension delivered to plants in this experiment was approximately

2.6*104 cells/ml in 50 mL doses. The background soil microbial population levels for this experiment were approximately 107 CFU g-1 of soil (Figure 4.2). A 1 L pot of soil holds about 1000g of dry soil, each pot of soil contained on the order of 1010 bacteria. Thus it is likely that the 1.30*106 CFU applied to each pot (Table 4.3) were overpowered by the native microbial population, and thus had no measurable effect. In future experiments, it would be a good practice to do a preliminary dosage experiment with minimal replication to ensure that the inoculum concentration is appropriate for the specific microorganisms, host plant, and rooting environment under study. A recent study of the effects of A. brasilense on wheat yields suggests that this fine-tuning of dosage is particularly important for field applications (Fukami et al. 2016).

While increasing the application rate of a commercial product by several orders of magnitude may be informative for research purposes, this strategy is not economically feasible for growers. It is unclear whether producing higher concentration inoculants and/or increasing the recommended product application rate would be cost prohibitive for manufacturers. The per-plant application rates of all products used in this body of work were calculated based on advertised CFU and recommended application rate (Table 4.6).

They are lower than the 107 mL plant-1 concentration that is has been found to be effective in the literature. Seed treatments for directly sown crops, or cell tray drenches

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for transplanted crops, may be a more cost-effective way to deliver a higher concentration of inoculum.

A study of strains of Bacillus and Pseudomonas suggested that the persistence of the inoculum in soilless potting mix depends on the species (Yan, Reddy, and Kloepper

2003). One study suggests that the medium in which tomato transplants are grown can have lasting effects on the soil microbial community (Jack et al. 2011). Further research with Bacillus subtilis and Bacillus amyloliquefaciens inoculants suggests that they can successfully promote plant growth after transplant, and there were no measureable benefits of re-applying the inoculant in the field later in the season (Kokalis-Burelle,

Kloepper, and Reddy 2006). Thus, seed treatments and transplant drenches may be more reliable means of delivering high concentrations of microorganisms to crops.

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Tables

Table 4.1 Soil test results and organic matter by loss on ignition for candidate plus and minus compost history soils. Composted dairy manure has been added in strips of fields 903 and 910 for over a decade. Approximately 67 t/ha of hay compost was added to half of the W. Badger field in 2014. Rows in gray show the difference in parameters between soil treated with and without compost.

STAR lab Bray P-1 Ammonium Acetate Extract µg g-1 µg g-1 µg g-1 OM

Sample pH LTI P K Ca Mg % Fry 903 control 6.56 65.5 25.5 109.0 958.4 211.7 2.91 Fry 903 + dairy compost 6.86 69.3 50.4 336.8 1140.0 247.1 3.83 Fry 903 difference 0.30 3.80 24.9 227.8 181.6 35.4 0.92 Fry 910 control 7.22 70.0 38.3 101.9 1042.0 185.2 2.48 Fry 910 + dairy compost 7.43 70.0 66.8 278.2 1220.0 206.7 2.97 Fry 910 difference 0.21 0.00 28.5 176.3 178.0 21.5 0.49 W. Badger control 6.84 64.3 48.7 111.9 1009.0 211.9 2.86 W. Badger + hay compost 6.98 70.0 121.0 693.6 1367.0 239.7 4.44 W Badger difference 0.14 5.70 72.3 581.7 358.0 27.8 1.58

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Table 4.2 Physical and chemical properties of the dairy manure compost amendment (average of 3 samples).

pH SS % Volatile mS % Solids % Ash Solids 8.1 13.98 44.06 39.29 60.71

% Total N % Total C NO3-N NH3-N OM µg g-1 µg g-1 % 3.41 33.87 1870.23 28.27 58.25

P K Ca Mg S Al µg g-1 µg g-1 µg g-1 µg g-1 µg g-1 µg g-1 8057.5 50359.8 42220.9 13341.4 7114.0 1963.6

Cu Fe Mn Mo Na Zn µg g-1 µg g-1 µg g-1 µg g-1 µg g-1 µg g-1 100.7 4296.8 395.8 4.4 3878.5 263.1

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Table 4.3 Results of microbial analysis of compost and biofertilizer amendments. TSA is a general medium, and NFb is selective for nitrogen fixing bacteria.

Starting concentration Cells delivered to each plant Microbial Environoc Compost Environoc Low compost High compost -1 -1 -1 -1 -1 population (cells mL ) (cells dry g ) (2.34 L ha ) (5.6 T ha ) (56 T ha ) 8 9 6 9 10 General (TSA) 4.25*10 1.07*10 1.30*10 2.47*10 2.47*10 8 8 5 8 9 N-fixing (NFb) 1.45*10 1.36*10 4.44*10 3.14*10 3.14*10

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Table 4.4 Results of MANOVA with fresh root mass, dry root mass, fresh leaf mass, dry leaf mass, and leaf area as variables. Factors include history (hist), recent compost application (comp), and inoculation (inoc), and their interactions.

Run 1 Factor Pillai's trace F- value P-value hist 0.84 106.97 0 *** comp 0.77 13.25 2.20E-16 *** inoc 0.02 0.42 0.86 hist×comp 0.34 4.27 3.78E-06 *** hist×inoc 0.07 1.59 0.15 comp×inoc 0.05 0.52 0.90 hist×comp×inoc 0.07 0.75 0.70 Run 2 Factor Pillai's trace F- value P-value hist 0.89 170.34 2.20E-16 *** comp 0.28 3.4 1.25E-04 *** inoc 0.03 0.55 0.77 hist×comp 0.12 1.39 0.17 hist×inoc 0.04 0.95 0.46 comp×inoc 0.10 1.13 0.33 hist×comp×inoc 0.07 0.74 0.72

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Table 4.5 Results of MANOVA with WinRHIZO data (root length, average root diameter, root surface area, and root volume) as variables. Factors include history (hist), recent compost application (comp), and inoculation (inoc), and their interactions.

Run 1 Factor Pillai's trace F- value P-value hist 0.49 31.38 2.20E-16 *** comp 0.58 13.12 6.83E-16 *** inoc 0.06 1.96 0.1 hist×comp 0.3 5.64 1.33E-06 *** hist×inoc 0.03 0.94 0.44 comp×inoc 0.04 0.61 0.77 hist×comp×inoc 0.05 0.78 0.62 Run 2 Factor Pillai's trace F- value P-value hist 0.54 38.27 2.20E-16 *** comp 0.22 4.08 1.37E-04 *** inoc 0.02 0.64 0.63 hist×comp 0.14 2.39 0.02 * hist×inoc 0.05 1.86 0.12 comp×inoc 0.01 0.23 0.99 hist×comp×inoc 0.02 0.28 0.97

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Table 4.6 Estimated CFU delivered based on two application methods. The broadcast method assumes that half of the land area is drenched and the plant occupies a 1 L rooting volume. The cell-tray application scenario assumes a density of 37,000 plants/ha.

Advertised CFU/plant CFU/plant Product CFU Rate/ha CFU/ha (broadcast) (cell-tray) Azos Blue 1.00E+09/mL 1170 mL 1.17E+12 1.53E+06 3.15E+08 BioYield 3.00E+06/mL 4940 mL 1.48E+10 1.95E+04 4.00E+06 Biogenesis 1 NP 1.20E+06/g 1120 g 1.35E+09 1.77E+03 3.63E+05 Bioplin 1.00E+07/mL 618 mL 6.18E+09 8.11E+03 1.67E+06 Environoc 401 1.00E+08/mL 2240 mL 2.34E+11 3.07E+05 6.31E+07 Hydroguard 1.00E+04/mL 2240 mL 2.34E+07 3.07E+01 6.31E+03

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Figures

Figure 4.1 Sketch of the portable photo chamber used to track changes in canopy cover over time. A small hole for a camera lens is cut in the bottom of a large pot, and a light source is attached to the inside of the pot. A hole about the size of the plant is cut into a sturdy frame, such as a wing of a three ring binder. The frame is then attached to the rim of the pot to block out light. The plant should be passed gently through the hole on the frame and photographed.

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Figure 4.2 Results of microbial analysis of soils and rhizosphere of ryegrass growing in soil with low and high compost application history. Bulk soil populations were estimated on a g dry soil basis, and rhizosphere population were estimated on a g dry root basis. TSA is a general medium, and NFb is selective for nitrogen fixing bacteria. The mean values from four randomly sampled zones with standard error bars are displayed.

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Figure 4.3 Change in canopy cover over time for the six soil treatments and the two inoculation treatments. Data are shown separately for run 1 and run 2. Canopy cover was measured non-destructively by taking areal photos of plants and analyzing images in WinCAM.

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Figure 4.6 Change in canopy cover over time for two inoculation (inoc) treatments, and two rates of historical compost amendment (hist) for run 1 and run 2.

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Chapter 5: Conclusions

Ecologically context-specific performance remains a persistent problem for the successful application of microbial biostimulants. Much research has gone into understanding the mechanisms by which microorganisms promote plant growth in controlled environments. However, few clear trends have emerged from applied research.

A framework developed by Leary (1985) for evaluating progress in science provides interesting insight into the development of microbial biostimulant research. Leary describes the following seven axioms to assess research productivity:

Axiom 1. A unit of scientific productivity is a valid answer to a scientific question.

Axiom 2. Not all scientific questions are of the same difficulty to answer.

Axiom 3. Explanatory questions are more difficult to answer than predictive questions, which are, in turn, more difficult to answer than descriptive questions.

Axiom 4. Scientific disciplines are at different stages of development as given by the class of question typifying research in the discipline.

Axiom 5. Scientists within disciplines are at different stages of personal development as evidenced by the class of question they answer successfully.

Axiom 6. Answering propositions comes with varying kinds of existential quantifiers or universes.

Axiom 7. Answering propositions with more general quantifiers (universal or bounded universal) have a better chance of becoming law level statements than singular answering propositions or ones with existential quantifiers (indefinite or definite). 72

The first axiom states that the smallest unit of scientific productivity is a valid answer to a scientific question. In the case of biostimulants, this answer could be expressed by some variation of the following form: “inoculant X has effect Y on plant growth under conditions Z.” Axioms 2 and 3 suggest that explanatory questions are the hardest to answer. The form of an explanatory question has an additional component:

“inoculant X has effect Y on plant growth under conditions Z by way of mechanism W.”

Leary argues that explanatory questions are the most valuable because explanations generally include descriptions and predictions. However, the descriptions and predictions derived from explanatory research on the mechanisms of biostimulants can seldom be applied with confidence to real world conditions. It has been established, for example, that pseudomonads are capable of improving plant phosphorus nutrition by the excretion of phytases. Whether they will actually do so when formulated into a product and applied to a farmer’s field depends on many factors. Thoroughly investigating all factors that affect biofertilizer performance and their interactions under field conditions is neither technologically feasible nor practically necessary.

Axioms 4 and 5 suggest that the stage of development of entire disciplines—and individual scientists within disciplines—can be evaluated based on the types of questions they address. The relevance of this axiom is questionable for applied science, since sophisticated empirical models of factors affecting inoculant performance that have predictive value may be more useful than a mechanistic understanding. Axioms 6 and 7

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address the generality of answers to research questions. Leary notes that the more general the answer, the closer it is to becoming a law-like statement.

Given these axioms, Leary cross-tabulated the difficulty of the question addressed with the generality of the answer to provide a grid for assessing research productivity

(Figure 5.1). In the biostimulant and biofertilizer literature, substantial progress in basic biofertilizer research has been made along the “B” axis. Questions range from descriptive to predictive to explanatory (including cellular mechanisms of action for particular plant- microbe-environment combinations).

In contrast, limited progress has been made along the “A” axis. Robust, longstanding, generalizable results regarding the factors influencing the reliability and performance of inoculants are few. Many biostimulant research questions take the singular indefinite existential form: this particular inoculant had these particular effects on this particular plant variety under these particular environmental conditions. Development along the

“A” axis is arguably the most important for extension research, education and outreach, since growers face a variety of unique environmental conditions. Generally applicable guidelines for the successful application of microbial biostimulants are needed. A study that documents the effect of an inoculant under one set of growing conditions may only be relevant to a small portion of stakeholders.

A search of review articles published on microbial biostimulants, biofertilizers and/or

PGPR between 2014-2016 was conducted. Abstracts of each article were used to classify the type of research recommended. Articles were classified as basic if they called for a

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more thorough understanding of mechanisms. Articles were classified as technological if they called for improved screening of novel microorganisms, product formulation or delivery methods. Articles were classified as applied if they called for improved understanding of plant, soil, and environmental factors that affect inoculant performance.

Of the 21 articles identified, 8 called for more technological research on microbial biostimulants, 6 called for more basic research, 4 called for a combination of basic and applied research, and only 3 called for primarily applied research (Table 5.1).

There are many explanations for the underdevelopment of applied research in microbial biostimulant research, or the “A” axis of Leary’s framework. In reality, it is not a simple linear axis, but a complex, multi-dimensional matrix of factors including inoculant composition, concentration, host plant, application method, environmental conditions, and their interactions. In order for farmers to benefit reliably from the use of microbial biostimulants, more systematic research into factors that explain variability in their performance is required.

Two general approaches can be utilized to better understand the conditions affecting inoculant performance: the theoretical approach would be to conduct factorial experiments in highly controlled settings comparing inoculated plants to non-inoculated plants across various gradients (nutrient concentrations, soil texture, temperature, etc.).

The empirical, and perhaps more practical approach, would be to extensively document the biotic and abiotic conditions of simple field experiments in which inoculated plants are compared with non-inoculated controls. Multivariate analysis could then be used to

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tease apart factors that affect the performance of inoculants. Factors that are identified as most influential by the empirical models could then be targeted for more through theoretical research.

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Tables

Table 5.1 Reviews published from 2014-2016 on biofertilizers, biostimulants, and PGPR. Recommended Citation Title research Assessment of affinity and specificity of Azospirillum for (Pereg, de-Bashan, Applied plants and Bashan 2016) From the Lab to the Farm: An Industrial Perspective of (Parnell et al. 2016) Plant Beneficial Microorganisms. Technological Bacterial-mediated drought tolerance: Current and future (Ngumbi and prospects Basic/Applied Kloepper 2016) Methylotrophic bacteria in sustainable agriculture Basic (Kumar et al. 2016) Role of Plant Growth Promoting Rhizobacteria in (Vejan et al. 2016) Agricultural Sustainability-A Review Technological Integrated plant nutrient system - with special emphasis on (K P and R 2016) mineral nutriton and biofertilizers for Black pepper and cardamom - A review Technological Plant-growth-promoting rhizobacteria: drought stress (Kaushal and Wani alleviators to ameliorate crop production in drylands Technological 2016) Implementing plant biostimulants and biocontrol strategies (Le Mire et al. 2016) in the agroecological management of cultivated ecosystems. A review Applied Plant growth-promoting rhizobacteria act as biostimulants (Ruzzi and Aroca in horticulture Applied 2015) Novel plant growth promoting rhizobacteria-Prospects and (Chauhan et al. 2015) potential Basic Role of plant growth promoting rhizobacteria in sustainable (Zaidi et al. 2015) production of vegetables: Current perspective Basic/Applied Plant growth promoting rhizobia: challenges and (Gopalakrishnan et al. opportunities Basic 2015) Unexploited potential of some biotechnological techniques (Vassilev et al. 2015) for biofertilizer production and formulation Technological Plant growth-promoting bacteria as inoculants in (Souza et al. 2015) agricultural soils Basic PGPR Interaction: An Ecofriendly Approach Promoting the (Bishnoi 2015) Sustainable Agriculture System Technological (“Biofertilizers in Pakistan: Initiatives and Limitations” Biofertilizers in Pakistan: Initiatives and Limitations Technological 2016) Plant-growth-promoting rhizobacteria to improve crop (Paul and Lade 2014) growth in saline soils: a review Basic/Applied (Malusá and Vassilev A contribution to set a legal framework for biofertilisers Technological 2014) Biofertilizers function as key player in sustainable (Bhardwaj et al. 2014) agriculture by improving soil fertility, plant tolerance and crop productivity Basic Proven and potential involvement of vitamins in (Palacios, Bashan, and interactions of plants with plant growth-promoting bacteria- de-Bashan 2014) an overview Basic The role of mycorrhizae and plant growth promoting (Nadeem et al. 2014) rhizobacteria (PGPR) in improving crop productivity under stressful environments Basic/Applied

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Figures

Figure 5.1 A framework for evaluating research productivity. Specific descriptive questions are the easiest to answer, whereas universal why questions are the hardest and most valuable. Adapted from Leary (1985).

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Appendix A: The Effects of Microbial Biostimulants on Organic Tomato Growth and Yield Under Normal Fertility Management Practices

Objective

Biofertilizers are beneficial microorganisms that can promote plant growth by increasing the availability and/or supply of nutrients to crops. In order for plant growth response to inoculation to occur, the biofertilizer must alleviate one or more factors that limit plant growth. It is unclear whether biofertilizers survive and perform their functions reliably in complex on-farm environments. The purpose of this study was to test a range of commercially available biofertilizers on four organic farms in different regions of

Ohio. A representative set of biofertilizer treatments ranging from single species to multi- species were chosen for this study.

Materials and Methods

Experimental Design. The design was randomized complete block. Due to small plot sizes, replication was not possible at two of the four farm sites. The data presented below are only from replicated sites. Treatments were randomized and replicated three times for each variety at the Ashland site, and 8 times at the Fremont site.

Site details. Soils were sampled at each of the replicated sites, and classified according to the USDA-NRCS Web Soil Survey classification. At the Fremont site, Mariana F1

88 tomatoes were seeded in the greenhouse on April 29th, 2015, and transplanted into plastic-covered raised beds double rows on June 5th. Plants were grown without trellises, and in-row spacing was 46 cm. At the Ashland site, Amish Paste and Cherokee Purple tomatoes were seeded in the greenhouse on June 4th, and transplanted in staggered double rows into flat plastic-covered beds on July 1st. Plants trellised with stakes and wires, and in-row spacing was 30 cm.

Product testing and Inoculation. Each product was tested for the number of colony forming units (CFU) on 1/10 strength tryptic soy agar. The number and classification of distinct colony types was recorded. Inoculants were applied to tomato plants at label recommended rates on four farms within 48 hours of transplant (Appendix D). Table A1 contains product application rates and other details.

Data Collection. Vegetative growth, fruiting, flowering, and yields were recorded at each site between 3-4 weeks after transplanting. At the Ashland site, tomatoes were harvested about once a week as they ripened, and the farmer recorded yields. At the Fremont site, getting yield data was not feasible. Instead, fruit counts were recorded at most harvests, and yield was estimated by multiplying fruit counts by the mass of a typical Mariana F1 tomato. Analysis of variance and LS means separation tests were run in SAS (version 9.3;

SAS Institute, Cary, NC).

Results and Discussion

Soil test results are located in Appendix E. Ashland site was Centerburg silt loam, and the soil at the Fremont site was Dixboro-Kibbie complex. All products tested

89 were at or above the advertised CFU, though two of the three single-species products were not pure cultures (Table A2). Based on visual observation, the diverse product did not appear to have as many colony types as advertised on the label: 29 species were listed and only 5 clearly distinct colony types were observed. Slight increases in early season growth, flowering, and fruiting were observed at some sites. Overall, early season data were inconsistent, and no clear trends were replicated across sites. Furthermore, the yield data did not show any significant or even numerically consistent yield increases due to inoculation (Table A3). Spring of 2015 was exceptionally wet, so the weather could have contributed to the lack of treatment effects. Sample sizes on many farm sites were also small, so the results are inconclusive.

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Tables

Table A.1 Product information for microbial biostimulants. Product manufacture and carrier information is located in Appendix E.

Product name Species Label recommended Cost per application rate hectare

BioYield Pseudomonas 4.94 L/ha $494 fluorescens

Hydroguard Bacillus 2.34 L/ha $74 amyloliquefaciens

Azos Blue Azospirillum 1.17 L/ha $141 brasilense

Combination of Combination of 8.44 L/ha $709 products 1-3 products 1-3

Environoc 401 29-strain mix 2.34 L/ha $45

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Table A.2 Serial dilutions of each product were plated on 1/10 strength tryptic soy agar, and the number of colony forming units (CFU) was calculated. Colony types were also counted and classified by observation under a microscope.

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Table A.3 Early season data and yield data for each tomato site and variety combination. Treatments are (1) control, (2) Azos Blue, (3) Hydroguard, (4) BioYield, (5) Mix of products 2-4, and (6) Environoc. A * indicates a significant increase over the water control; *L indicates a significant decrease in comparison to the control.

Location: Ashland, Variety: Amish Paste Treatment 1 2 3 4 5 6 Stem length (m) 1.52 1.43 1.73 1.53 1.31 1.44 Flower number/plant 0.42 1.67* 1.06 1.25 1.33 1.75* Fruit number/plant 0.08 0.42 0.38 0.58 0.08 0.67 Stem diameter (mm) 8.91 7.2*L 7.97 8.08 6.92*L 7.68*L Yield (kg/ha) 15623.76 18553.22 18553.22 12206.06 10741.34 11717.82 Location: Ashland, Variety: Cherokee Purple Treatment 1 2 3 4 5 6 Stem length (m) 1.14 1.1 1.13 1.18 1.16 1.23 Flower number/plant 2.75 1.17*L 1.25 2.08 2.17 2.42 Fruit number/plant 0 0.08 0 0.25 0.17 0.08 Stem diameter (mm) 7.44 7.08 7.41 7.61 7.83 8.22 Yield (kg/ha) 15135.52 12694.31 8788.37 13182.55 16600.25 17576.73 Location: Fremont, Variety: Mariana F1 Treatment 1 2 3 4 5 6 Stem length (m) 1.96 1.84*L 1.96 1.87 1.87 1.92 Flower number/plant 7.5 7.05 7.3 7.63 6.75 8.45 Fruit number/plant 1.43 1.23 1.48 1.88 1.69 2.08 Stem diameter (mm) 9.98 9.75 10.27 9.8 9.68 10.28 Yield (kg/ha) 62983.29 66889.23 67865.72 63959.78 67377.48 61518.56

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Appendix B: The Interactive Effects of Microbial Biostimulants and Compost Amendments on Organic Broccoli

Objective

Nitrogen is a major limiting nutrient in organic farming systems, and nitrogen-rich organic fertilizers can be costly (Gaskell and Smith 2007). Previous studies have suggested that farmers may be able to cut back on fertilizer without reducing yield by inoculating their crops with biofertilizers (Adesemoye, Torbert, and Kloepper 2009;

Dobbelaere et al. 2002; Kapulnik et al. 1981). However, yield response due to inoculation is highly dependent on host plant, soil properties, and climactic factors, so reducing or replacing fertilizers with biofertilizers can be economically risky. The purpose of this study was to test the individual and combined effects of commercially available biofertilizers and organic fertilizers on broccoli yield. Four microbial inoculants and were tested in the presence and absence of two organic fertilizers.

Materials and Methods

Experimental design. A split plot design was used with three fertility levels (composted chicken manure, a mixed compost amendment of the same NPK value, and an unfertilized control), and five microbial levels (two N-fixing inoculants, two mixed

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species inoculants, and an uninoculated control). Each plot consisted of 12 plants and was replicated four times.

Site details and cultivation details. This experiment was conducted on an organic farm in

Fremont, Ohio. Broccoli was seeded in the greenhouse on July 4th and planted in staggered double rows in raised beds on July 22nd. In-row spacing was 30 cm.

Inoculation. Products were diluted in well water and applied in 50 mL doses at twice the recommended application rate. The four inoculants were Azos Blue, Bioplin, Biogenesis

1 NP and Environoc 401, and product details are listed in Table B.1.

Data Collection. Heads of marketable size were harvested. Fresh weight, number of heads, and head diameter were recorded. Analysis of variance and LS means separation tests were run in SAS (version 9.3; SAS Institute, Cary, NC).

Results and Discussion

Both organic fertilizers significantly (P < .05) increased yield in comparison to the control, and both performed equally well. Where no fertilizer was added, the inoculants increased yield by 13%-65%, with only the highest performing biofertilizer resulting in a significant yield increase over the control. The two mixed inoculants resulted in a greater yield increase than the inoculants that contained only nitrogen fixing bacteria. No additive effects of inoculation and fertilization were observed, and inoculation generally decreased yield where fertilizer was added, but not significantly. This negative effect may be due to stimulation of unnecessary root growth at the expense of shoot growth.

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To visualize plot-to-plot variation in plant growth response to inoculation, the percent increase yield due to inoculation was graphed against the control yield for each plot, irrespective of compost amendment. Using the control yield as the x-axis provides integrative proxy for background soil quality and other factors that affect plant growth.

This method revealed a clear negative relationship between the control yield and the percent yield increase due to inoculation (Figure B.2).

On average, inoculation resulted in a much higher return on investment per dollar spent than did amendments ($82 vs. $14). However, fertilization was still more profitable because biofertilizers did not fully compensate for the yield hit taken by reduced fertilizer application. See table and calculations below.

Yield response to fertilizer generally increases with rate, whereas yield response to inoculation does not. Regardless of application rate, the potential yield increase due to inoculation will not be as high as the yield increase due to full fertilization. In this case, inoculants also don’t seem to have any effect in addition to full fertilization (Table B2).

Thus, fertilization will be more profitable than inoculation unless (1) inoculation fully compensates for the yield hit due to reduced fertilizer, or (2) the price of the crop is extremely low ($0.75/kg in this case).

Biofertilizers are only a fraction of the cost of organic fertilizers, so their potential return on investment is very high. However, their performance is notoriously context- dependent (Song et al. 2015), and the conditions under which they perform most consistently are not well understood (Díaz-Zorita and Fernández-Canigia 2009). In this

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study, inoculation significantly increased yield when no fertilizer was added, suggesting that biofertilizers may have potential in low-input systems.

Some questions for further study include: Will inoculants be of greater economic value at intermediate soil quality levels? For example, inoculants might fully compensate for the yield hit taken by reducing fertilizer by 75% or 50% instead of cutting it out entirely. Previous studies have suggested this potential in many greenhouse and some field trials. However, this effect is not always seen, and it is unclear whether factors such as soil type, percent organic matter, or mineral nutrient content can predict whether a response to inoculation is expected.

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Tables

Table B.1 Details of products used in the broccoli study. Product manufacture and carrier information is located in Appendix E.

Product name Species Application rate Cost per hectare

Azos Blue Azospirillum brasilense 1.17 L/ha $140

Bioplin Azotobacter spp. 0.617L/ha $49

Environoc 401 29-strain mix 2.34 L/ha $44

Biogenesis 1 NP 12-strain mix 1.12 kg/ha $ 30

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Table B.2 Comparison of the average costs and benefits of the four inoculants and two fertilizers tested. Economic gain and return on investment were calculated as follows: Economic gain = yield increase due to treatment (kg/ha)*price of broccoli ($/kg) – cost of treatment ($/ha) Return on investment = economic gain ($/ha) / cost of treatment ($/ha)

$/ha $/ha $/ha Yield gain Cost of gained for gained for gained for (kg/ha) due to treatment/ $2.75/kg $3.30/kg $0.75/kg Treatment treatment ha crop crop crop Microbial biostimulants 2,447 $198 $6,548 $7,899 $1,637.36 Compost amendment 4,701 $1,914 $11,043 $13,637 $1,610.39

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Figures

1.2 ab ab ab ab ab ab ab 1 ab abc abc abc abc Control abc 0.8 bc Azos Blue c 0.6 Bioplin

Environoc 401 0.4 Biogenesis 1

Broccoli Yield (kg/m ^2)(kg/m Yield Broccoli 0.2 NP

0 No amendment Composted Mixed compost chicken manure amendment

Figure B.1 Broccoli yield for different amdendment and inoculant treatments. The same letter above two bars indicates that the means are not significantly (p<0.05) different.

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500 Azos Blue 400 y = -253.2x + 269.17 300 R² = 0.54262

200

100 Yield response

to inoculation (%) inoculation to 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 -100 Yield of the uninoculated control (kg/m^2)

500 Bioplin 400

300 y = -38.667x + 37.613 R² = 0.14189 200

100 Yield response

to inoculation (%) inoculation to 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 -100 Yield of the uninoculated control (kg/m^2)

500 Biogenesis I NP 400

300 y = -209.34x + 222.62 R² = 0.76311 200

100 Yield response

to inoculation (%) inoculation to 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 -100 Yield of the uninoculated control (kg/m^2)

500 Environoc 401 400

300 y = -194.32x + 209.83 R² = 0.52892 200

100 Yield response

to inoculation (%) inoculation to 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 -100 Yield of the uninoculated control (kg/m^2)

Figure B.2 Broccoli yield response to inoculation with four products across a soil quality gradient. Yield response was calculated as % increase relative to the non-inoculated control: (Inoculated-Control)*100/Control. Graphed values represent individual plots, not treatment averages. 101

Appendix C: The Interactive Effects of Microbial Biostimulants and Nitrogen-Rich Amendments on Organic Spinach

Objective

Nitrogen is a major limiting nutrient in organic farming systems, and N-rich fertilizer inputs can be costly. Inoculation with N-fixing bacteria may be an effective strategy for reducing or replacing other N inputs. In our previous on-farm research two inoculants containing N-fixing bacteria notably increased yield, but their effects were diminished when fertilizer was added (Figure B.1). Several other studies suggest that N-fixing bacteria have the most dramatic effects on plant growth under low levels of N levels, but little is known about how different sources of N might impact their survival and efficacy in the rhizosphere. Spinach was chosen as a host plant due to its high N requirements and tendency to accumulate nitrates.

The purpose of this study is to compare the performance of two commercially available N-fixing microbial inoculants under different sources of nitrogen fertilizer.

Chilean nitrate (sodium nitrate) provides readily available nitrate to the plant, whereas blood meal must first be mineralized to ammonium before it can be absorbed. The difference in availability and composition of these two N sources would be expected to interact differently with N-fixing bacteria.

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Materials and Methods

Experimental Design. A split-plot design with three fertilizer treatments (main plot) and three inoculant treatments (subplot). Each treatment was replicated six times. The three soil nutrient levels were as follows: no fertilizer, organic N (blood meal (12-0-0), and soluble N (Chilean nitrate (16-0-0)). The three microbial levels were as follows: non- inoculated control, N-fixing inoculant: Azos Blue (Azospirillum Brasilense), diverse inoculant including N-fixing bacteria: Environoc 401 (29 strains).

Site Details and Cultivation. Spinach was planted in a 30 x 80 ft. high tunnel on October

11th. Each plot was 5x9ft and 4x4ft samples were taken at each harvest. Plant spacing was

6-inch between row and 2-inch in-row.

Inoculation and Soil Amendment. Spinach was inoculated 3 times according to the label application instructions: Seed treatment at planting, root drench at the 2-leaf stage, and a root drench at the 4-leaf stage. Fertilizer rates were based on the Midwest Vegetable

Production Guide for Spinach. Given the level of organic matter in the soil, this translated to about 22 kg/ha nitrogen. Fertilizers were broadcasted at the 4-leaf stage

Plant growth analysis. Plants were harvested on November 13th and December 8th. All of the leaf tissue from a one meter squared quad from each plot was removed and measured for fresh weight, dry weight, leaf area, and tissue nitrate. Roots with adhering soil were dug up, placed in plastic bags, and left in the cooler at 5 °C until analysis 48 hours later.

Rhizosphere Microbial Analysis. The general microbial population size and the size of the N-fixing population were estimated by using the most probable number method in

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tryptic soy broth (a general media) and NFb (a semi-solid N-free media), respectively.

Roots and closely adhering soil were shaken for 1 hour, serially diluted in PBS buffer, and inoculated into NFb and TSB in a 96-well format. The typical protocol for the most probable number method generally involves inoculating a serial dilution into 5 mL vials of semi-solid NFb (Baldani et al. 2014), which is very time and labor intensive, and takes up a lot of bench space. This protocol was scaled down to 0.5mL of semi-solid NFb in a

96 well-tube format to enable a high-throughput processing of many samples. The method was tested against the original, and similar results were obtained: the color change from green to blue indicative of positive growth was easily detected in the 0.5mL tubes. McCrady probability tables were used to estimate the size of the microbial population in the original sample. Data were analyzed in SAS using analysis of variance and LS means separation (version 9.3; SAS Institute, Cary, NC).

Results and Discussion

The soil was classified as Wooster Riddles silt loam, and standard soil test results are in Appendix D. As main effects, fertilization and inoculation both resulted in slight but insignificant yield increases for the first harvest. When the interactive effects of the two inoculants and two fertilizers are considered, yield differences between treatments are insignificant across the board (Figure C.1). A significant increase in fresh root mass was observed for the mixed inoculant in the plot where no fertilizer was added (Figure C.2b).

In the second harvest, there were no significant differences, or even numerical differences among treatments. There was no significant difference between treatments for leaf tissue nitrate-N, and values were relatively high for all treatments (between 1200-1400 ppm).

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Thus, it is very unlikely that nitrogen was a limiting nutrient in this system, so conditions were not ideal to address the question.

To better visualize differences in soil quality across individual plots, the control yield was plotted against the x-axis to provide integrative proxy for background soil quality.

This method revealed a clear negative relationship between the mass of the control and the percent increase due to inoculation for the fresh root weigh and fresh shoot weight of both inoculants tested (Figure C.3). These results are consistent with those of the broccoli experiment in (Figure B.2).

Microbial analysis revealed that as the main effect, N fertilization resulted in an insignificant decrease in the number of culturable N-fixing bacteria in the rhizosphere.

No significant effects of N-fertilization were observed on the CFU of the general microbial population. The mixed inoculant significantly increased root mass where no fertilizer was added, and interestingly, it significantly decreased the number of N-fixing bacteria in the rhizosphere (Figure C.1). Plant nutrient status is known to affect the composition of root exudates, and plant root exudates grown in different nutrient conditions can differentially alter bacterial gene expression (Carvalhais et al. 2013). Thus it is possible that a non-nitrogen fixing species in the mixed inoculant effectively dominated the rhizosphere, suppressing the N-fixing population.

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Figures

0.9 a a a a a 0.8 a a a 0.7 a 0.6 0.5 Control 0.4 Azos Blue 0.3 Environoc 401 0.2

Freshleafmass (kg/m^2) 0.1 0 No Fertilizer Chilean Bloodmeal Nitrate

0.03 a ab ab ab ab ab 0.025 ab b ab 0.02 Control 0.015 Azos Blue 0.01 Environoc 401 0.005 Fresh root mass Freshroot (kg/m^2) 0 No Fertilizer Chilean Bloodmeal Nitrate

Figure C.1 Fresh leaf and root mass for n-rich fertilizer by inoculant treatments.

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11 a ab ab ab ab ab 10 b ab ab

9 Control 8 Azos Blue

7 Environoc 401

6 Log N-fixing cells/g dry root dryroot cells/g N-fixing Log

5 No Fertilizer Chilean Nitrate Bloodmeal

11 a ab ab 10 ab ab ab ab ab b

9 Control 8 Azos Blue

7 Environoc 401

6 Log culturable cells/g dry root dryroot cells/g culturable Log

5 No Fertilizer Chilean Nitrate Bloodmeal

Figure C.2 Culturable population size of the general and N-fixing bacteria in the spinach rhizosphere

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200 Root mass response to Azos Blue 150 y = -13.105x + 119.01 100 R² = 0.52431 50 Response to Response to

inoculation (%) inoculation 0 0 2 4 6 8 10 12 -50 Mass of non-inocualted control (kg/m^2)

Figure C.3 Spinach yield and root mass response to inoculation vs. the mass of non- inoculated control the four products. Growth response was calculated as % increase relative to the non-inoculated control: (Inoculated-Control)*100/Control. Graphed values represent individual plots, not treatment averages.

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Appendix D: Soil Test Results from Preliminary Experiments

Table D.1 Soil test results from preliminary experiments. Tests were conducted by Logan Labs, LLC.

Sample Site Ashland Fremont OARDC

Total Exchange Capacity (M. E.) 8.52 9.53 10.36 pH of Soil Sample 7.7 7.7 7.2 Organic Matter, Percent 3.33 2.84 3.97 Anions Sulfur (ppm) 17 22 34 Phosphorus, Mehlich III (ppm) 41 47 139 Exchangeable Calcium (ppm) 1213 1445 1203 Cations Magnesium (ppm) 205 190 363 Potassium (ppm) 96 97 194 Sodium (ppm) 43 28 90 Trace Boron (ppm) 0.26 0.51 0.36 Elements Iron (ppm) 257 158 149 Manganese (ppm) 107 25 13.88 Copper (ppm) 6.13 4.28 89 Zinc (ppm) 2.5 4.83 6.76 Aluminum (ppm) 579 614 577

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Appendix E: Microbial Biostimulant Product Label Information

Table E.1 Label information of microbial biostimulant products under study Product Manufacturer Microorganisms CFU Carrier Application Azos Blue Reforestation Azospirillum brasilense Inert carrier Foliar spray or Technologies 1.00E drip: 16oz per acre, International +09 Seed treatment: mL-1 0.5% by weight BioYield 3Bar Pseudomonas Liquid In furrow: 2 L per Biologics, Inc. fluorescens media for 10-40 acres, Seed bacterial treatment: 6 oz per 3.00E growth 100lbs seed, Foliar +06 application: 1 mL-1 bottle per acre Biogenesis 1 Tainio Arthrobacter Milk Whey In furrow or foliar NP Technology globiformis, Proteins, spray: 1 lb per acre and Azospirillum lipoferum, Diatom- Technique, Azotobacter aceous Inc. chroococcum, Earth, Azotobacter vinelandii, Humates Bacillus from amyloliquefaciens, Leonardite Bacillus cereus, Bacillus megaterium, Bacillus subtilis, Micrococcus luteus, Pseudomonas fluorescens, 1.20E Pseudomonas putida, +06 Streptomyces griseus g-1 Bioplin Interglobe Azotobacter spp. Inert carrier Spread 250 mL Agro 1.00E product over 1 acre BioNatural +07 Product, LLC mL-1 Environoc Biodyne- 29 strains from the Ferment In furrow: 16 oz. 401 Midwest following genera: residue, per acre, foliar: 16- Azospirillum, Bacillus water 32 oz. per acre cereus, Bacillus during season, licheniformis, Bacillus Seedling tray: add subtilis, Cellulomonas, 1.00E 4 oz. product per Pseudomonas, +08 gallon of water and Streptomyces mL-1 soak Hydroguard Botanicare Bacillus Inert carrier Root dip: mix 2 ml amyoliquefaciens with 1 gallon of 1.00E water and dip roots +04 into the mixture at mL-1 transplanting

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