The Effect of Soil Cation Balancing on Soil Properties and Weed Communities in an Organic Rotation

THESIS

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

Katie Jo Linder, B.S.

Graduate Program in Horticulture and Crop Science

The Ohio State University

2015

Master’s Examination Committee:

Dr. Douglas Doohan, Advisor

Dr. Warren Dick

Dr. Laura Lindsey

Dr. Steven Culman

Copyrighted by

Katie Jo Linder

2015

Abstract

Organic farmers rely on many methods for weed control. While cultural practices are considered to be important, tillage, cultivation and hand weeding are paramount. Soil balancing is a cultural approach to weed management that is frequently discussed but poorly understood. Likewise organic-approved herbicides are of great interest to some farmers but of uncertain efficacy. Soil balancing is an approach to soil management based upon the “Basic Cation Saturation Ratio” (BCSR) hypothesis, which postulates that an ideal soil has a base saturation ratio of ~70% Ca, ~10% Mg, and ~5% K. The effects of soil balancing and the efficacy of approved natural-product herbicides on weeds have not been adequately investigated. A long-term field experiment was initiated to determine the effects of various soil balancing amendments, gypsum plus limestone, limestone only, and an amendment obtained from a local company (Green Field Farms Cooperative) on crop, weed, and soil parameters. Respecting organic herbicides, a greenhouse experiment was initiated to determine the efficacy of cinnamon oil, manuka oil, lemongrass oil, clove oil, citric acid, acetic acid, and a mixture of citric acid and garlic oil on three broadleaf weeds and two grasses. In the soil balancing experiment, balanced levels of Ca, Mg, and

K base saturation were not achieved following two years of amendment application, although changes in soil pH, Ca, Mg, P, and S were measured over the course of the experiment with the specific amendments applied. Soil amendment led to higher levels of

K, Ca, S, Mo, Cu, and Mn in corn and soybean foliage in 2014, and S in 2015, there were no treatment effects on grain yield or quality. Conclusions about the effect of soil balancing on weed communities could not be made because balance was not achieved; moreover, there were not clear treatment effects on weeds. However, a rotational effect was observed. Clover plots had the fewest weeds in 2015 because fewer weed seedlings emerged under the nearly complete cover of the clover that had been established in the spring of 2014. The 2015 corn, planted in 2014’s clover/oat plots, had the greatest number of weeds, likely resulting from a heavy infestation in clover/oats plots in 2014.

Regarding the natural-product herbicide experiment, essential oils generally performed better than acids. Of the species evaluated, hairy galinsoga was the most difficult to control. Manuka oil had the greatest efficacy across species and experiments. Cinnamon oil and lemongrass oil had similar efficacy to manuka oil on common purslane and common lambsquarters, but generally did not control weeds as well as manuka oil.

Although most weeds recovered by 2 WAT, dry weights were reduced in comparison to untreated plants.

iii

Dedicated to my parents, John and Cheryl Linder, the rest of my family, and my fiancé, Jesse Post.

Acknowledgments

Attending graduate school was one of the best experiences of my life. I learned so much from everyone and all of the experiences that I had during my time at The Ohio

State University. I never would have had this opportunity without my family, especially my parents, John and Cheryl Linder. It is them that I have to thank for encouraging me through the years and for showing me skills I would need through my life on the farm.

My uncle, Mike Linder, is also a big part of my life, instilling in me a passion for learning about agriculture. In addition, I thank my sister, Joanna Gall, her husband, Mike

Gall, and their children, Kaden and Ashtyn for supporting me through my adventures as a graduate student, and for the love and laughs that we have shared thus far. Last, but not least, I am thankful for the ongoing support of my fiancé, Jesse Post. His patience and understanding have been truly appreciated.

I would also like to thank my advisor, Dr. Douglas Doohan, and all of the members of our lab for their patience, advice, and assistance in carrying out this project. I want to extend a special acknowledgement to Dr. Doohan for his advice and support throughout the duration of my project. I could not have asked for a better advisor to lead me to success over the past two years. In addition, I would like to thank the members of my committee (Drs. Warren Dick, Laura Lindsey, and Steve Culman) for their guidance, patience, lessons, and assistance throughout the project. My advisor and all of the

v members of my committee are extremely great role models, and I hope to make them proud through the career that will follow my graduate work. Working with all of you has been invaluable, and for that I am truly thankful!

Vita

December 4, 1990 ...... Born, Edison, Ohio

May 2013 ...... B.S. Agronomy, Wilmington College

August 2013 to August 2015 ...... Graduate Research Associate, Department

of Horticulture and crop Science, The Ohio

State University

Publications

Linder, K., Mohseni-Moghadam, M., Felix, J., and Doohan, D. (2015). Tolerance of

Processing Tomato (Solanum lycopersicum L.) Varieties to Thifensulfuron-

Methyl. Weed Technology, In-Press. Accessed 12/2/2015.

Fields of Study

Major Field: Horticulture and Crop Science

vii

Table of Contents Abstract ...... ii

Dedication ...... iv

Acknowledgments...... v

Vita ...... vii

List of Tables ...... x

List of Figures ...... xiv

Chapter 1: The Theory and Practice of Soil Balancing ...... 1

1.1 Introduction ...... 1

Chapter 1 References ...... 8

Chapter 2: The Effect of Two Cycles of Soil Balancing Amendments on Soil, Crops and

Weeds ...... 11

2.1 Abstract ...... 11

2.2 Introduction ...... 12

2.3 Materials and Methods ...... 23

2.4 Results and Discussion ...... 35

2.4.1 The effect of soil balancing amendments, and crop on soil chemical and physical parameters ...... 35

2.4.2 The effect of soil balancing amendments on crops ...... 40

viii

2.4.3 The effect of soil balancing treatments and crop on weed communities ...... 50

2.5 Conclusion ...... 58

Chapter 2 References ...... 60

Chapter 3: The effect of natural product herbicides on hairy galinsoga, common lambsquarter, common purslane, large crabgrass, and Johnsongrass ...... 69

3.1 Abstract ...... 69

3.2 Introduction ...... 70

3.3 Materials and Methods ...... 74

3.4 Results and Discussion ...... 78

3.5 Conclusions ...... 88

Chapter 3 References ...... 89

Full Reference List ...... 91

Appendix A: Additional Soil Data ...... 104

Appendix B: Additional Crop Data ...... 112

Appendix C: Additional Weed Data ...... 121

Appendix D: Compost Analysis Data ...... 131

Appendix E: Additional Organic Herbicide Experiment Data ...... 133

List of Tables

Table 1. Crop, variety, planting date, seeding rate, seeding depth, and harvest date in

2014 and 2015...... 25

Table 2. Timing and depth of soil samples taken for various analysis...... 27

Table 3. Planned and actual amendment rates for 2014 and 2015...... 29

Table 4. Effect of sample depth and replication in 2013 on soil nutrient levels...... 35

Table 5. Effect of soil amendment in 2014 on soil nutrient levels ...... 37

Table 6. Effect of crop and soil amendment on soil bulk density (g cm3), the effect of soil amendments on water infiltration, stable aggregates, and β-glucosidase activity ...... 39

Table 7. Effect of treatment on nutrient analysis (HClO4 Digest) in corn leaf tissue and soybean leaf tissue ...... 41

Table 8. Effect of treatment on nutrient analysis (HClO4 Digest) in corn leaf tissue and soybean leaf tissue ...... 44

Table 9. Effect of treatment on crop emergence counts and analysis of corn and soybean grain...... 46

Table 10. Effect of treatment on crop emergence counts and analysis of corn and soybean grain ...... 48

Table 11. Effect of soil amendment and crop on 2014 counts of emerged weeds

(individuals meter2 ) ...... 49 x

Table 12. Effect of soil amendment and crop on 2015 counts of emerged weeds reported in individuals meter2 ...... 52

Table 13. Distribution of species across blocks as determined through the exhaustive germination performed on seedbank samples reported in individuals meter2 ...... 54

Table 14. Effect of soil amendment on 2014 exhaustive germination performed on seedbank samples reported in individuals meter2 ...... 55

Table 15. Effect of soil amendment and crop on the 2015 exhaustive germination performed on seedbank samples reported in individuals meter2 ...... 57

Table 16. The effect of natural product herbicides on hairy galinsoga, common lambsquarter, and common purslane ...... 78

Table 17. The effect of natural product herbicides on Johnsongrass and large crabgrass

...... 81

Table 18. The effect of natural product herbicides on hairy galinsoga, common lambsquarter, and common purslane ...... 83

Table 19. The effect of natural product herbicides on Johnsongrass and large crabgrass

...... 86

Table 20. The effect of sample depth and replication in 2013 on soil Ca, K, and Mg .. 105

Table 21. The effect of sample depth and replication in 2013 on soil nutrient levels ... 106

Table 22. The effect of sample depth and amendment on soil nutrient levels in 2014 soil samples ...... 107

Table 23. The effect of soil amendment in 2014 on soil nutrient levels ...... 108

Table 24. The effect of profile depth on soil texture of each block ...... 109

Table 25. The effect of amendment and crop on percent carbon in soil samples collected from each plot on 10/13 and 10/20 of 2014...... 110

Table 26. The effect of amendment on nutrient analysis of corn and soybean leaf tissue in

2014...... 113

Table 27. The effect of amendment on nutrient analysis of corn and soybean leaf tissue in

2015...... 115

Table 28. The effect of amendment on crop emergence counts and analysis of corn and soybean grain ...... 117

Table 29. The effect of amendment on crop emergence counts and analysis of corn and soybean grain ...... 118

Table 30. The effect of amendments on crop emergence counts in the oats plots ...... 119

Table 31. The effect of amendment on the biomass (in grams) of clover and weeds ... 120

Table 32. The effect of soil amendment and crop on density of emerged weeds

(individual meter2) in 2014 ...... 122

Table 33. The effect of soil amendment and crop on density of emerged weeds

(individual meter2) in 2015 ...... 124

Table 34. Distribution of species across blocks as determined through the exhaustive germination technique performed on seedbank samples in 2013 ...... 126

Table 35. The effect of soil amendment on weed communities as determined by the exhaustive germination technique performed on seedbank samples collected March 15,

2014...... 127

Table 36. Effect of soil amendment and crop on the weed community as determined by xii the exhaustive germination technique performed on seedbank samples collected on April

1, 2015...... 129

Table 37. Effect of crop on weed seed production ...... 130

Table 38. Analysis of compost applied to plots on 10/24/2014 ...... 132

Table 39. The effect of natural product herbicides on hairy galinsoga, common lambsquarters, and common purslane...... 134

Table 40. The effect of natural product herbicides on Johnsongrass and large crabgrass

...... 135

Table 41. The effect of natural product herbicides on hairy galinsoga, common lambsquarters, and common purslane...... 136

Table 42. The effect of natural product herbicides on Johnsongrass and large crabgrass

...... 137

xiii

List of Figures

Figure 1. Plot map of the long-term soil-balancing experiment ...... 30

Figure 2. Results of the loss-on-ignition test for percent soil organic matter plotted against percent organic carbon...... 111

xiv

Chapter 1: The Theory and Practice of Soil Balancing

1.1 Introduction

Weed control is one of the biggest challenges that farmers face. Infestations reduce leaf area index and compete with the crop for light, resulting in yield loss (Rajcan and Swanton, 2001). Uncontrolled, weeds interfere with harvest, and can reduce crop quality (Wilson et al., 2008). Weeds can prevent crop foliage from drying after a rain event, increasing the humidity beneath the canopy and creating conditions conducive for disease development. This effect can lead to further yield loss and increased mycotoxin levels in grain (Xu et al., 2007).

Organic farmers report that managing weeds is amongst the very most challenging production problems. Organic farming techniques are becoming more desirable to many consumers and farmers in the United States. Individuals are increasingly more conscious of environmental sensitivity to agricultural practices. During the 1990’s, the amount of farmland under certified organic management more than doubled (USDA ERS, 2000), and increased another four-fold between 2002 and 2005.

These growth rates confirm that organic farming is a sector of steadily increasing importance in U. S. agriculture (USDA ERS, 2010). However, not all sectors have adopted organic growing methods equally. For example, the percentage of total acres of

1 fruit and vegetable crops grown using organic methods is greater than the percentage of all organic grain crops (USDA ERS, 2010).The United States Department of Agriculture

Economic Research Service (2010) reported that only 0.2% of corn and soybeans, and

0.7% of wheat were grown organically in 2008.

To be certified organic, a farmer must meet standards set forth by the United

States Department of Agriculture. Certified organic croplands cannot use irradiation, sewage sludge, synthetic fertilizers, genetically modified organisms, nor most pesticides

(USDA AMS, 2015). Respecting weed control, organic farmers have turned to a variety of methods that do not require the use of herbicides. While some of these methods are based in science, others are based on alternative sources of knowledge. Alternative approaches to weed control may come from personal experience, or other informal sources of knowledge. Organic farmers are in constant search for alternative methods, as weed control is more challenging for them in comparison with conventional farmers.

Most organic farmers use a variety of weed management methods; crop rotation, intercropping, cultivation, and mowing are examples (Bond & Grundy, 2001; DeDecker et al., 2014; Liebman & Dyck, 1993; Melander et al., 2012; Turner et al., 2007).

DeDecker et al. (2014) surveyed several farmers in 2010 regarding weed management practices used in the Midwestern United States and found that the three most commonly used practices were crop rotation, primary tillage, and cover cropping.

Many farmers believe that soil conditions are a key factor in weed growth. Fifty five percent of a sample of organic farmers from Ohio and Indiana who were involved in a series of in-depth interviews believed that weeds are indicators of nutrient excess or

2 deficiency in the soil (Zwickle, 2011). A parallel to this idea is that by achieving ‘ideal’ levels of nutrients in the soil, ‘indicators of imbalance’ (i.e. weeds) will no longer persist.

Therefore, as the nutrient levels in the soil are restored to an appropriate balance, weed control will become easier. This idea is based on the basic cation saturation ratio (BCSR), more commonly known as soil balancing. The BCSR is an approach to soil fertility management that calls for a soil with ~70% Ca, ~10% Mg, and ~5% K on the soil’s cation exchange capacity (CEC) (Kopittke and Menzies, 2007). The BCSR is a controversial concept and is not considered to be well-grounded in a base of scientific data. Even so, many of the Midwest’s organic farmers and agronomists, as well as some conventional farmers are adhering to this method (William McKibben, personal communication, 2015).

ACRES U.S.A. is an organization that has been instrumental in encouraging the use of BCSR. Its founder, Charles Walters, was a proponent of the work of Dr. William

Albrecht, who studied the effect of different cation saturations on plant growth (Kopittke

& Menzies, 2007). Albrecht wrote The Albrecht Papers, that outlined the ideal ‘balanced’ soil with an exchange complex of 60-75% Ca, 10-20% Mg, 2-5% K, 10% H, 0.5-5% Na, with other cations occupying 5% on the soil’s CEC (Kopittke & Menzies, 2007). ACRES

U.S.A. ACRES USA supported this approach to farming and published some of Albrecht’s controversial work. They called their application of the BCSR approach “eco-agriculture”

(Anonymous, 2015). Many farmers, conventional and organic alike, subscribe to ACRES

U.S.A., which has given them access to information about the balancing approach, and have chosen to use soil balancing on their farms.

Achieving balance requires applications of ‘balancing’ products to the soil.

Gypsum and limestone are key components used to achieve balance. There are two types of limestone: dolomitic limestone (calcium magnesium carbonate) and calcitic limestone

(calcium carbonate). Dolomitic limestone is used when the addition of magnesium is deemed important, while calcitic is used when additional magnesium is not needed.

Organic and conventional farmers use both types of limestone to raise the pH of the soil.

Farmers hoping to achieve a balanced soil apply limestone to increase the calcium saturation and to raise the pH. Gypsum (calcium sulfate) is also used to increase the soil’s calcium saturation; however, it does not affect pH.

Although the BCSR theory is controversial, there are some scientific principles that support it. It is accepted that soil conditions play an important role in plant growth.

Nutrient levels, moisture, soil physics, chemistry, and soil biology can drastically affect growth. It is reasonable to speculate that various soil characteristics will affect species in different ways, because each species has unique growth requirements and optimal environmental conditions. Of the common calcium-bearing amendments used by farmers, gypsum application can result in certain chemical and physical changes to the soil that may be of particular relevance to weed management. Annual weeds, especially grasses are associated with a fine soil texture, and cloddy soils (Doohan, personal observation, 2015). Over time careful use of gypsum can improve soil structure providing an environment that may be less conducive to establishment of annual weeds (Chen and

Dick, 2011). As soil structure is improved rainfall infiltration increases, reducing the incidence of crop flooding and death (Chen and Dick, 2011). Gypsum also supplies S, an

4 essential nutrient that is becoming deficient in some Midwest soils (Chen and Dick,

2011). Gypsum reacts with Al3+ and lessens its toxic effects in the soil; thereby, providing a larger soil environment for crop growth (Shainberg et al., 1989; Smyth and

Cravo, 1992; as cited by Chen and Dick, 2011). Together these factors are likely to result in a more vigorous crop, better able to compete with weeds.

Changes in fertility can also affect the ability of the crop to compete with weeds.

For example, manipulation of the environment through the addition of nitrogen (N) fertilizer within a cropping sequence has an effect on competition between a crop and weed species (Liebman and Dyck, 1993). In comparison with a single application of N, split applications applied in a weedy corn system resulted in increased crop growth and reduced growth of common lambsquarters (Chenopodium album L.) and charlock mustard (Sinapis arvensis L.) (Alkaemper et al., 1979, as cited by Liebman and Dyck,

1993). This finding suggests that the timing of N availability to the crop affects the ability of the crop to compete with certain weeds (Liebman and Dyck, 1993). When extending this idea to the broader concept of total management it is reasonable to expect that weed and crop competition can be affected through the manipulation of a host of environmental characteristics.

Most weed management methods rely on disturbing the growth of weeds.

Disturbances can be of natural origin, such as a fire, or of human origin, like tillage

(Young and Evans, 1976). Physical methods, such as cultivation, mulches, tillage, and thermal control are frequently used by organic farmers (DeDecker et al., 2014). These disturbances are designed to disrupt weed growth and decrease the number of weed seeds

5 in the seedbank (Liebman and Dyck, 1993). Timing and frequency affect the success of these practices (Bond and Grundy, 2001). Tillage, for example, can provide immediate control of annual weeds, however, timing is important. If the disturbance is not timed properly, seeds can quickly germinate, grow, and reproduce once the disturbance is over.

Timing and type of tillage can be limited by the crop being grown (Bond and Grundy,

2001). For example, rotary hoes are more feasible for inter-row weeding in corn than in solid-stand crops like alfalfa (Bond and Grundy, 2001).

Farmers must manage disturbances at the right intensity and timing to prevent competition and seed production. Liebman and Dyck (1993) stated that in addition to timing and frequency of tillage, planting date, and different fertilizer regimes can also affect weed management through improving crop competition with weeds. However, weeds are well adapted to many man-made disturbances, thus tillage, cultivation, hand- weeding and even the use of cultural practices like crop rotation and cover cropping must be practiced with finesse in order to prevent both competition and return of weed seeds to the soil (Young & Evans, 1976). Therefore, careful thought and consideration need to be taken in regards to the timing and intensity of weed control practices.

Farmers who attempt to use soil balancing, do so alongside weed control methods that disturb weed growth and attempt to harness ecological processes, such as those associated with increased species diversity and elevated soil health. Although the BCSR concept is still considered controversial, a large number of organic farmers are spending money to incorporate soil balancing with the belief that this method can lead to improved weed control. However, there is a lack of scientific evidence to support this idea. The

6 money that is being spent on balancing the soil should be justified scientifically, as soil balancing amendments are more costly than amendments applied using the sufficiency level of available nutrients (SLAN) concept (which refers to fertilizing based on crop requirements). Therefore, it is important that the claims being made about the BCSR are examined through carefully designed and executed long-term experimentation to determine whether claims about soil balancing effects on weed control are justified.

Chapter 1 References

Alkaemper, J. E. Pessios, & Long, D. V. (1979). Einfluss der duengung auf die

entwicklung und naehrstoffaufnahme verschiedener unkrauter in mais.

Proceedings of the European Weed Research Society, 181-192.

Anonymous. (n.d.). Our history. Retrieved from http://www.acresusa.com/history/.

Accessed October 13, 2015.

Bond, W., & Grundy, A. C. (2001). Non-chemical weed management in organic farming

systems. Weed Research, 41(5), 383-405.

Chen, L., & Dick, W. A. (2011). Gypsum as an agricultural amendment: General use

guidelines. Ohio State University Extension.

DeDecker, J. J., Masiunas, J. B., Davis, A. S., & Flint, C. G. (2014). Weed management

practice selection among Midwest U.S. organic growers. Weed Science, 62(3),

520-531.

Kopittke, P. M., & Menzies, N. W. (2007). A review of the use of the basic cation

saturation ratio and the “ideal” soil. Soil Science Society of America Journal,

71(2), 259-265.

Liebman, M., & Dyck, E. (1993). Crop rotation and intercropping strategies for weed

management. Ecological Applications, 3(1), 92-122.

Melander, B., Holst, N., Rasmussen, I. A., Hansen, P. K. (2012). Direct control of

perennial weeds between crops – Implications for organic farmers. Crop

Protection, 40, 36-42.

Rajcan, I., and Swanton, C. J. (2001). Understanding maize-weed competition: resource

competition, light quality and the whole plant. Field Crops Research, 71, 139-

150.

Shainberg, I., Sumner, M. E., Miller, W. P., Farina, M. P. W., Pavan, M. A., and Few, M.

V. (1989). Use of gypsum on soils. A review. Advances in Soil Science, 9, 1-111.

Smyth, T. J., and Cravo, S. (1992). Aluminum and calcium constraints on continuous

crop production in a Brazilian Amazon soil. Agronomy Journal, 84, 843-850.

Turner, R. J., Davies, G., Moore, H., Grundy, A. C., & Mead, A. (2007). Organic weed

management: A review of the current UK farmer perspective. Crop Protection,

26, 377-382.

United States Department of Agriculture Agricultural Marketing Service. (2015).

National organic program. Retrieved from

http://www.ams.usda.gov/AMSv1.0/NOPOrganicStandards. Accessed October

23, 2015.

United States Department of Agriculture Economic Research Service (USDA ERS).

(2000). U.S. organic agriculture gaining ground. United States Department of

Agriculture. Retrieved from

http://webarchives.cdlib.org/sw1tx36512/http://www.ers.usda.gov/publications/ag

outlook/apr2000/ao270d.pdf. Accessed October 23, 2015.

United States Department of Agriculture Economic Research Service (USDA ERS).

(2010). Organic production [Data file]. Retrieved from

http://webarchives.cdlib.org/sw15d8pg7m/http://ers.usda.gov/Data/organic/.

Accessed October 23, 2015.

Wilson, R. S., Tucker, M. A., Hooker, N. H., LeJeune, J. T., & Doohan, D. (2008).

Perceptions and beliefs about weed management: Perspectives of Ohio grain and

produce farmers. Weed Technology, 22, 339-350.

Xu, X. –M., Monger, W., Ritieni, A., & Nicholson, P. (2007). Effect of temperature and

duration of wetness during initial infection periods on disease development,

fungal biomass, and mycotoxin concentration on wheat inoculated with single, or

combinations of, Fusarium species. Plant Pathology, 56, 943-956.

Young, J. A. & Evans, R. A. (1976). Responses of weed populations to human

manipulations of the natural environment. Weed Science, 24(2), 186-190.

Zwickle, S. L. (2011). Weeds and organic weed management: Investigating farmer

decisions with a mental models approach. M.S. Thesis, The Ohio State

University, Columbus, OH.

Chapter 2: The Effect of Two Cycles of Soil Balancing Amendments on Soil, Crops and Weeds

2.1 Abstract

Organic farms rely on a diversity of cultural and physical methods for weed control. Of those, tillage and hand weeding are commonly used in Ohio. However, farmers seek alternative approaches that are less time-consuming, expensive, and of higher-efficacy. Soil balancing is an approach to soil management that 50% or more of

Ohio farmers believe may help relieve the burden of weed control. The articulated hypothesis behind soil balancing is known as the “Basic Cation Saturation Ratio”, which is based on the idea that an ‘ideal soil’ has a base saturation ratio of ~70% Ca, ~10% Mg, and ~5% K. Effects of soil balancing on weeds has not been adequately investigated and is presently not supported by peer-reviewed literature. A field experiment was initiated at

The Ohio State University’s Ohio Agricultural Research and Development Center

(OARDC) in Wooster, Ohio, to determine the effects of gypsum plus limestone, limestone only, and a proprietary blend provided by a local company with experience in soil balancing on crop, weed, and soil parameters. Balanced levels of Ca, Mg, and K base saturation were not achieved following two annual applications of three amendment treatments intended to achieve balance. In comparison with analysis of baseline soil samples, soil pH, S, Ca, Mg, and P increased in 2014 in response to added amendments.

Potassium base saturation did not change between November 2013 and October

2014. Though increases in concentration of K, Ca, S, Mo, Cu, and Mn in 2014, and S were noted in the foliage of corn and soybean in amended plots in 2015, there were no effects on grain yield or quality. The 2015 clover plots had the fewest weeds likely because fewer seedlings emerged under the nearly complete cover of clover that had been established 12 months earlier. The 2015 corn, planted in 2014’s clover/oat plots, had the greatest number of weeds, just as the clover/oats plots did in 2014. There were more weeds in the field in 2014 than in 2015. The weed community was dominated by summer annual, large-seeded, broadleaf weeds; however, because balance was not achieved conclusions could be made in regards to the effect of amendments on weeds.

2.2 Introduction

Soil balancing is a term that refers to the application of the basic cation saturation ratio (BCSR). The BCSR hypothesis states that base saturation of a soil’s cation exchange capacity (CEC) should be ~70% Ca, ~10% Mg, and ~5% (Kopittke and

Menzies, 2007). Base saturation refers to the percent of exchangeable cations (including

Ca, Mg, K and Na) on the CEC (in meq/100 g soil) and is calculated using the following equation (Havlin et al., 2014):

푡표푡푎푙 푏푎푠푒푠 퐵푎푠푒 푆푎푡푢푟푎푡푖표푛 (%) = ( ) 푥 100 퐶퐸퐶

Kopittke & Menzies (2007) conducted a review of the research supporting the

BCSR. They attribute the origin of the BCSR idea to Loew (1892) who observed that 12 limestone (a Ca containing product) and Mg were toxic to plants when levels of Ca and

Mg were not similar to one another. Loew (1892) then searched for an “ideal” Ca:Mg ratio for plants. In a later experiment reported by Loew and May (1901), data showed that optimum plant growth occurred under a range of Ca:Mg ratios (Kopittke & Menzies,

2007). Lipman (1916a & b) later reviewed the literature regarding the ideal Ca:Mg ratio and concluded there was no reason to believe in the existence of an ideal ratio. Moser

(1933) concluded that Ca:Mg ratios have no effect on crop yield, but, that yield is dependent on the amount of soluble Ca.

These early investigators laid the scene for research focusing on ideal levels of Ca and Mg in soils. While studying the growth of alfalfa, Bear et al. (1945) concluded that the ideal soil had 65% Ca, 10% Mg, 5% K, and 20% H on the soil’s CEC. According to

Bear & Toth (1948), the ratios for these suggested saturations would be a Ca:Mg ratio of

6.5:1, a Ca:K ratio of 13:1, a Mg/K ratio of 2:1, and a Ca/H ratio of 3.25:1. William

Albrecht then began his investigations and led a continuation of the BCSR research, which would prove to be highly influential in the current day (Kopittke & Menzies,

2007). Albrecht confirmed that a high level of Ca saturation was important (Kopittke &

Menzies, 2007). Albrecht (1975) later wrote a series of books (The Albrecht Papers), in which he claimed that the ideal base saturation of agricultural soils was 60-75% Ca, 10-

20% Mg, 2-5% K, 10% H, 0.5-5% Na, with other cations occupying 5%. Kopittke and

Menzies (2007) claim, that Albrecht’s experimental methodologies were flawed

(Kopittke and Menzies, 2007). Kopittke and Menzies (2007) stated that Albrecht’s reports of crop quality improvements almost always accompanied a yield increase and

13 that controls experienced a reduction in quality because of “growth-limiting factors”. For example, yield of lespedeza increased with added phosphate and limestone; however, control plots did not receive as much added P or Ca as the treated plots (Albrecht and

Smith, 1941). Albrecht also found that soil amendments could improve crop quality, yet neglected to monitor soil properties and determine the optimum rate of soil amendments to be applied (Kopittke & Menzies, 2007). Koittke & Menzies argue that due to his flawed experimental approach, Albrecht should not have made the conclusion that crop yield and quality improvements were due to balancing alone (Kopittke & Menzies, 2007).

Graham (1959) (as cited by Kopittke & Menzies, 2007) later redefined the ideal soil as having a range of these cations from 65-85% Ca, 6-12% Mg, and 2-5% K for a balanced soil.

A friend of William Albrecht, Charles Walters, created a publication called

ACRES U.S.A. in 1970, in which he projected his support for the BCSR method to soil fertility (Anonymous, 2015). By publishing some of Albrecht’s papers, Walters was able to bring soil balancing to a larger audience. Due to Walters’ support for Albrecht’s work, he was able to give his magazine a new direction for organic farming, which he called

“eco-agriculture” (Anonymous, 2015). Walters also wrote a book called “Weed Control without Poisons”, in which he tells of his support for the idea that weeds are indicators of soil balance. It is through these avenues that many readers became familiar with BCSR, and BCSR became more “main stream” than ever before.

Most major and minor nutrients essential to crop growth are not directly considered within the BCSR theory (Eckert, 1987). In contrast, most research on plant

14 nutrition through soil management has focused primarily on the ‘sufficiency level of available nutrients’ (SLAN) concept, which guides soil fertility management according to crop requirements for all macro and micro nutrients (Kopittke and Menzies, 2007; Eckert,

1987). SLAN is the dominant method for interpreting soil tests today; however, BCSR was once as commonly used (Eckert, 1987). In 1977, McLean (1977) reported that the

BCSR interpretation was more commonly used by private soil testing laboratories in the

North Central region, while university laboratories preferred SLAN. Over time, SLAN became more accepted and used by private and university laboratories alike. Yet, some

Ohio farmers and agronomists adhere to the soil balancing method due in part to evidence that gypsum, a major component of most BCSR amendments, increases water infiltration, helps prevent soil erosion, and improves seedling emergence (Chen and Dick, 2011).

Claims by contemporary supporters of the BCSR claim that achieving balance or moving toward a balanced soil will lead to reduced weed problems, as well as improvements in crop yield and quality. Charles Walters in particular was an individual who promoted that belief (Walters, 1996).

Results of a recent survey conducted with organic farmers in Ohio and Indiana indicated that 55% believed that weeds were indicators of soil nutrient ‘imbalances’ and soil health (Zwickle, 2011). For example, one farmer stated that by increasing calcium, dandelion populations decreased (Zwickle, 2011). Indeed, research supports the view that weeds respond to local environmental conditions, particularly rainfall and temperature

(Harper, 1977). Using a similar approach with organic farmers in New England as that of

Zwickle, Jabbour et al. (2013) found that approximately 33% of the farmers in their

15 sample believed that weeds could be used to gauge soil conditions. For example, one farmer claimed that common lambsquarters (Chenopodium album L.) was an indicator of high soil fertility (Jabbour et al., 2013). However, while numerous organic farmers in both regions of the U.S. think that there is a connection between weed populations and soil management, very few scientific experiments have been conducted to examine this claim. The notion of weeds as indicators of soil conditions is grounded in a small body of empirical data. Weeds are mostly ruderal plants adapted to respond to disturbances and to a variety of environmental conditions including soil pH, nutrient status, and moisture levels. Harper, in this tome Population Biology of Plants (1977), concluded that each weed species has its own set of “ideal” local environmental conditions for optimal growth.

Limestone and gypsum are primary ingredients included in most soil balancing blends. Limestone is used primarily to raise the pH of soil but supplies calcium that is its major constituent (Kopittke and Menzies, 2007). Following the traditional (i.e. SLAN) approach to soil management, exchangeable soil Ca levels in excess of 200 parts per million (ppm) are not recommended as a yield response will not occur (Vitosh et al.,

1995). However, this concentration may or may not be associated with soil balance depending on the total CEC of the soil in question. Calcitic limestone (calcium carbonate) and dolomite (calcium magnesium carbonate) are commonly used forms of limestone.

Calcitic limestone is typically used when the addition of magnesium is not needed, or may be detrimental. Magnesium is a useful addition to the soil when levels are too low to sustain crop growth (less than 50 ppm) (Barker et al., 2005).

Zhang and Norton (2002) studied two cultivated soils (a Fayette silt clay loam and a Miami silt loam) and determined that Mg resulted in greater clay swelling than Ca.

They explained that the increase in clay swelling is caused by clay surface hydration, cation hydration, and osmotic pressure, and therefore when Mg is hydrated, it is “held with a lower electrostatic force at clay surfaces” (Tirado-Corbalá, 2010). In turn, clay swelling caused smaller soil pores and weakened soil aggregates (Zhang & Norton,

2002). Thus the Mg in dolomitic lime affects physical soil properties, such as an increase in the dispersion of clay, decreasing soil aggregate stability (Zhang & Norton, 2002).

Curtin et al. (1994) also suggested that Mg can have a deleterious effect on soil structure stability. However, the calcium in limestone can minimize the ability of Mg to disperse clay, thus, improving aggregation and structural stability (Wallace and Terry, 1998).

Generally the most important effect of limestone is an increase in soil pH, which can also result in a reduction in the amount of exchangeable aluminum, molybdenum and magnesium (Tisdale et al., 1985; Caires et al., 2005; Barker et al., 2005). The availability of these cations to plants decreases as soil pH rises. Manganese and aluminum can be toxic to crop roots when levels are too high. Limestone reduces this effect by raising the pH so that Mn and Al become less soluble and exchangeable as soil pH rises (Wallace and Terry, 1998; Chen and Dick, 2011). Limestone applications reduce levels of toxic cations allowing for more widespread root growth (Tisdale et al., 1985). Although aluminum toxicity is not typically a problem in Ohio’s soils, limestone is still a beneficial amendment for farmers. For wheat, corn (Zea mays) and soybeans (Glycine max), a pH of

6.5 to 6.8 or higher is needed to sustain healthy crop growth (Vitosh et al., 1995). Soil pH

17 needs to be within this range for healthy crop growth because most nutrients are available to the crop in this range (Jensen, 2010).

Gypsum is an alternative to limestone that supplies Ca and S, while not affecting soil pH. Gypsum offers a number of benefits not available through limestone. First, gypsum increases the amount of available Ca in the soil, meaning that gypsum would be a better soil amendment than limestone in cases where pH is already within the optimum range (Caires et al., 2011; Chen and Dick, 2011). Secondly, gypsum can increase the uptake of water and nutrients by allowing roots to penetrate to deeper levels of the soil profile (Caires et al., 2011; Chen and Dick, 2011; Ritchey et al., 1980). This is because gypsum is more soluble than limestone and is able to percolate deeper within the profile more quickly than lime (Chen and Dick, 2011; Tirado-Corbalá, 2010). Thus, with a deeper rooting system, the crop can access soil water previously out of reach. Deep rooting during times of drought can be crucial for plant survival (Ritchey et al., 1995).

Thirdly, gypsum can improve soil aggregation, prevent soil crusting and erosion, and improve the movement of water through the profile (Amezketa et al., 2005; Norton,

2008; Chen and Dick, 2011; Shainberg et al., 1989, as cited by Buckley and Wolkowski,

2014). Gypsum can also replace Mg and Na with Ca on exchange sites causing flocculation of soil particles, which improves water infiltration through the soil profile

(Chen and Dick, 2011). Additionally, clay dispersal is reduced by gypsum because gypsum changes the concentration and composition of electrolytes (Sumner, 1993,

Baldock et al., 1994, as cited by Bronick and Lal, 2005).

Improvements in soil physical properties may enable the farmer to plant the crop earlier in the year, providing a competitive advantage over weeds. Water infiltration is an important factor for crop health because lower water infiltration rates can lead to ponding during heavy rain events. These anoxic conditions could make the crop more vulnerable to stress. Similar to limestone, gypsum can also reduce the amount of exchangeable Al in the soil minimizing the effects of Al toxicity on the crop, leading to improved crop growth (Chen and Dick, 2011). Improved crop growth will improve the crops competitive ability with weeds, which may in time lead to a measurable decrease in the number of weeds in a field.

Soil biology is very important to organic farmers because they believe that through taking care of soil biota, micro-organisms can release, transform and transfer nutrients (“Organic FAQs”, 2015). With the numerous physical and chemical effects caused by the addition of limestone or gypsum, biological effects are also to be expected.

Considering that many changes in the soil may occur following use of the common

BCSR amendments, it stands to reason that weed populations may too be affected by these physical, chemical, and biological effects.

A visit to a farm trade show will indicate that many companies are now recommending their own commercially-available blends of amendments to balance soil

(Doohan, Personal Observation). Some of these firms clearly subscribe to the BCSR theory, and base their recommendations on its prescribed Ca, Mg, and K base saturations.

Some make claims about which soil ‘imbalance’ is related to occurrence of a particular weed, and how to go about addressing that species through soil amendment.

Recommendations are based on experience, and not grounded in scientific research. With the large amounts of money being spent to balance the soil, it is important that more research be conducted regarding the effectiveness of soil balancing on weeds.

Amongst those who have investigated the cost effectiveness of the BCSR, there is a general consensus that it is more costly than following the SLAN (Eckert, 1987). Exner et al. (2007) working in Iowa compared the costs of using the SLAN method versus the balancing approach. They took into account the costs of amendments, the amount of amendments used, the frequency of applications, and crop yields in the short-to-medium term (Exner et al., 2007). Fertilizer and lime costs were on average $9.27 higher per acre when the soil balancing approach was used in comparison with the SLAN approach

(Exner et al., 2007). Schonbeck (2001) also claimed that the balancing approach was more costly, estimating costs to range from $40 to $300 per acre including shipping and application costs. These figures were based on the application of 1-2 tons/acre of high-Ca lime or gypsum, quantities required to change the base saturation ratio from 5-10% percent (Schonbeck, 2001). We interviewed a representative of Greenfield Farms

Cooperative, a local company in Wooster, Ohio, that supplies consulting services and products to farmers interested in balancing the chemistry and increasing the biology of their soils. They indicated that their typical balancing blend would cost approximately

$150-200 per acre for a pasture, $350-500 per acre for a new vegetable producer, and needed to be applied for two or more years, depending on the background soil quality and the crops to be grown (Raymond Yoder Jr., personal communication, 2015).

While a small body of literature supports that quality and yield of some crops can be increased with high Ca treatments, and pest populations reduced, research has not focused directly on the claim that BCSR has any effect on weeds (Schonbeck, 2001;

Cullen and Mittenthal, 2011). Schonbeck (2001) found that balancing improved soil tilth on soils that were loamy or clay, and had very high levels of K. Depending on the year and site, yields were also affected by soil balancing. Broccoli yield across all 4 sites was approximately 11% higher in the Ca treated plots. At one of the sites, broccoli, tomato, and butternut squash all experienced higher average marketable yields in the high Ca treatment. At that site, the low Ca treatment consisted of 1,379 kg ha-1 of dolomitic limestone, while the high Ca treatment had 1,379 kg ha-1 of calcitic limestone and 1,681 kg ha-1 of gypsum (Schonbeck, 2001). No effects were demonstrated on crop nutrient uptake, crop resistance to pests and diseases, weeds, or longer shelf lives for vegetables.

Exner et al. (2007) also tested the BCSR in terms of crop, soil, and weed response. No clear-cut treatment effects regarding grain quality, weeds, or soil organic matter were observed (Exner et al., 2007). Effects that were noted regarding these parameters were seen only on individual sites (Exner et al., 2007).

Cullen and Mittenthal (2011) conducted field and greenhouse experiments to determine if soil balancing affects insect feeding or population growth. On one sampling date in 2008 aphid populations were lowest in BCSR organic plots in comparison with plots that used standard organic fertilization or conventional fertilization (Cullen and

Mittenthal, 2011). Conclusions could not be made as to why this occurred, as balance may not have played a role in 2008 field trials, since it had not yet been achieved. In

21 contrast to field results, BCSR did not have any effect on insects in the greenhouse

(Cullen and Mittenthal, 2011). Cullen and Mittenthal (2011) suggested that the use of organic fertility management (whether it be BCSR or otherwise) may cause lower pest populations in comparison with a conventional fertility system without pesticides.

Thus, the current work is vastly important to farmers in the region and in time will help farmers, agronomists, and researchers understand the true effects of soil balancing on soil, weeds, crops, and pests. Here I report initial results from a long-term experiment intended to deeply probe the alleged impact of soil balancing on weeds and weed communities within a crop rotation. The hypothesis for this experiment is that soil balancing improves crop growth and health so that crops are better competitors with weeds, thus reducing weed impacts. The objectives were to (1) measure the impact of soil amendments on base saturation of exchange sites, levels of soil nutrients, pH, cation exchange capacity (CEC), organic matter, soil texture, bulk density, stable aggregates, and soil enzymes; and (2) to determine how crop productivity, and the recruitment, growth, and fecundity of weeds are affected by the rotation and soil amendment.

2.3 Materials and Methods

An experiment was initiated in 2014 to determine effects of the BCSR method of management, on soils, weeds and crops. The experiment was set up in a transitional organic field at The Ohio Agricultural Research and Development Center (OARDC) in

Wooster, Ohio (40.77 °N, -81.93 °W, and elevation 358 m). This field was planted with

Roundup Ready soybean in 2012 and 2013. Weeds were treated with glyphosate herbicide. Fertilizer was not applied either year. The soil type was a Canfield silt loam

(fine-loamy, mixed, active, mesic aquic fragiudalfs) and the field had an ENE aspect with a slope that varied from 0-2% to 2-6% (USDA NRCS, 2015). The experimental design was a randomized complete block with 64 plots and 4 replications. Plots were 6.1 meters x 12.2 meters. Treatments were based on background soil chemistry and were designed to achieve balance over time. The factorial treatment design of crop rotation and soil amendment included dolomitic limestone, dolomitic limestone plus gypsum, a custom- blended proprietary amendment supplied by a local farm supply (Green Field Farm

Cooperative (GFF)), plus a control that did not receive any soil balancing amendments

(Table 3).

The rotation planned for this experiment was corn, soybeans, and wheat

(underseeded with clover) with each crop represented each year. Oats underseeded to clover were planted instead of wheat in 2014 because they could be seeded in the spring and develop a grain crop. Clover remained in the 2014 oat plots for the 2015 growing season. In 2015, corn followed clover/oats, soybean followed corn, and winter wheat followed soybean. Crops, varieties, planting dates, depths, rates and harvest dates for

2014 and 2015 are summarized in Table 1. Soybean and corn yield was determined from the middle two rows of each plot in 2014. In 2015, corn yield was determined from the entire plot and in soybean was determined from the middle two rows. Corn and soybeans were both planted on May 15, 2015. Emerged corn and soybean seeds were terminated due to poor stands caused by insect damage and cold weather and replanted on June 5,

2015.

Soil was sampled with a 2 cm diameter soil probe to a depth of 45 cm in

November 19, 2013 by block and in October 13 and 20, 2014 by plot. Soil cores were separated into the 0-15, 15-30, and 30-45 cm sections, each of which was labeled separately. Analyses were performed at the OARDC Service Testing and Research

(STAR) Lab1, or at Brookside Laboratories (for plots to be treated with the proprietary soil balancing amendment). Analyses included pH (measured on a 1:1 soil:water ratio), base saturation and nutrient levels. Specific tests included the Bray test for phosphorous, ammonium acetate extraction for potassium, calcium and magnesium, a test for CEC

(using the following equation for concentrations of Mg, Ca, and K with a correction for

푢푔 ( ⁄ 표푓 푎 푐푎푡𝑖표푛) × 100 soil pH) 퐶퐸퐶 = 푔 + 1.2 (70 − 퐿푇퐼), ((푎푡표푚𝑖푐 푤푒𝑖푔ℎ푡 표푓 푡ℎ푒 푐푎푡𝑖표푛 𝑖푛 푚푔)÷푣푎푙푒푛푐푒 표푓 푡ℎ푒 푐푎푡𝑖표푛) loss-on-ignition for organic matter, and the Mehlich 3 extraction for P, K, Ca, Mg, S, Al,

B, Cu, Fe, Mn, Mo, Na, and Zn. Results of the 2013 sample analyses were used to prescribe rates for amendments applied first in the spring of 2014. Organic matter measured by loss-on-ignition was conducted on the 2015 samples for all 3 depths and plotted against organic carbon (Combs and Nathan, 1998; Schulte and Hopkins, 1996).

1 OARDC STAR Lab, http://oardc.osu.edu/starlab/t08_pageview3/Home.htm 24

Table 1. Crop, variety, planting date, seeding rate, seeding depth, and harvest date in 2014 and 2015.

2014 2015 Seeding Planting Seeding Seeding Harvest Planting Seeding Harvest Crop Variety Variety Depth Date Rate Depth (cm) Date Date3 Rate Date (cm) Merit 5/21 86,450 Merit O- 74,100 Corn 4.0 10/27 6/5 4.0 10/7 5454 & 5/23 seeds ha-1 4234 seeds ha-1 Albert Blue 469,300 401,375 Soybeans Lea O- 5/22 4.0 10/2 River 6/5 4.0 9/24 seeds ha-1 seeds ha-1 2265 2A12 100.9 kg Oats GR-65 5/20 2.5 N/A N/A N/A N/A N/A N/A ha-1

25 Mediu -1

Clover 5/21 18 kg ha Broadcast N/A N/A N/A N/A N/A N/A m Red Wheat N/A1 10/28 N/A N/A N/A2 N/A N/A N/A N/A N/A 1 Oats were planted instead of wheat in 2014. 2 Wheat was terminated in 2015 to kill weed seedlings that were emerging and disked periodically afterward to stimulate germination of additional weeds and kill them. 3 Planting date for 2015 corn and soybean refers to the replant date.

Soil was sampled with a 2 cm diameter soil probe to a depth of 45 cm in

(STAR) Lab2, or at Brookside Laboratories (for plots to be treated with the proprietary soil balancing amendment). Analyses included pH (measured on a 1:1 soil:water ratio), base saturation and nutrient levels. Specific tests included the Bray test for phosphorous, ammonium acetate extraction for potassium, calcium and magnesium, a test for CEC

(using the following equation for concentrations of Mg, Ca, and K with a correction for

2 OARDC STAR Lab, http://oardc.osu.edu/starlab/t08_pageview3/Home.htm 26

Table 2. Timing and depth of soil samples taken for various analyses.

Analysis Performed Time of sampling Depth

Nutrient analysis Spring & Fall 2014 0-15, 15-30, 30-45 cm

Bulk density Fall 2014 0-15 cm

Soil Enzymes Fall 2014 0-15 cm

Organic matter Fall 2014 0-15 cm

Soil texture Fall 2014 0-15 cm

Stable aggregates Fall 2014 0-15 cm

For the spring of 2014, GFF prepared a blend of 560 kg ha-1 of soft rock phosphate (0-3-0) (17 kg ha-1 of P), 224 kg ha-1 of sulfate of potash (0-0-50) (112 kg ha-1 of K), 448 kg ha-1 of aragonite (33% calcium) (147 kg ha-1 of Ca and less than 13 kg ha-1 of Mg), 448 kg ha-1 of Flora-stim (microorganisms and trace elements), 2,242 kg ha-1 of

Hi-Cal Lime (calcitic limestone), and 1,121 kg ha-1 of compost (3-3-3 or 3-4-3) (34 kg ha-1 of of N, 34 or 45 ha-1 of P, and 34 kg ha-1 of K). This amendment was formulated based upon samples submitted to Brookside Laboratories for analysis and based on

GFF’s experience with soil balancing. Rates of dolomitic limestone, and gypsum plus dolomitic limestone were prescribed by Warren Dick, Ohio State University soil scientist and project team-member and were based upon the analyses performed at the STAR lab as described. Gypsum and dolomitic limestone were obtained from Tyler’s Grain and

Fertilizer Company (Smithville, Ohio). Both materials were recommended at 2,242 kg ha-

1. However, the actual gypsum rate applied in 2014 was 2,578 kg ha-1, and the actual

27 applied rate of limestone was 2,152 kg ha-1. These discrepancies were caused by operator error. Initial plans were for calcitic limestone; however, dolomitic lime was applied instead. All treatments were applied on April 24, 2014.

Additional soil samples collected on a per plot basis after harvest in autumn 2014 were used to prescribe treatments for 2015. Based upon their samples and analyses GFF recommended an application of 560 kg ha-1of soft rock phosphate (0-3-0), 448 kg ha-1 of gypsum (calcium sulfate), 224 kg ha-1 of aragonite (which supplied 74 kg ha-1 of Ca and less than 7 kg ha-1 of Mg), 448 kg ha-1 of Flora-stim, 56 kg ha-1 of kelp (seaweed, a source of trace elements, enzymes, and others), 56 kg ha-1 of molasses (carbohydrates and proteins for bacteria), 22 kg ha-1 of boron, 11.2 kg ha-1 of zinc, 5.6 kg ha-1 of copper, and

1,121 kg ha-1 of compost for a total application of 2,952 kg ha-1 of material. However, the actual rate of application of the GFF treatment was 2,289 kg ha-1. As with limestone and gypsum applications, these discrepancies were caused by operator error.

In the fall of 2014 after grain harvest, gypsum plus limestone plots received 5,187 kg ha-1, more than twice the rate of gypsum recommended. Discrepancies in rate recommended versus actual applied rate occurred because of operator error. Limestone was not applied in the fall of 2014. Compost (14-7-8) (from dairy manure) was applied to the 2015 wheat plots at a rate of 11,432 kg ha-1 on October 23, 2014. Plots intended for

2015 corn received 14,571 kg ha-1 of compost on December 1, 2014, and 2015 clover plots received 11,209 kg ha-1 on December 5, 2014. The 2015 soybeans plots received gypsum at a rate of 5,187 kg ha-1 on December 10, 2014. The GFF treatment was applied to the 2015 wheat plots at a rate of 2,288 kg ha-1 on the same day gypsum was applied.

The remaining plots designated to receive the GFF amendment received amendments at a

28 rate of 2,289 kg ha-1, this included compost (14-9-6). The treatments were disked into on the 2015 corn and soybean plots and surface applied elsewhere. All recommended rates are shown in Table 3. Figure 1 is a plot map of the field.

Table 3. Planned and actual amendment rates for 2014 and 2015 in kg ha-1

2014 (applied 4/24/2014) 2015 (applied 10/24/2014)

Planned Actual Planned Actual (kg/ha) (kg/ha)2 (kg/ha) (kg/ha) Limestone 2,242 2,158 0 0 Gypsum + 2,242 2,578 2,242 5,187 Limestone GFF 2,289 2,289 2,952 2,289 Amendments1 1 Proprietary blend supplied by Green Field Farms Cooperative. 2014 Application consisted of (kg ha-1): 560 soft rock phosphate (0-3-0), 224 sulfate of potash (0-0-50), 448 aragonite, 448 Flora-stim, 2,242 hi-cal lime, 1,121 compost. 2015 Application consisted of (kg ha-1): 448 gypsum, 224 aragonite, 448 Flora-stim, 56 kelp, 56 molasses, 22 boron, 11 zinc, 6 copper. 2 Discrepancies caused by operator error.

Figure 1. Plot map of the long-term soil-balancing experiment established May 2014 at the OARDC East Badger Farm, Wooster, Ohio1

1 “C” designates a corn plot, “S” designates a soybean plot, “V” represents a plot with the cover crop (clover), and “W_v” represents wheat/oats under-seeded to clover. Different colors designate blocks. Codes in the lower right hand corner are treatment codes: “LS” is limestone, “GF” is GFF amendments, “GP” is gypsum”, and “CT” is control.

For soil bulk density and texture, three additional cores were collected from each plot at a 15 cm depth on November 4 and 5, 2014. Bulk density measurements were completed on all samples from all plots. Soil texture was assessed only at the level of replication. Individual cores were kept in separate paper bags, put into a dryer, and dried at 49℃ for 72 h. Oven-dried samples were weighed. Bulk density was calculated in accordance with the USDA-NRCS Soil Quality Indicators (2008) document. Once the bulk density measurements were completed, some of those samples were selected randomly and used to determine soil texture. Soil texture was determined by the 30 hydrometer method (Gee and Bauder, 1986). Bulk density and soil texture were not measured again in 2015 due to the likelihood that soil treatments would not have an effect on these parameters.

To measure stable aggregates, samples were taken from each plot at the 0-15 cm depth. A rainfall simulator was acquired and configured in accordance with the Cornell

Soil Health Assessment Training Manual (2009). The methodology was followed precisely, except for the drying temperature, which was raised from 40℃ to 50℃. Water infiltration was measured in the field in each plot using the single ring method (Reynolds et al., 2002). This occurred during September 2015.

Biological activity in the soil was assessed by measuring β-glucosidase activity by the method of Eivazi and Tabatabai (1988) and was also measured on samples collected in autumn 2014. Their methodology was modified by using 0.5 M NaOH instead of tris(hydroxymethyl)aminomethane (THAM) buffer. Enzyme analysis was performed on samples from the 0-15 cm depth and assays were completed on samples from all plots.

Two samples from each plot were used, one sample treated with the β-glucosidase solution prior to incubation, and one as a control which did not receive any β-glucosidase until after the 1 hour incubation period (Eivazi and Tabatabai, 1988). β-glucosidase mineralizes plant residues and is involved in the carbon cycle (Stroobants et al., 2014).

The role of β-glucosidase in the carbon cycle is as follows: cellulose is degraded into cellobiose, which is hydrolysed by β-glucosidase to glucose, which can then be oxidized into CO2 (Stroobants et al., 2014). Due to this involvement in the carbon cycle, β- glucosidase is known as an indicator for soil quality (Stroobants et al., 2014).

Crop emergence counts were taken on June 12, 2014 and June 25, 2015. The number of plants in a 5.8 m section of the middle two rows of corn and in a 6.1 m section of the middle two rows of soybeans was determined. The two counts were then averaged together to get the final emergence value. Oat plant stand was determined by counting seedlings in two randomly located quadrants. The mean stand was used to calculate the number of plants ha-1.

Weeds were managed by between-row cultivation, rotary hoeing, hand-weeding, and mowing. Tillage was 15 cm deep while cultivation was 5-8 cm deep. Preplant tillage occurred in the corn and soybeans May 20, 2014. Corn and soybean plots were rotary hoed on May 28, 2014. Mowing occurred on July 14, 2014 in the oats. Corn and soybean plots were cultivated on June 10 and July 2, 2014. Hand-weeding occurred on July 10,

July 14, 2014 and August 4, 5, and 6, 2014. Many of the same weed control methods were used in 2015. All plots were tilled with a cultivator on May 11 and 14, 2015. Tillage with a field cultivator occurred in the corn, soybean, and wheat plots, after which corn and wheat plots were finish-disked on May 11, 2015. The clover plots were mowed on

June 4, while the corn, soybean and wheat plots were cultivated with a field cultivator on the same date in 2015. A row cultivator was run through the corn and soybean plots on

June 24 and July 6, 2015, and only the soybean plots on July 24, 2015. The wheat plots were mowed, then cultivated to terminate the crop on July 7, 2015 and July 24, 2015, respectively. After termination of the wheat, the plots were tilled periodically to kill seedlings as they emerged. Mowing occurred again in the clover plots on July 16, 2015.

To determine the effect of soil balancing treatments on the soil weed seedbank, soil samples were taken three times; November 19, 2013, March 15, 2014, and April 1,

2015. Sampling was from the at 0-15 cm depth. Soil samples were sieved and spread into trays (26.7 cm x 26.7 cm x 6.4 cm, with soil filled to 3.8 cm) lined with capillary matting.

Samples were then placed in a greenhouse with a temperature of 24ºC during the day and

21ºC during the night and a 16 h photoperiod (Al-Sarar, 2003). Trays were watered from the bottom in order to prevent a crust from forming on the top of the soil layer (Al-Sarar,

2003). Species were identified, counted, and then pulled from the soil as soon as the weeds could be identified, but before seeds were produced. The exhaustive germination technique as described by Al-Sarar (2003) was used with two modifications; samples were kept in the greenhouse for longer than the 4-6 weed period recommended because weed seedlings continued to emerge, and the stratification period of cold storage at 4° C was also extended. Once the stratification period was over, trays containing soil samples were again placed in a greenhouse at the same temperature regimen mentioned previously. A second cycle of germinating seeds and counting emerged seedlings was carried on until no more germination was observed (Al-Sarar, 2003).

To further document effects of soil balancing treatments on weed communities, emerged weeds growing in the field plots were identified at the species level and counted several times throughout each growing season, prior to each tillage/cultivation operation.

Counts were completed in three quadrants measuring 30 cm by 30 cm. Each time weeds were counted, the same quadrant positions were used. Weeds were counted on May 20,

May 23, June 5, June 27, and July 25, 2014, and on May 26, June 3, June 9, and July 7,

2015.

Crop leaf tissue samples were collected on July 18, 2014 and July 31, 2015 from the soybean plots and August 5, 2014 and July 29, 2015 from corn plots. When soybeans

33 were in the R1 stage, 25 of the uppermost fully developed trifoliate leaves were collected from each plot (A & L Great Lakes Laboratories, 2009). The corn was in the silking stage

(R1) at sampling. Fifteen leaves below the ears were collected from each plot. Samples were dried at 45ºC for 6 days, ground using a Wiley Mill to 2 mm or less, and submitted to the STAR Lab for nutrient analysis in 2014. In 2015, tissue samples were treated the same as the previous year, except Spectrum Analytic3 analyzed the samples.

Crop yield was determined from grain harvested from the middle two rows of the corn and soybean plots in 2014, and all rows of the corn and the middle two rows of soybeans in 2015. Grain protein, oil, fiber, and starch content were measured using near infrared transmittance (NIT) with a Tecator Infratec whole grain analyzer (Select

Science, n.d.). The grain analyzer was calibrated using the Composition Systems

Calibration developed by the Iowa State University.

Data were subjected to analysis of variance (ANOVA) using Proc GLM (SAS

9.0). Factors analyzed included block, crop, amendment, and the interaction of crop and amendment. When results of the ANOVA were significant, Fisher’s protected LSD was used for mean comparisons and LS means were used when replication was uneven. Log transformations (z=log(y+1)) were performed prior to analysis when raw data did not meet the assumptions of ANOVA. Weed species were assessed based on life habit

(summer annual, winter annual or perennial), broadleaf or grass, and seed size (small and large). Seed size was based on a combination of seed length and seed weight (Aldrich,

1984; Thompson, Band, and Hodgson, 1993; Stevens, 1932; Stevens, 1957). In general, seeds smaller than 1.5 mm in length and less than 1 mg in weight, were considered small-

3 Spectrum Analytic, Inc., Washington Courthouse, OH, http://www.spectrumanalytic.com 34 seeded. Seeds with a length greater than 1.5 mm and a weight greater than 1 mg were considered of a large-seeded species.

2.4 Results and Discussion

2.4.1 The effect of soil balancing amendments, and crop on soil chemical and physical parameters.

Table 4. Effect of sample depth and replication in 2013 on soil nutrient levels in a long- term experiment established at the East Badger Farm, OARDC, Wooster, OH in May 2014.1

Depth (cm) Variable Analysis Unit 0-152 15-30 30-45 Block3 pH 1:1 soil:water 5.8 5.6 5.8 0.3

OM4 LOI % 2.7 a5 2.5 a 1.7 b 0.14 CEC6 meq/100 g 10 9 13 0.3

P BP1 ug/g 24 a 21 a 2.0 b 0.02 S M3 ug/g 31 b 28 c 45 a 0.006 Ca M3 ug/g 786 721 836 0.051 Ca BS % 41 a 39 ab 35 b 0.0004 Mg M3 ug/g 177 b 160 b 243 a 0.09 Mg BS % 13 12 14 0.0009 K M3 ug/g 84 61 66 0.09 K BS % 3 a 2 b 2 b 0.035

1 For information regarding standard deviations, refer to Appendix A.2 2 Samples were collected from 3 depths (0-15, 15-30 and 30-45 cm) on November 19, 2013. Analysis included the Mehlich 3 (M3), Bray P-1 (BP1), loss-on-ignition (LOI). Base saturations (BS) were also determined. A log transformation was used on P, Ca (M3), Mg (M3), and K (M3) and an arcsine transformation was used on Mg (BS) and K (BS) due to high variation in the standard deviations. Non-transformed data are presented. 3 Values in the “Block” column are Pr > F values for each variable. 4 OM stands for organic matter. 5 Letters represent statistical differences according to the LSD (P<0.05). 6 CEC stands for cation exchange capacity.

Sampling and analysis prior to initiation of the experiment revealed that the soil was acidic, with a low CEC, and unbalanced (41% Ca, 13% Mg, and 3% K) according to the commonly accepted criteria of the BCSR approach. The texture was a silty clay loam with an average of 27% clay, 58% silt, and 14% sand. CEC ranged from 9 to 13 meq/100 g soil for the three sampling depths. Soil organic matter ranged from 2-3% in 2013. The field was relatively homogenous, with only a few variables (P, S, and base saturations of

Ca, Mg, and K) showing differences between replications (Table 4). Base saturations of

Ca and K were too low according to the BCSR approach, while Mg base saturation was too high.

Table 5. Effect of soil amendment in 2014 on soil nutrient levels in a long-term experiment established at the East Badger Farm, OARDC, Wooster, OH in May 2014.1 pH CEC2 P S Ca Ca Mg Mg K K

Analysis 1:1 B1 M3 M3 BS M3 BS M3 BS meq/100 Unit ug/g ug/g ug/g % ug/g % ug/g % g 0-15 cm4

Treatment3

GP+LS 6.2 a* 8 39 68 a 1000 59 ab 230 ab 18 b 75 b 3 b

37 LS 6.3 a 7 47 50 bc 942 63 a 241 a 22 a 76 b 3 b

GFF 5.8 b 8 50 54 b 886 58 ab 197 bc 17 bc 92 a 4 a CT 5.8 b 7 47 48 c 861 55 b 192 c 16 c 75 b 3 b 15-30 cm

GP+LS 6 8 23 73 a 884 52 232 19 57 2 LS 6 9 29 47 bc 846 53 221 18 55 2 GFF 6 8 36 50 b 769 51 193 17 61 2 CT 6 7 36 45 c 785 50 182 15 51 2 30-45 cm

GP+LS 5 10 11 75 a 974 a 52 289 20 77 2 LS 5 11 14 63 b 942 ab 53 279 22 74 2 GFF 5 9 12 63 b 856 b 49 258 20 76 3 CT 6 9 9 57 b 858 b 51 258 20 70 3 1 For information regarding standard deviations, refer to Appendix A.4 2 CEC stands for cation exchange capacity 3 GP+LS is gypsum plus limestone, LS is limestone only, GFF is Green Field Farms Cooperative Proprietary Amendments, and CT is untreated control. 4 Samples were collected from 3 depths (0-15, 15-30 and 30-45 cm) on October 13 and October 20, 2014. Analysis included the Mehlich 3 (M3), Bray P-1 (BP1), loss-on-ignition (LOI). Base saturations (BS) were also determined.

Analysis of samples taken mid-autumn of 2014, following soil balancing amendments applied in April, indicated that concentrations of most cations had increased, but soil balance was not achieved. Base saturation of Ca and Mg increased from the previous year, but K base saturation remained at the 2-4% range. Calcium concentration increased by at least 140 ug/g according to the Mehlich 3 analysis, and at least 8% in base saturation in the 0-15 cm sampling range. The dolomitic limestone, gypsum plus dolomitic limestone, and GFF plots had the highest base saturations of Ca in the 0-15 cm depth. Because Mehlich 3 will measure all calcium in the sample, rather than just exchangeable calcium, it is possible that base saturation may have been overestimated.

However, since both limestone and gypsum are calcium-based amendments an increase in calcium was expected. Magnesium increased by 7 % or greater in concentration and at least 2% in base saturation in the uppermost soil layer due to the added magnesium in dolomite. Levels of K in the uppermost sampling depth were greatest in the GFF plots that had received sulfate of potash (0-0-50) in spring 2014. CEC and pH did not appreciably change from the baseline measured the previous year. On the other hand, S concentration in the 0-15 cm range increased by at least 35% in the second year. Sulfur levels were highest in gypsum plus dolomitic limestone amended plots at all three sampling depths. This increase was likely caused by the sulfate component of gypsum.

There were no significant differences between any nutrient levels according to crop (data not reported).

Table 6. Effect of crop and soil amendment on soil bulk density (g cm3) (11/4/2014 and 11/5/2014). Also reported here is the effect of soil amendments on water infiltration (corn plots 9/15/2015 and 9/16/2015), stable aggregates (soybean plots only 6/8/2015 through 6/12/2015), and average β-glucosidase activity measurements of soils sampled on 10/1/2014.

Soil Amendment Crop

GP+LS1 LS GFF CT Corn Soybeans Clover Wheat Mean 1.12 1.15 1.11 1.13 1.10 b2 1.17 a 1.11 b 1.12 b Bulk density (g/cm3) Std. 0.05 0.06 0.04 0.05 0.03 0.06 0.04 0.03 Dev.3 β-glucosidase Mean 151 a 114 b 142 a 147 a 129 133 146 146 activity (PNP/g

39 of soil/hour) Std. Dev. 44 38 40 33 35 30 46 49

Mean 24 23 22 21 N/A Stable aggregates (g) Std. Dev. 7 4 3 2 N/A

Soil water Mean 0.018 0.019 0.022 0.026 N/A infiltration (cm/sec) Std. Dev. 0.006 0.004 0.006 0.004 N/A

1 GP+LS is gypsum plus limestone, LS is limestone only, GFF is Green Field Farms Cooperative Proprietary Amendments, and CT is untreated control. 2 Letters represent statistical differences according to the LSD (P<0.05). 3 Std. Dev. represents standard deviation

Measures of soil bulk density ranged from 1.10 to 1.17 g cm3 -1. Bulk density was

1.17 g cm3 -1 in the plots planted with soybean compared to an average of 1.11 g/cm3 in plots seeded to corn, and to wheat and clover. These bulk densities are within the suggested range according to the NRCS; plant growth is optimal for a silty clay loam when bulk density is <1.40 (USDA-NRCS, n.d). Simulated rainfall-stable aggregate values ranged from 21 to 24 g and the soil water infiltration rate ranged from 0.018 to

0.026 cm sec-1. Neither variable was affected by amendment. A treatment effect on β- glucosidase activity was noted. β-glucosidase activity was lowest in the dolomitic limestone-amended plots (114 PNP g-1 of soil h-1). β-glucosidase was similar in soils treated with all other amendments, ranging from 142 to 151 PNP g-1 of soil h-1.

2.4.2 The effect of soil balancing amendments on crops

In addition to soil parameters, crop parameters were also assessed. Crop stand, tissue analysis, grain analysis and yield were assessed in the corn and soybean plot.

Table 7. Effect of treatment on nutrient analysis (HClO4 Digest) in corn leaf tissue collected 8/5/2014 and soybean leaf tissue collected 7/18/2014 results from soil amendments applied on 4/24/2014. Mn was log transformed in the soybean tissue1.

Nutrient Levels in Corn Tissue Nutrient Levels in Soybean Tissue Acceptable GP + Acceptable GP + LS GFF CT LS GFF CT levels2 LS3 levels* LS N (%) 2.90-3.50% 1.7 1.4 1.3 1.5 4.25-5.50% 4.7 4.7 4.8 4.8 P (%) 0.30-0.50% 0.21 0.24 0.21 0.17 0.30-0.50% 0.29 0.31 0.29 0.30

41 1.42 K (%) 1.91-2.50% 4 1.50 a 1.51 a 1.32 b 2.01-2.50% 1.69 1.51 1.86 1.65

ab Ca (%) 0.21-1.00% 0.32 0.23 0.26 0.31 0.36-2.00% 0.93 a 0.52 a 0.82 b 0.89 a Mg (%) 0.16-0.60% 0.20 0.17 0.15 0.20 0.26-1.00% 0.35 0.37 0.31 0.33 S (%) 0.16-0.50% 0.16 a 0.11 b 0.11 b 0.13 b 0.21-0.40% 0.32 a 0.29 b 0.30 ab 0.28 b Al (ppm) N/A 8 10 12 10 N/A 60 72 35 51 B (ppm) 4-25 ppm 0.34 0.58 0.14 0.14 21-55 ppm 31 32 35 35 Cu (ppm) 6-20 ppm 4.2 3.1 2.9 4.1 10-30 ppm 7.5 a 5.7 ab 6.4 ab 4.5 b Fe (ppm) 21-250 ppm 66 66 56 67 51-350 ppm 105 118 74 101 Mn (ppm) 20-150 ppm 30.3 20.9 25.1 38.3 21-100 ppm 4.3 b 4.4 b 4.6 a 4.6 a Mo (ppm) N/A 0.40 b 0.81 a 0.31 b 0.30 b 1.0-5.0 ppm 0.3 0.4 0.3 0.4 Na (ppm) N/A 13.4 13.6 12.1 10.4 N/A 46.0 59.8 28.3 9.6 Zn (ppm) 20-70 ppm 28 21 22 22 21-50 ppm 48 41 46 39

1 For information regarding standard deviations, refer to Appendix B.1 2 Acceptable levels of each nutrient according to the Tri-State Fertilizer Recommendations (Vitosh et al., 1995) 3 GP+LS is gypsum plus limestone, LS is limestone only, GFF is Green Field Farms Cooperative Proprietary Amendments, and CT is untreated control. 4 Letters represent statistical differences according to the LSD (P<0.05).

Analysis of tissue from 2014 corn plots revealed deficiencies in N, P, K, S, Cu, and Mg in some treatments. Soybeans were also deficient in some nutrients, including: P,

K, Cu, and Mo. Throughout most of the 2014 growing season soybeans appeared to be in much better health than corn. In contrast to soybean, corn plants were chlorotic and stunted because N fertility had not been added to these plots, while soybeans form a symbiotic relationship with soil bacteria resulting in an internal N supply within the plant.

Sulfur levels were affected by treatment in both crops. Gypsum plus dolomite resulted in the highest S levels in corn and soybean, 1,606 and 3,203 ug g-1, respectively. This result corresponds to S levels detected in the soil in November 2014 (Table 7), where gypsum plus limestone treated plots also had the greatest S levels. Ca levels were lowest in soybean tissues treated with the GFF amendment, which only had 2,242 kg ha-1 of applied Hi-Cal limestone. Foliage from soybean treated with gypsum and limestone had higher levels of Ca.

The soybeans accumulated greater amounts of P, K, Ca, and Mg. Acceptable levels of P and K are similar between corn and soybeans, yet the soybeans had higher levels than did corn, but were still deficient in both P and K. Phosphorous in the soil (24 ppm) was greater than the critical soil test level (15 ppm) (Vitosh et al., 1995). However,

K (84 ppm) was below the critical level at CEC 10 (100 ppm) (Vitosh et al., 1995).

Alternatively, neither crop was deficient in Ca or Mg, yet soybeans accumulated more of these cations than the corn. However, soybeans need more of these divalent cations than corn does (Vitosh et al., 1995). Greater levels of these nutrients may have accumulated in the soybeans than in the corn, due to the better N nutrition in the soybeans versus the corn since soybeans produce N through a symbiotic relationship with Rhizobia bacteria.

The unhealthy corn was partially the result of the major N deficiency. Crop nutrition played a major role in the levels of nutrients in the tissue of both crops. Nitrogen and chlorophyll content are directly related. Thus a N deficiency reduces the plants photosynthetic efficiency, and may lead indirectly to a reduction in nutrient uptake

(Varvel et al., 1997; Anonymous, n.d.).

Table 8. Effect of treatment on nutrient analysis (HClO4 Digest) in corn leaf tissue collected 7/31/2015 and soybean leaf tissue collected 7/29/20151. Nutrient Levels in Corn Tissue Nutrient Levels in Soybean Tissue Acceptable Acceptable GP+LS3 LS GF CT GP LS GF CT levels2 levels* N (%) 2.90-3.50% 3.0 3.1 2.8 2.9 4.25-5.50% 4.5 4.6 5.0 4.6 P (%) 0.30-0.50% 0.34 0.33 0.33 0.32 0.30-0.50% 0.35 0.37 0.44 0.4

K (%) 1.91-2.50% 2.3 2.3 2.3 2.1 2.01-2.50% 2.0 2.0 2.2 2.0 Ca (%) 0.21-1.00% 0.66 0.6 0.58 0.62 0.36-2.00% 1.0 0.96 1.0 1.0 Mg (%) 0.16-0.60% 0.18 0.18 0.17 0.17 0.26-1.00% 0.34 0.33 0.37 0.38 S (%) 0.16-0.50% 0.40 a4 0.27 b 0.28 b 0.28 b 0.21-0.40% 0.32 0.3 0.35 0.33 B (ppm) 4-25 ppm 20 20 19 18 21-55 ppm 47 46 55 51 Cu (ppm) 6-20 ppm 15 14 13 13 10-30 ppm 12 11 13 12 Fe (ppm) 21-250 ppm 150 149 137 139 51-350 ppm 123 121 123 121 Mn (ppm) 20-150 ppm 80 71 73 73.5 21-100 ppm 75 76 91 101 Zn (ppm) 20-70 ppm 27 23 24 21 21-50 ppm 33 28 36 38 Na (ppm) N/A 7.0 9.0 7.0 5.5 N/A 9.5 5.3 6.3 6.5

1 For information regarding standard deviations, refer to Appendix B.2 2 Acceptable levels of each nutrient according to the Tri-State Fertilizer Recommendations (Vitosh et al., 1995) 3 GP+LS is gypsum plus limestone, LS is limestone only, GFF is Green Field Farms Cooperative Proprietary Amendments, and CT is untreated control. 4 Letters represent statistical differences according to the LSD (P<0.05).

In 2015, visual inspection throughout the season indicated that corn and soybeans were healthy; growth was vigorous and there were no visual deficiency or disease symptoms. There were no nutrient deficiencies in the foliage of either crop in 2015.

Nitrogen levels were 2-3 times higher in the corn leaf tissue in 2015 than in 2014.

Analysis of the corn tissue detected elevated levels of S in the gypsum treated plots, with

0.40 ug g-1, which is roughly 31% more than the other treatments in the corn (Table 8).

The gypsum plus dolomitic limestone amendment also resulted in the highest S levels in the soil in 2014, with 68 ug g-1 in the 0-15 cm depth. Soil amendments did not affect on any other foliar analysis variable in 2015.

Table 9. Effect of treatment on crop emergence counts (taken 6/12/2014) and analysis of corn and soybean grain (harvested 10/27/2014) from soil amendments applied on 4/24/20141. Corn Soybean GP + LS2 LS GFF CT GP + LS LS GFF CT Stand (plants Ha-1) 72,700 82,500 78,500 81,900 324,400 282,500 310,500 336,600 Yield (Kg Ha-1) 5,575 6,242 1,618 3,713 1,055 1,525 1,247 1,164 Protein (%) 6.1 6.1 6.2 6.1 38.8 39.8 36.9 38.5 Oil (%) 3.38 3.3 3.35 3.43 17.9 17.7 18.3 17.9 Fiber (%) 17.9 17.7 18.3 17.9 4.4 4.4 4.6 4.4 Density 1.2 1.2 1.2 1.2 N/A Starch (%) 62.9 63.1 62.8 62.9 N/A

1 For information regarding standard deviations, refer to Appendix B.3 2 GP+LS is gypsum plus limestone, LS is limestone only, GFF is Green Field Farms Cooperative Proprietary Amendments,

6 and CT is untreated control.

Treatment effects of amendments on corn and soybean stand, yield, or grain quality were not detected in 2014. The average corn stand in Ohio varies from 79,375 to

87,250 plants ha-1, indicating that our emerged counts were within the normal range for 3 out of 4 treatments (Thomison et al., 2014). Corn yield in Wayne County, Ohio averaged

10,362.9 kg ha-1 in 2014 (USDA NASS, 2015). Yield in the current experiment was roughly 60% or less of the county average, likely due to the lack of good crop nutrition in the corn plots. Average soybean yield in Wayne County, Ohio was reported to be 3,584.5 kg ha-1, indicating that our yields (1,248 kg ha-1 on average) were much lower than the state average (USDA NASS, 2015). Nutrient deficiencies may have been to blame for the lower than average yields in these crops. Variation in the yields of both crops was quite high. This was likely due to the poor health and nutrient deficiencies that the corn experienced during the growing season.

Table 10. Effect of treatment on crop emergence counts (taken 6/25/2015) and analysis of corn (harvested 10/7/2015) and soybean grain (harvested 9/24/2015) from soil amendments applied on 10/24/2014. Corn emergence data was log transformed1. Corn Soybean GP + LS GFF CT GP + LS LS GFF CT LS2 Stand (plants Ha-1) 58,100 59,700 60,000 61,900 439,000 385,900 522,700 575,000 Yield (Kg Ha-1) 6,900 6,900 6,600 6,600 1,800 2,000 2,100 2,100 Protein (%) 7.2 7.3 7.2 7.1 35.7 35.5 35.6 35.2 Oil (%) 4.2 4.1 4 4.1 18.7 18.9 18.8 19 Fiber (%) N/A 4.8 4.7 4.7 4.7 Density 1.3 1.3 1.3 1.3 N/A Starch (%) 62.6 62.5 62.6 62.5 N/A

4 For information regarding standard deviations, refer to appendix B.4 8 2 GP+LS is gypsum plus limestone, LS is limestone only, GFF is Green Field Farms Cooperative Proprietary Amendments, and CT is untreated control.

Treatment effects on corn and soybean we emergence, yield, or grain quality in 2015 were not detected.

2.4.3 The effect of soil balancing treatments and crop on weed communities. Table 11. Effect of soil amendment and crop counts of emerged weeds (individuals meter2) performed on 5/20, 6/5, 6/27, and 7/25/2014. Only species with a frequency of more than 20 are presented1. Soil Amendment Crop

Variable GP+LS2 LS GF CT C V W S Annual fleabane 23 19 25 24 26 21 23 20 Chickweed 34 27 37 37 38 35 33 30 Common lambsquarters 5 5 6 8 6 9 6 4 Common ragweed 3 3 2 2 2 4 2 2 Deadnettle 11 10 11 11 12 10 12 9 Giant ragweed 103 103 98 78 93 bc3 116 a 98 ab 74 c Poa spp. 8 8 9 5 7 6 8 9

49 Pennsylvania smartweed 43 b 43 b 51 b 61 a 48 50 52 48

Purslane speedwell 36 b 37 b 43 b 53 a 42 43 42 43 Velvetleaf 4 5 6 7 5 8 5 3 Yellow woodsorrel 2 b 6 a 3 b 4 ab 1 b 7 a 5 a 1 b Total Weeds 278 271 301 298 285 b 317 a 297 ab 249 c Total Weed Species 17 18 18 18 17 b 19 a 19 a 17 b Total Broadleaf Plants 241 240 260 258 244 b 278 a 260 ab 216 c Total Grass Plants 34 27 37 37 38 35 33 30 Total Summer Annuals 189 185 198 185 187 b 215 a 194 ab 161 c Total Winter Annuals 84 b 80 b 94 ab 105 a 93 95 92 82 Total Large-Seeded Plants 161 161 167 153 155 bc 185 a 165 ab 137 c Total Small-Seeded Plants 111 b 104 b 125 ab 137 a 125 125 121 106 1 For information regarding standard deviations, refer to Appendix C.1 2 GP+LS is gypsum plus limestone, LS is limestone only, GFF is Green Field Farms Cooperative proprietary amendments, and CT is untreated control. 3 Letters represent statistical differences according to the LSD (P<0.05)

Flora in the field during 2014 consisted of mainly summer annual, large-seeded, broadleaf weeds (Table 11). Summer annuals were two times or more abundant than winter annuals. Broadleaf weeds exceeded grass weeds by more than 189 individuals m2.

Giant ragweed, Pennsylvania smartweed, purslane speedwell, and annual fleabane were dominant, with other species making up less than 23% of the total weeds. The number of species recorded per square meter ranged between 17 and 19.

Soybean plots had the fewest weeds, almost without exception. The total number of weeds was 249 plants m2, compared with a range of 285 to 317 plants m2 in the other crops. A similar effect was noted respecting the synthetic variables of total weeds, total broadleaf weeds, total summer annuals, and total winter annuals. Better weed control in soybean likely reflects the high efficacy of inter-row cultivation early in the season and in contrast to corn, the fact that the soybean canopied much earlier in the growing season.

Soybeans were inter-row cultivated 2 times, while corn was cultivated 3 times. Due to the nutrient deficiencies and relative lack of nutrients available to the corn, it was less competitive with weeds as well. In contrast to soybean, plots seeded with clover and oats had more weeds. However, it is important to note that during 2014, the experiment establishment year, both sets of plots were treated identically throughout; consisting of a

May seeding of oat under seeded to red clover. Inter-row cultivation was not practiced in these plots during 2014 and is a likely explanation of the larger communities observed.

Weed control consisted of mowing within these plots once during the season.

The untreated control plots had the most weeds according to several of the variables. Pennsylvania smartweed, purslane speedwell, yellow woodsorrel, the number of winter annuals and the number of small-seeded plants were all highest in control plots.

Occasionally, GFF-treated plots and control plots shared similar characteristics.

Examples are the total winter annuals and total small-seeded plants. These two

“treatments” had the lowest amounts of added calcium (with the untreated control having none added).

Table 12. Effect of soil amendment and crop on counts of emerged weeds (individual meter2) 5/26, 6/3, 6/9 and 7/7/2015 in the field. Only species with a frequency of more than 20 are represented1. Soil Amendment Crop GP+LS Variable 2 LS GF CT C V W S Common chickweed 12 19 8 12 4 c3 1 d 10 b 33 a Common lambsquarters 14 27 22 32 51 a 1 c 11 b 32 a Dandelion 8 6 5 4 10 a 1 c 3 bc 6 ab Giant ragweed 68 59 60 50 82 b 3 d 104 a 48 c

52 Poa spp. 50 43 87 42 87 20 62 53

Pennsylvania smartweed 7 13 9 9 14 a 1 c 8 b 16 a Redroot pigweed 6 4 5 8 10 1 4 7 Virginia copperleaf 3 1 5 7 8 a 2 b 2 b 4 ab Yellow woodsorrel 2 2 4 11 5 4 4 6 Total Weeds 183 198 233 195 281 a 40 b 242 a 246 a Total Weed Species 15 15 16 15 91 b 14 c 65 a 114 a Total Broadleaf Plants 132 154 145 153 194 a 18 b 179 a 193 a Total Grass Plants 51 44 88 42 87 a 22 b 63 a 53 a Total Summer Annuals 157 159 206 167 264 a 35 b 211 a 180 a Total Winter Annuals 16 31 19 22 7 c 2 d 25 b 54 a Total perennials 9 8 8 6 10 a 3 b 6 b 11 a Total Large-Seeded 130 120 167 106 190 a 25 b 211 a 132 a Plants Total Small-Seeded 53 78 66 88 91 ab 14 c 65 b 114 a Plants 1 For information regarding standard deviations, refer to Appendix C.2 2 GP+LS is gypsum plus limestone, LS is limestone only, GFF is Green Field Farms Cooperative Proprietary Amendments, and CT is untreated control. 3 Letters represent statistical differences according to the LSD (P<0.05).

In 2015, summer annual, large-seeded, broadleaf weeds, particularly giant ragweed, were dominant. Annual grasses had similar values to those of giant ragweed, even exceeding giant ragweed in some treatments or crops. Grassy weeds can be more difficult to control, particularly in wet weather when cultivation is less effective at uprooting their fibrous root systems. Tillage is most effective in dry, hot weather because it allows for desiccation and death of weeds (Radosevich et al., 1997). In contrast to

2014, there were fewer total weeds, especially when it came to giant ragweed. There were also changes in the dominant species. Annual fleabane, common ragweed, purslane speedwell, and velvetleaf, all prevalent the previous year, were not so in 2015. Instead, we observed for the first time Virginia copperleaf, dandelion, and redroot pigweed. In contrast to 2014, control plots were not the most heavily infested with weeds. While soil amendment did not significantly affect any of the weed variables, crop affected several.

Clover plots had the fewest weeds, dramatically so for some variables. For example, common chickweed, dandelion, and common lambsquarters only had an average of 1 plant m2, while other crops had at least 3 times more plants m2 than clover plots. Clover was established in May 2014, and by early 2015 had developed a dense canopy that provided nearly complete cover over the soil surface. The canopy shaded the soil surface likely preventing weed seeds from germinating, and seedlings from establishing. As there were fewer disturbances in these plots in 2015, few annual weeds emerged. Corn, planted in 2014 clover/oat plots, consistently had the largest number of weeds across variables.

These results are in line with the heavy weed infestation noted in clover plots during the previous year. Large weed numbers were likely a result of heavy seed production in those

plots during the previous growing season. Giant ragweed plants had been removed by

hand from 2014 corn before any seed production occurred.

Table 13. Distribution of species across blocks as determined through the exhaustive germination performed on seedbank samples collected on November 19, 2013 reported in individuals meter2. Only species with a frequency of more than 20 are represented1. Block Variable 1 2 3 4 Annual Fleabane 2 4 2 2 Common Lambsquarters 1 2 2 2 Giant Ragweed 3 3 3 3 Green Foxtail 1 1 2 1 Pennsylvania Smartweed 0 2 3 1 Purslane Speedwell 3 2 4 9 Slender Rush 8 b2 41 a 31 a 22 ab 61 White Clover 2 3 3 4 Yellow Rocket 1 b 2 a 1 b 1 b Yellow Woodsorrel 9 5 6 8 Total Weeds 35 74 63 66 Total Broadleaf Plants 32 b 72 a 61 a 64 a Total Grass Plants 3 2 2 2 Total Weed Species 11 b 14 a 12 ab 14 a

1 For information regarding standard deviations, refer to Appendix C.3 2 Letters represent statistical differences according to the LSD (P<0.05).

Prior to initiation of the field experiment, soil samples were taken on November

19, 2013. Samples were taken by block for the purpose of determining the resident weed

community through the exhaustive germination technique. Data indicated that blocks 2

and 4 were slightly more rich in species than blocks 1 and 2. Broadleaf weeds

outnumbered grasses by at least 16 times. Slender rush was the most abundant weed in

the seedbank; however, only a few were observed in the field. Another difference

between the weed counts in the field and soil seedbank counts was the abundance of giant

ragweed. Giant ragweed dominated in the field in 2014 but was not nearly as dominant

in the seedbank. Seedbank samples taken prior to the initiation of the experiment were

not a good predictor of the weeds that were abundant in the field the following year.

Cardina et al. (2002) reported that the seedbank is a representation of existing vegetation

that is effected by past and current management practices, and may provide insight into

future vegetation.

Table 14. Effect of soil amendment on exhaustive germination performed on seedbank samples collected on March 15, 2014, reported in individuals meter2. Only species with a frequency of more than 20 are represented1. Soil Amendment2 Variable GP+LS3 LS GF CT

Annual Fleabane 3 2 6 4 Bittercress 1 1 3 2 Chickweed 6 14 6 15 Common Lambsquarters 4 4 4 6 Giant Ragweed 6 5 7 6 Henbit/Purple 5 9 3 15 Deadnettle Indian Tobacco 4 4 2 3 Purslane Speedwell 17 13 28 25 Slender Rush 3 4 7 13 Yellow Rocket 16 10 11 10 Yellow Woodsorrel 41 34 36 39 Total Weeds 115 108 122 144 Total Broadleaf Plants 112 107 120 142 Total Grass Plants 3 1 2 2 Total Weed Species 26 29 28 28 1 For information regarding standard deviations, refer to Appendix C.4 2 GP+LS is gypsum plus limestone, LS is limestone only, GFF is Green Field Farms Cooperative Proprietary Amendments, and CT is untreated control. 3 Soil amendments had not yet been applied at the time seedbank samples were taken.

Yellow woodsorrel was the most abundant weed, with 34-41 weeds m2. Because

yellow woodsorrel was not a major species in the previous year’s field flora, it is likely

that many seeds had shattered from scattered, low density individual plants. Yellow

55 woodsorrel plants can shatter seeds up to 16 feet (Neal and Derr, 2005). Giant ragweed populations were not as abundant in soil samples taken in spring 2015, even though it was dominant in the field in 2014. Seedbank samples were once again not a good indicator of the emergent weed community that developed in the field.

Table 15. Effect of soil amendment and crop on the exhaustive germination performed on seedbank samples collected on April 1, 2015, reported in individuals meter2. Only species with a frequency of more than 20 are represented1.

Soil Amendment2 Crop Variable GP+LS LS GF CT C V W S Common lambsquarters 7 9 9 17 6 14 12 9 Giant Foxtail 3 4 8 4 2 4 5 6 Giant ragweed 13 8 13 8 9 9 12 14 Green Foxtail 3 4 8 12 3 8 11 6 Henbit/Deadnettle 2 5 4 6 2 4 6 3 Indian Tobacco 23 29 15 29 43 22 5 26

7 Pennsylvania 2 5 4 2 3 3 4 4 smartweed Purslane Speedwell 13 13 6 11 8 6 10 18 Slender Rush 32 7 8 23 23 19 7 21 St. John's Wort 9 14 11 18 19 12 12 9 Virginia copperleaf 2 3 4 7 3 3 7 3 Yellow Woodsorrel 6 14 10 17 11 13 13 10 Total Weeds 132 146 121 170 158 141 125 144 Total Broadleaf Plants 93 127 96 131 128 108 101 110 Total Grass Plants 39 20 25 39 31 34 25 33 Total Weed Species 28 30 28 30 26 28 32 29

1 For information regarding standard deviations, refer to Appendix C.5 2 GP+LS is gypsum plus limestone, LS is limestone only, GFF is Green Field Farms Cooperative Proprietary Amendments, and CT is untreated control.

No treatment or crop effects were detected on any variables for the 2015 seedbank samples. All species were similarly abundant, with the exception of Indian tobacco, which was slightly higher. This datum does not correspond with observations made in the field. Giant ragweed was far more abundant in the field than any other species in both

2014 and 2015, yet this did not correspond with the 2015 seedbank data. In contrast to the 2014 seedbank samples, yellow woodsorrel (oxalis) was much less dominant.

2.5 Conclusions

Sampling and analysis in 2013 and 2014 revealed that soil balance was not achieved. Base saturations of Ca and K were too low and Mg was too high (Kopittke and

Menzies, 2007). In 2013, the field was relatively homogeneous. The soil had a low soil pH and CEC. Sulfur concentration was greatest in the soils treated with gypsum plus dolomitic limestone. Treatment effects on soil bulk density, water infiltration, stable aggregates, or average β-glucosidase activity were not detected. However, crop affected bulk density, being higher in soybean than in corn, clover, or oats.

Tissue analysis indicated numerous nutrient deficiencies in corn and soybeans in

2014 when no external N was added other than a small amount of compost (1,121 kg ha-

1) in plots treated with the GFF amendment. In particular, visual assessment of the corn indicated that it was chlorotic and stunted by early August, indicating poor nutrition. In

2015, neither crop experienced measurable deficiencies, and both appeared to be in good health. Analysis of tissue showed that soils treated with gypsum plus dolomite had

58 elevated levels of S corresponding well with higher analysis in the soil (Chen and Dick,

2011). Amendments did not affect crop emergence, yield, or grain nutrient content in either year. In 2014, yields were low, due in part to nutrient deficiencies indicated by tissue analysis. Yields of corn and soybeans increased in 2015, a fact that can be attributed to crops being in better health according to the tissue analysis.

Soil balancing amendments did not measurably affect weed communities in the field or soil seedbank samples. However, certain weed community characteristics were identified. Broadleaf weeds were more prevalent than grasses in seedbank samples and in the field. In the field, summer annual weeds with large seeds were also dominant. Fewer weeds were observed in field plots in 2015 than in 2014. Soil seedbank samples did not provide a good indication of weeds that would be observed in the field. Since soil balance according to BCSR standards was not yet achieved, no attempt to draw conclusions about effects of balancing on weeds should be made at this point. In the future, long-term results of this research will be of importance to many of Ohio and Indiana’s farmers who use soil balancing on their farms

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Chapter 3: The effect of natural product herbicides on hairy galinsoga, common

lambsquarters, common purslane, large crabgrass, and Johnsongrass

3.1 Abstract

Organic farmers have consistently claimed that their greatest challenge is weed control. Weed control in organic operations is carried out using a variety of methods, including tillage, cultivation, and cultural practices. Essential oils and organic acids have been studied to determine whether application of these products could improve weed control. However, their efficacy is still widely unknown. Greenhouse experiments were initiated at The Ohio State University’s Ohio Agricultural Research and Development

Center (OARDC) in Wooster, Ohio to determine the efficacy of cinnamon oil, manuka oil, lemongrass oil, clove oil, citric acid, citric acid with garlic oil, and acetic acid on hairy galinsoga, common lambsquarter, common purslane, large crabgrass, and

Johnsongrass. Injury was visually assessed on a 0-100 scale (0 = no injury; 100 = death) at 1 and 3 days after treatment (DAT) and 1 and 2 weeks after treatment (WAT). Plants were harvested for fresh and dry weights at 2 WAT. Essential oils had higher efficacy than acids, however, many weeds showed recovery by 2 WAT. Results indicated that manuka oil performed consistently well across species and trials, with obvious phytotoxicity often lasting through 2 WAT. Species differed in their responses to organic

69 herbicides. For example, hairy galinsoga was less susceptible to these natural products than were the other two broadleaf weeds. There were differences between the first and second run of the experiment. Broadleaf control was better, particularly for hairy galinsoga, and grass control was worse in the second run compared with the first.

Differences in weed growth stage and seasonal differences were likely to blame for the differences between experiments.

3.2 Introduction

Demand for organically grown fruits, vegetables and grain products is increasing on a yearly basis (USDA ERS, 2013). Between 2011 and 2012, organic food sales increased by 11% (USDA ERS, 2013). Consumer interest in organic products is largely due to their environmentally friendly production methods and perceived freedom from chemical contaminants. However, production can be challenging in terms of pest, disease, and weed control because the efficacy of control methods is generally low. In respect to weeds, timing and frequency of control methods can be challenging and costly.

For example, cultivation can have low efficacy if timing is not right (i.e. weeds are too large). Cultivation is also not selective, meaning that damage to crops can occur. Also, weather conditions dictate when and if cultivation can occur, leaving the farmer open to the possibility that weeds can get out of control. Herbicides are more forgiving in terms of weather and timing, reducing the chance of weed populations growing to disastrous levels.

Herbicides are one control method that could lead to increased efficacy in weed control for farmers. Natural product herbicides approved for use in organic operations are often expensive and have low-efficacy. Controlling weeds without harming the crop is difficult because candidates identified to date are non-selective. Discovery of a selective herbicide that is approved for organic use would be extremely useful for farmers. To be used in certified organic systems an herbicide must be OMRI registered (Organic

Materials Review Institute). OMRI is an independent organization that receives applications from companies hoping to release their products with organic certification.

OMRI then reviews these products to determine whether they meet organic standards and if so, the product is then added to a list of other approved products.

The efficacy of natural product herbicides being considered for approval by

OMRI is relatively unknown and cost remains high. The high cost is partially due to the fact that most require multiple applications to achieve control. Thus, more product is applied and more fuel, time, and man-hours are used in the application process (Dayan et al., 2011). An example of the cost associated with these herbicides is application of clove oil at a dose of 10-40%, which would cost $880 to $2,140 per hectare (Boyd and

Brennan, 2006). Even the lowest costs associated with natural product herbicides may be enough to prevent most farmers from using them. In order to justify these costs, the efficacy of the potential products on common weeds needs to be investigated.

A number of essential oils and acids have been evaluated as possible herbicides that could be used in organic farming. Essential oils are plant-derived products that are presently used as natural flavors and/or fragrances (Mukhopadhyay, 2000). Examples of

71 natural products that have been previously studied for use as herbicides include but are not limited to: clove oil, cinnamon oil, eucalyptus oil, manuka oil, garlic oil, lemongrass oil, acetic acid, and citric acid. However, much remains unknown regarding their efficacy, active ingredient(s), mechanism of action, and effective rates.

Most natural product herbicides that are approved for use in organic operations are applied as burn-down, POST applications, with no systemic activity and non-specific mechanisms of action (Dayan et al., 2009; Dayan et al., 2011; Dayan & Duke, 2010).

These essential oils disrupt the leaf cuticle, causing desiccation of young plant tissue

(Dayan et al., 2009; Dayan et al., 2011). Some contain allelochemicals, and may inhibit the germination of weed seeds (Dudai et al., 1999). Many of the OMRI certified organic herbicides contain mixtures of essential oils and acids, such as Weed-A-Tak4, which is a mixture of citric acid, clove oil, and cinnamon oil.

Manuka oil, lemongrass oil, cinnamon oil, clove oil, acetic acid, citric acid, and a mixture of citric acid and garlic oil were selected for inclusion in the research reported here because previous studies indicated that the efficacy of these products should be further examined. Acetic and citric acids were the only products tested in the current experiment that are not essential oils.

Manuka oil is unique amongst the essential oils because of POST and PRE activity (Dayan et al., 2011). It is derived from the manuka tree (Leptospermum scoparium J.R. and G. Frost) (Dayan et al., 2011). The active ingredient is leptospermone, which can remain stable in the soil for up to 1 week (Dayan et al., 2011).

4 Weed-A-Tak by Natura, http://www.deergone.com/Products-Weed-A- Tak_Herbicide.html 72

Manuka oil has also been shown to contain β-triketone. β-triketone is the basis for a group of synthetic herbicides (HPPD inhibitors) currently on the market for use in conventional systems (Dayan et al., 2011). Examples of these synthetic herbicides include: mesotrione and tembotrione (Anonymous, 2013). β-triketones target the HPPD

(ρ-hydroxyphenylpyruvate dioxygenase) enzyme (Dayan et al., 2007; Meazza et al.,

2002; Romagni et al., 2000; as cited by Dayan et al., 2011). Manuka oil controls both monocotyledonous and dichotyledonous weeds (Dayan et al., 2011).

Cinnamon oil has activity on dandelion (Taraxacum officionale Weber in Wiggers

TAROF), Johnsongrass (Sorghum halapense (L.)), common lambsquarters

(Chenopodium album L.), and common ragweed (Ambrosia artemisiifolia L. AMBEL)

(Tworkoski, 2002). Tworkoski (2002) identified eugenol, benzyl benzoate (benzoic acid phentylmethyl ester), humulene (2,6,6,9-tetramethyl-1,4,8-cycloundecatriene), and isoeugenol (2-methoxy-4-propenyl-phenol) in his pursuit of the active ingredient in cinnamon oil (Tworkoski, 2002). He determined that eugenol is the active compound.

Clove oil is a product from the leaves of clove trees (Eugenia aromatica L. Baill) that also contains eugenol (Evans et al., 2009). Boyd and Brennan (2006) determined that clove oil is most effective at a dose of 10-40%. They estimated a range of $880-1,600 ha-

1 for controlling purslane with clove oil and suggested it be used as a spot-treatment

(Boyd and Brennan, 2006). Lemongrass oil was identified as a potential organic herbicide in England in 1924 (Charlesworth, 1924; as cited by Dayan et al., 2009). The main constituent of lemongrass oil is citral (80%) (Dayan et al., 2009). Lemongrass has no systemic activity (Dayan et al., 2009).

Vinegar is a naturally occurring fermentation product that has non-selective herbicidal activity (Dayan et al., 2009). Household vinegar can have up to 20% acetic acid (Dayan et al., 2009). Respecting costs, Dayan et al. (2009) estimated the cost of using acetic acid as an herbicide to be greater than 10 times the cost of using a non- selective synthetic alternative, such as glyphosate. In addition, because acetic acid has contact activity only, it controls the portions of the plant that intercept the herbicide, leaving the root system intact (Dayan et al., 2009). Citric acid at a 10% dilution was shown by Abouziena et al. (2009) to have high efficacy on stranglervine (Morrenia odorata Hook & Arn.), black nightshade (Solanum nigrum L.), and velvetleaf (Abutilon theophrasti Medik), providing 95% or greater control 4 WAT. A mixture of 5% citric acid plus 0.2% garlic acid controlled 90-100% of stranglervine, wild mustard (Brassica kaber (DC.)), black nightshade, sicklepod (Senna obtusifolia (L.)), velvetleaf, and redroot pigweed (Amaranthus retroflexus L.) 1 WAT (Abouziena et al., 2009). Interestingly,

Abouziena et al. (2009) reported that when a mixture of 5% citric acid was more effective than a 10% concentration. This finding suggests that essential oil mixtures of various proportions should be further evaluated.

3.3 Materials and Methods

Experiments were conducted during spring and early summer of 2015 in greenhouses at The Ohio Agricultural Research and Development Center in Wooster,

Ohio (40.77 °N, -81.93 °W, and elevation 358 m). Treatments included an untreated control, manuka oil (Natural Solutions by Atmor Sales and Marketing, PO Box 44,

Beachlands Manukau 2147, Auckland, New Zealand), lemongrass oil (Aura Cacia, 5398

31st Avenue, Urbana, IA, 52345), cinnamon leaf oil (Aura Cacia, 5398 31st Avenue,

Urbana, IA, 52345), clove bud oil (Aura Cacia, 5398 31st Avenue, Urbana, IA, 52345), vinegar (Weed Pharm - 20% acetic acid) (Pharm Solutions, Inc., 2023 E. Sims Way,

Suite 358, Port Townsend, WA 98368), citric acid (New England Cheesemaking Supply

Company, 54B Whately Road, South Deerfield, MA 01373), and a mixture of 50% citric acid (New England Cheesemaking Supply Company, 54B Whately Road, South

Deerfield, MA 01373) and 50% garlic oil (Bulk Apothecary, 125 Lena Drive, Aurora,

OH 44202). For the remainder of this chapter vinegar will be referred to as acetic acid.

Weeds were hairy galinsoga (Galinsoga quadrirata), large crabgrass (Digitaria sanguinalis), common purslane (Portulaca oleracea), common lambsquarters

(Chenopodium album), and Johnsongrass (Sorghum halapense). There were 5 replications and the experimental design was a randomized complete block.

Weed seeds were germinated in petri dishes in a growth chamber with a day temperature of 30℃ and a night temperature of 25℃, and a photoperiod of 16 light hours and 8 dark hours. Seedlings were transplanted into ProMix (Premier Tech Horticulture

INC., 127 S. Fifth St., #300, Quakertown, PA 18951) in 15.24 cm tall cones when they were newly germinated (1-2 leaf stage) and then placed in the greenhouse. Due to lack of transplant survival in some species, seeds of those were directly planted in the cones filled with ProMix and then put into the growth chamber. The greenhouse temperature was 24 to 28℃ during the day and 21 to 24℃ at night. Lighting was supplemented when outdoor light levels were less than 250 watts per square meter. Lights in the greenhouse

75 turned off when sunlight was greater than 350 watts per square meter. After transplanting, plants were fertilized weekly with Jack’s Professional (JR Peters, Inc.,

6656 Grant Way, Allentown, PA 18106) 20-20-20 at 150 ppm at the 1/100 setting (which means 1%, or 1 part concentrate for 100 parts of water will be delivered).

Three ml of each herbicide concentrate were mixed and applied with 26.7 ml of water, and 1% v/v (0.3 ml) NuFilm P sticker/ spreader (Miller Chemical & Fertilizer,

LLC., Box 333, 120 Radio Road, Hanover, PA., 17331) for a 10% V/V application.

Individuals within a species were blocked according to their size, from largest to smallest. The first replication was always the largest plant and the last was the smallest.

POST applications of each herbicide were made using a handheld sprayer set at 275 kPa.

Herbicides were applied when plants were in the 2-4 leaf stage of common purslane, and in the 4-6 leaf stage of hairy galinsoga, common lambsquarter, large crabgrass, and

Johnsongrass. The output of the sprayer at 40 PSI was 0.5 ml per plant. Injury was visually rated on a 0% (no injury) to 100% (death) scale at 1 DAT, 3 DAT, 1 WAT, and 2

WAT. At 2 WAT, plants were harvested, weighed fresh, dried, and weighed again. In the first run of the experiment, hairy galinsoga, common lambsquarter, and common purslane were sprayed on April 7, March 4, and March 31, 2015, respectively, while Johnsongrass and crabgrass were sprayed on February 24, 2015. In the second run of the experiment hairy galinsoga, common lambsquarter, and common purslane were sprayed on July 5,

May 7, and June 5, 2015, respectively, and Johnsongrass and large crabgrass were sprayed on April 29 and May 6, 2015, respectively.

Data were subjected to analysis of variance (ANOVA) using Proc GLM in SAS

9.0. Factors analyzed included block, crop, amendment, and the interaction of crop and amendment. When results of the ANOVA were significant, Fisher’s protected LSD was used for mean comparisons and LS means were used when replication was uneven. Log

(z=log(y+1)) and arcsine transformations (z=arcsin(sqrt(percent/100)) were performed on data prior to analysis when data did not meet the assumptions of ANOVA. For information regarding standard deviations, refer to the Appendix E (page 122).

3.4 Results and Discussion Table 16. The effect of natural product herbicides on hairy galinsoga, common lambsquarters, and common purslane. Percent control was visually assessed on a 0-100 scale (0 = no injury; 100 = death)1. % Control Dry Weight (g)

Species Product 1 DAT2 3 DAT 1 WAT 2 WAT 2 WAT Cinnamon Oil 60 a3 55 a 40 ab 15 0.20 a Manuka Oil 60 a 60 a 65 a 30 0.09 b Lemongrass Oil 60 a 40 a 35 b 20 0.23 a Clove Oil 25 b 15 b 15 cd 10 0.25 a Hairy galinsoga Citric Acid 25 b 20 b 30 bc 35 0.12 b CA & GO4 10 b 10 b 10 d 5 0.13 b Acetic Acid 10 b 10 b 10 d 5 0.14 b Control 0 0 0 0 0.24 a Cinnamon Oil 80 a 70 b 80 b 15 b 0.11 b Manuka Oil 85 a 90 a 95 a 95 a 0.04 c Lemongrass Oil 90 a 90 a 90 a 85 a 0.03 c Common Clove Oil 80 a 80 ab 40 c 10 b 0.15 b

8 lambsquarters Citric Acid 20 b 10 c 5 d 5 b 0.23 ab CA & GO 10 c 10 d 5 de 0 c 0.24 a Acetic Acid 0 d 0 c 0 e 0 c 0.24 a Control 0 0 0 0 0.24 a Cinnamon Oil 80 a 80 a 95 a 95 b 0.004 c Manuka Oil 70 a 80 a 100 a 100 a 0.00 c Lemongrass Oil 60 a 80 a 100 a 100 a 0.00 c Clove Oil 70 a 80 a 90 a 90 b 0.008 c Common purslane Citric Acid 30 b 25 b 40 b 20 c 0.08 b CA & GO 30 b 10 c 20 b 20 c 0.11 ab Acetic Acid 5 c 0 d 25 b 15 c 0.12 ab Control 0 0 0 0 0.15 a Continued

Table 16: Continued 1 For information regarding standard deviations, refer to Appendix E.1 2 DAT represents “days after treatment”. WAT represents “week(s) after treatment”. 3 Letters represent statistical differences according to the LSD (P<0.05). 4 CA & GO: Citric Acid and Garlic Oil Mixture

Natural product herbicides were generally fast acting, with symptom development obvious within 24 h. Essential oils were particularly phytotoxic resulting in apparent mortality ranging from about 60 to 80% of common lambsquarters and common purslane seedlings within 24 hours of application. However, weeds appeared to recover from initial injury quickly and by 2 WAT only common lambsquarters and common purslane retained high levels of foliar injury in response to cinnamon, manuka, lemongrass and clove oils. With very few exceptions onset of recovery was detected after the passage of just 3 days. In a separate study by Tworkoski (2002) cinnamon oil injured common lambsquarters with just a 1% concentration, and 7 DAT killed most weeds with concentrations of 5-10%.

Cinnamon, manuka, lemongrass and clove oil were consistently the top performers; whereas, citric acid, acetic acid and the mixture of clove oil and garlic oil never provided more than 30% control. Citric acid provided more control of lambsquarters and purslane than did acetic acid. Citric acid had no obvious effect on lambsquarters. Of the best performing natural products, manuka oil invariably rose to the top, providing 95 and

100% control of common lambsquarters and common purslane, respectively, 2 WAT.

Lemongrass and cinnamon oil were as effective in controlling purslane as manuka oil, and lemongrass oil scored the same as manuka oil on common lambsquarters.

Hairy galinsoga response to these natural products was less intense than common lambsquarters and common purslane. The maximum response was 65% injury 1 WAT with manuka oil. The response to other herbicides at this rating interval ranged from

10% with acetic acid to 40% with cinnamon oil. By 2 WAT the average response of galinsoga to all herbicides was 20%.

Weed dry weights were recorded following the 2 week visual evaluation period, and shed valuable additional insight into weed response. Reduction in weed dry weight corresponded with the high levels of injury noted with cinnamon, manuka and lemongrass oil. Galinsoga dry weight was reduced by 50% or more following treatment with manuka oil, as was the case when treated with citric acid. Even acetic acid reduced dry weight relative to untreated plants. These data indicate that growth was checked with these herbicides more than indicated by the visual assessments. This observation suggests that weed competitiveness would be reduced and perhaps fewer seed produced following treatment.

Table 17. The effect of natural product herbicides on Johnsongrass and large crabgrass. Percent control was visually assessed on a 0-100 scale (0 = no injury; 100 = death)1. % Control Dry Weight (g)

Species Product 1 DAT2 3 DAT 1 WAT 2 WAT 2 WAT Cinnamon Oil 75 a3 75 a 70 ab 50 b 0.02 bc Manuka Oil 95 a 95 a 95 a 95 a 0.01 d Lemongrass Oil 35 b 30 b 45 bc 30 bc 0.02 c

8 Clove Oil 80 a 80 a 60 b 35 bc 0.02 c 1 Johnsongrass Acetic Acid 20 b 15 bc 15 cd 15 cd 0.03 bc Citric Acid 0 c 5 c 10 d 0 d 0.05 a CA & GO4 25 b 25 b 15 cd 10 cd 0.04 ab Control 0 0 0 0 0.03 bc Cinnamon Oil 70 b 70 c 10 c 5 c 0.08 bc Manuka Oil 95 a 95 a 90 a 95 a 0.04 d Lemongrass Oil 90 ab 90 ab 80 a 75 a 0.05 cd Large Clove Oil 75 b 75 bc 35 bc 30 b 0.05 cd crabgrass Acetic Acid 35 c 35 de 35 bc 25 bc 0.10 b Citric Acid 0 d 20 e 35 bc 35 b 0.13 a CA & GO 40 c 45 d 40 b 40 b 0.10 ab Control 0 0 0 0 0.10 b

1 For information regarding standard deviations, refer to Appendix E.2 2 DAT represents “days after treatment”. WAT represents “week(s) after treatment”. 3 Letters represent statistical differences according to the LSD (P<0.05). 4 CA & GO: Citric Acid and Garlic Oil Mixture

Cinnamon oil, manuka oil, and clove oil were fast acting on grass species, with

70-95% injury observed 1 DAT. In contrast to broadleaf weeds, neither grass showed any sign of recovery 3 DAT. Lemongrass oil provided better control (90% at 1 DAT) of large crabgrass compared to Johnsongrass (35% at 1 DAT). Cinnamon and clove oil treated grasses recovered 2 WAT, showing only 5 and 30% injury respectively in crabgrass, and

50 and 35% injury in Johnsongrass, respectively. Grasses treated with manuka oil did not recover, and control was still 95% 2 WAT. Citric acid, with or without the addition of garlic oil, and acetic acid by itself failed to control Johnsongrass or crabgrass; neither product provided over 45% control. Johnsongrass was less sensitive to the acids with or without garlic oil than was large crabgrass; 25% control was the maximum injury level observed, while large crabgrass control was as high as 45%.

Grass dry weight corresponded with the injury ratings for most of the data.

Manuka oil and clove oil performed best across species. Lemongrass oil, clove oil, and manuka oil induced the lowest dry weights in large crabgrass. Citric acid with or without garlic oil did not reduce dry weight of either species.

Table 18. The effect of natural product herbicides on hairy galinsoga, common lambsquarters, and common purslane. Percent control was visually assessed on a 0-100 scale (0 = no injury; 100 = death)1. Control (%) Dry Weight (g)

Species Product 1 DAT2 3 DAT 1 WAT 2 WAT 2 WAT Cinnamon Oil 20 cd3 20 b 40 b 35 c 0.19 d Manuka Oil 85 a 90 a 90 a 95 a 0.04 e Lemongrass Oil 30 c 50 b 55 b 20 cd 0.27 c Hairy Clove Oil 10 d 15 c 15 c 10 de 0.33 bc galinsoga Acetic Acid 5 d 10 c 5 d 10 de 0.37 b Citric Acid 60 b 80 a 90 a 75 b 0.08 e CA & GO4 10 d 10 c 10 cd 10 e 0.32 bc Control 0 0 0 0 0.51 a Cinnamon Oil 40 ab 30 b 25 bc 20 bc 0.50 a Manuka Oil 65 a 80 a 90 a 85 a 0.11 c Lemongrass Oil 10 d 10 bc 10 bc 5 bc 0.51 a

8 Common Clove Oil 30 bc 30 b 15 bc 10 bc 0.53 a

lambsquarters Acetic Acid 0 d 10 bc 10 bc 5 bc 0.47 ab Citric Acid 15 cd 20 b 30 b 30 b 0.44 ab CA & GO 5 d 0 c 5 c 0 c 0.56 a Control 0 0 0 0 0.30 b Cinnamon Oil 50 ab 75 a 80 b 65 ab 0.15 bc Manuka Oil 70 a 90 a 95 a 75 a 0.06 c Lemongrass Oil 10 c 25 bc 30 c 25 d 0.45 ab Common Clove Oil 20 bc 35 b 50 c 45 bcd 0.13 c purslane Acetic Acid 5 c 20 bc 30 c 55 bc 0.25 bc Citric Acid 10 c 15 c 35 c 30 cd 0.35 abc CA & GO 10 c 25 bc 40 c 50 bcd 0.33 abc Control 0 0 0 0 0.56 a

1 For information regarding standard deviations, refer to Appendix E.3 2 DAT represents “days after treatment”. WAT represents “week(s) after treatment”. 3 Letters represent statistical differences according to the LSD (P<0.05). 4 CA & GO: Citric Acid and Garlic Oil Mixture

In the second run of the experiment, manuka oil was the only treatment to provide consistent control across broadleaf species. Manuka oil induced 65-85% injury of the three broadleaf weeds 1 DAT. Other treatments provided similar control, varying somewhat depending on the species. In contrast to the first experiment, hairy galinsoga was relatively well controlled at the beginning of the observation period. Citric acid provided 60-90% control across the four rating times, second only to the control with manuka oil (95% 2 WAT). Cinnamon oil provided acceptable control of common purslane, with 80 and 65% control achieved respectively, 1 and 2 WAT. Only manuka oil controlled common lambsquarter 2 WAT contrasting with the result of the first experiment in which cinnamon and lemongrass oils performed similarly to manuka oil.

Overall the recovery of broadleaf weeds appeared to be less robust in the second run of the experiment, as injury increased slightly 3 DAT and again at 1 WAT for all three broadleaf weeds. However by 2 WAT, all species were showing signs of recover from essential oils with the exception of manuka.

Low dry weights of hairy galinsoga and common lambsquarters were recorded at the end of the experiment with manuka oil, corresponding with high injury ratings 2

WAT. Hairy galinsoga dry weight, following treatment with manuka oil, had a dry weight that was approximately 8% of the control, corresponding with 95% injury 2

WAT. Galinsoga treated with citric acid also had low dry weight corresponding with the high level of injury (70%) observed 2 WAT.

Table 19. The effect of natural product herbicides on Johnsongrass and large crabgrass. Percent control was visually assessed on a 0- 100 scale (0 = no injury; 100 = death)1.

Control (%) Dry Weight (g)

Species Product 1 DAT2 3 DAT 1 WAT 2 WAT 2 WAT Cinnamon Oil 30 abc3 20 d 10 c 0 c 0.39 bcd Manuka Oil 50 ab 60 a 60 a 30 a 0.20 e Lemongrass Oil 30 bc 60 ab 60 a 10 ab 0.29 de Clove Oil 50 a 50 b 20 b 5 bc 0.30 cde Johnsongrass Acetic Acid 0 d 0 e 0 d 0 c 0.58 a Citric Acid 30 c 30 c 30 b 10 b 0.43 b CA & GO4 30 bc 45 b 50 a 10 b 0.32 cde Control 0 0 0 0 0.41 bc Cinnamon Oil 60 ab 40 b 30 bc 25 b 0.70 ab

8 Manuka Oil 80 a 80 a 50 a 50 a 0.39 d

6 Lemongrass Oil 35 cd 35 b 20 c 15 b 0.52 bcd Large Clove Oil 55 bc 50 b 40 ab 30 ab 0.61 bc crabgrass Acetic Acid 5 e 5 c 5 d 0 c 0.59 bc Citric Acid 35 d 45 b 30 bc 30 ab 0.49 cd CA & GO 10 e 5 c 5 d 0 c 0.59 bc Control 0 0 0 0 0.80 a 1 For information regarding standard deviations, refer to Appendix E.4 2 DAT represents “days after treatment”. WAT represents “week(s) after treatment”. 3 Letters represent statistical differences according to the LSD (P<0.05). 4 CA & GO: Citric Acid and Garlic Oil Mixture

Natural product herbicides were uniformly less effective on grasses in the second run of the experiment. Manuka oil continued to outperform other natural products; however, control of Johnsongrass 2 WAT was only 25% in this iteration compared to

95% in the first. Cinnamon oil, manuka oil, and clove oil, provided the best control of

Johnsongrass 1 DAT in both runs.

Dry weights were lowest in grass weeds treated with manuka oil; however, other herbicides provided similar results. Lemongrass oil, clove oil, and citric acid with garlic oil resulted in similar final Johnsongrass dry weights as manuka oil. Dry weight of large crabgrass was similar when treated with manuka oil, lemongrass oil and citric acid.

Since the grass control provided by these treatments was not as good as the in the first experiment, possible explanations should be considered. Grass plant size was a little smaller in the first experiment. Smaller weeds are almost always better controlled by contact herbicides than larger weeds. Secondly, the time of year may have affected herbicide absorption. Higher light intensity and temperatures during the second experiment may have changed the quality of epicuticular wax; thereby, retarding herbicide penetration of leaf surfaces. Growth rates, metabolism, and the physio-chemical properties of the herbicide (ultimately affecting penetration) are variables affected by changes in ambient temperature (Hammerton, 1967). In general, the rate of plant injury and death increases as ambient temperature increases. Light intensity also may affect leaf morphology, and stomatal opening (Hammerton, 1967).

3.5 Conclusions

Essential oils were faster acting than acids; however, weeds showed recovery by 2

WAT. Manuka oil performed consistently well across species and runs, with injury symptoms persisting through 2 WAT. In contrast to manuka oil, cinnamon oil and lemongrass oil occasionally performed well on common purslane and common lambsquarters, but not on galinsoga or either grass species. Despite visually apparent indication of weed recovery, dry weights were often much reduced indicating less competitive growth and likely less seed production.

Weed response to herbicides differed between the first and second run of the experiment. Broadleaf control was better and grass control was worse in the second run.

Hairy galinsoga was less susceptible to these natural products than were common lambsquarters and purslane; however, control of the species was better in the second experiment. Differences in results may have been caused by differing environmental conditions in the greenhouse. The first run was performed between February 24 and April

7, while the second was performed between April 29 and July 5. Higher temperature, humidity, and light at the later period are all factors that may have affected sensitivity of weeds (Hammerton, 1967). Future work with natural product herbicides should continue to explore response of the many weed species found in organic fields and how weed growth stage modulates response. Finally research must be conducted under field conditions if natural product herbicides are to help farmers.

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Full Reference List