Effects of Soil Balancing Treatments on Soils, Vegetable Crops and Weeds in Organically

Managed Farms

THESIS

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

Graduate School of The Ohio State University

By

Andrea Leiva Soto, B.S.

Graduate Program in Horticulture and Crop Science

The Ohio State University

2018

Master’s Examination Committee:

Dr. Douglas Doohan, Advisor

Dr. Warren Dick

Dr. Matthew Kleinhenz

Dr. Steven Culman

Copyrighted by

Andrea Leiva Soto

2018

Abstract

In Ohio, many organic farmers use the term ‘balancing’ to express the rationale of using a wide variety of soil amendments to improve soil quality and plant health. Soil balancing or the base cation saturation ratio (BCSR) approach is a method first proposed more than 100 years ago that aims to achieve the ‘ideal soil’. William Albrecht in the 1970’s concluded that if saturation of the major exchangeable cations is 65-85% for Ca, 6-12% for Mg, and 2-5% for K, plant nutrition will be balanced. Research conducted by Zwickle et al. (2011) indicated that many organic farmers believe balanced soils produce higher quality crops and have diminished weed infestations compared to unbalanced soils. One of the most commonly applied mineral amendments is gypsum, a source of both calcium and sulfur. For many farmers, soil balancing includes using amendments thought to enhance soil biology and increase the soil’s capacity to store and release minerals needed by plants. Soil amendments for these purposes may include compost, manure, micro-nutrient blends, seaweed extract, molasses or inoculation of microorganisms. Combined mineral and these organic/bio-active soil products can be very costly, as much as US $1000-1250/ha in the first year. While farmers believe they are benefiting from these expenditures, there is no objective evidence to confirm their belief. I conducted on- farm studies at six locations in Northeast Ohio, with the overall goal of determining the effect of gypsum, with or without “biological stimulants”, on the soil microbial community, crop quality, weed populations, and soil chemical characteristics. Soil samples were taken 2 weeks after amendments were applied and 3 weeks after crop harvest. Soil health and biological properties were measured, including soil respiration, active carbon, protein content, microbial biomass, and complete mineral analysis. Additional soil samples were taken each spring to determine the weed seed bank. Crop foliage samples were taken mid-season for nutrient analysis, and crop quality

ii was determined after harvest. Farms differed in terms of soil texture, pH, organic matter content, and mineral content, including base saturation. However, all farms were much closer to the ideal ratios described above than anticipated. Further, differences in final soil nutrient levels, base saturation, crop and weed community effects were influenced more by the farm than by the treatments applied. After two years, soil sulfur levels were significantly higher in plots amended with gypsum. Failure to detect treatment response by other mineral amendments suggests the relatively narrow differences between the mineral blend applied to all plots and the soil balancing treatments. Cabbage and butternut squash foliage tissue tended to be deficient in K and

Mg regardless of the treatment applied. This outcome was likely caused by excessive saturation of the soils’ cation exchange capacity with Ca and/or Mg.

The only effect on marketable yield was butternut squash at one farm where the yield was higher in plots treated with gypsum and biological stimulant (BIO) compared to gypsum alone. The polar:equatorial diameter ratio of cabbage heads from plots amended with gypsum and BIO, were always closer to the ideal ratio compared to those from control plots. Active carbon

(POXC) at one farm was higher in plots treated with gypsum. The weed seed bank density was lower at, one farm in plots treated with gypsum alone compared to the control and BIO treatment. The most important finding of this research is that farmers who attempt to balance soils must pay very careful attention to not induce deficiencies of K and Mg while raising Ca saturation. These findings are also in general agreement with those of previous investigators that a soil balancing approach is costly with questionable benefits. However, this experimentation shares a shortcoming with those earlier works, in that the term of research may have been too short to demonstrate beneficial outcomes.

iii

Dedicated to my family in Chile and Perú

Especially my mother, Teresa Soto

iv Acknowledgements

Graduate school have been one of the experience I would recommend to everyone. It forces you to think pragmatically and reveals your weaknesses and strengths. I have learned that science is an endless learning pathway to follow, where peer communication is crucial. Along this journey, I am thankful for all my committee members (Drs. Warren Dick, Matthew

Kleinhenz and Steve Culman) that were always willing to help me, guide me and answer my doubts. Foremost I would like to thank to my advisor Dr. Douglas Doohan, who gave me this opportunity and taught me not only about science, but also to be humble and open minded to all probabilities. Without his support and knowledge, I would not have been able to finish this project. Also, I acknowledge my former professor in Chile, Dr. Rodrigo Figueroa for his encouragement, advise and support from the start. All are exceptional Scientifics and mentors for my future career.

I would like to thank my lab-mates in the Weed Science lab that helped me countless times during my graduate program, especially to Catherine Herms for her endless support, willingness and knowledge. Also, to all OARDC employees and faculty that make research to be enjoyable and easier. Always willing to assist students during the research process.

Lastly, but not least, I am tremendously thankful for my family. My mother Teresa Soto who taught me to be honest, just and grateful to God every day. She has been my guidance, my friend, my role model and the most valuable person in my life. Equally I thank to my father

Carlos Leiva, my brothers Rodrigo and Marcos Leiva, and my sisters in law Carla González and

Rocío Murillo. They all have been my support along the years of study. Giving me love, encouragement and the seven nieces and nephews to enjoy and love unconditionally everyday. I also thank to Juan Piñeiro for all his help, love and support along this program.

v Vita

March 11, 1988 ...... Born, Santiago, Chile. March 2014 ...... B.S. Agronomy Engineer, Pontificia Universidad Católica de Chile. August 2015 to present ...... Graduate Research Associate, Department of Horticulture and crop Science, The Ohio State University

Fields of Study Major Field: Horticulture and Crop Science

vi Table of Contents Abstract ...... ii

Dedication………………………………………………………………………………………..iv

Acknowledgements ...... v

Vita ...... vi

List of Tables ...... viii

List of Figures ...... x

Effects of Soil Balancing Treatments on Soils, Vegetable Crops and Weeds in Organically Managed Farms ...... 1 Introduction ...... 1 Materials & Methods ...... 12 Description of Sites, Treatments and Field Experimental Design ...... 12 Soil Sampling Timing and Procedures ...... 14 Harvest, Measurements and Data Collection ...... 15 Soil Biological Tests Methods and Procedures ...... 17 Data Analysis ...... 19 Results and Discussion ...... 19 Effect of soil balancing amendments on soil chemistry ...... 19 Effect of soil balancing amendments on cabbage and squash nutrient levels, yield and quality ...... 25 Cabbage – 2016 Season...... 25 Butternut squash – 2017 Season...... 27 Effect of soil balancing amendments on soil biology on vegetable farms ...... 28 Effect of soil balancing amendments on weed communities ...... 30 Tables and Graphs ...... 32 Conclusions ...... 73 Bibliography ...... 77 Appendices: Tables ...... 84

vii List of Tables

Table 1. Agricultural practices at each farm (A-F) during the study period...... 32 Table 2. Analysis of soil samples collected at the block level of each farm in Spring of 2016...... 33 Table 3. Soil amendments applied according to treatment and including individual ingredients...... 34 Table 4. Mineral composition of each treatment, including analysis of the mineral blend applied to all plots...... 35 Table 5. Average nutrient removal for cabbage...... 35 Table 6. Cabbage total and marketable yield and cabbage head weight, polar:equatorial diameter ratio and % moisture treatment averages with standard error in parenthesis for Fall 2016...... 67 Table 7. Butternut squash total and marketable yield, fruit number, fruit weight, length, color and brix treatment average with standard error in parenthesis for Fall 2017...... 68 Table 8. Organic matter, microbial biomass, active carbon, total mineralized carbon and protein treatment average with standard error in parenthesis, following harvest in the Fall of 2016, after amendments application in the Spring of 2017, and following harvest in the Fall of 2017...... 69 Table 9. Weeds present in soil weed seed bank samples, collected in spring 2016 at each farm...... 70 Table 10. Average soil calcium, magnesium and potassium content with standard errors in parenthesis. Soil samples were collected in Spring 2016 prior to treatment, and after harvest in fall of 2016 and 2017...... 84 Table 11. Average soil pH, CEC, phosphorus, and sulfur content with standard error in parenthesis. Soil samples were collected in Spring 2016 prior to treatment, and after harvest in fall of 2016 and 2017...... 85 Table 12. Average soil boron, copper, iron, manganese and zinc content with standard error in parenthesis. Soil samples were collected in Spring 2016 prior to treatment, and after harvest in fall of 2016 and 2017...... 86

viii Table 13. Average nitrogen, calcium, magnesium and potassium content in cabbage foliage (cupping stage) and whole heads, with standard error in parentheses, after harvest in Fall 2016...... 87 Table 14. Average phosphorus, sulfur, boron and copper content in cabbage foliage (cupping stage) and whole heads, with standard error in parentheses, after harvest in Fall 2016...... 88 Table 15. Average iron, manganese, zinc and sodium content in cabbage foliage (cupping stage) and whole heads, with standard error in parentheses, after harvest in Fall 2016...... 89 Table 16. Average nitrogen, calcium, magnesium and potassium content in butternut squash foliage (fruiting stage) and fruit (mesocarp plus exocarp), with standard error in parentheses, after harvest in Fall 2016...... 90 Table 17. Average phosphorus, sulfur, boron and copper content in butternut squash foliage (fruiting stage) and fruit (mesocarp plus exocarp), with standard error in parentheses, after harvest in Fall 2016...... 91 Table 18. Average iron, manganese, zinc and sodium content in butternut squash foliage (fruiting stage) and fruit (mesocarp plus exocarp), with standard error in parentheses, after harvest in Fall 2016...... 92

ix List of Figure

Figure 1. Baseline concentration/ base saturation of calcium in the soil in Spring 2016 prior to treatment, and after harvest in fall of 2016 and 2017...... 38 Figure 2. Baseline concentration/ base saturation of magnesium in the soil in Spring 2016 prior to treatment, and after harvest in fall of 2016 and 2017...... 38 Figure 3. Baseline concentration/ base saturation of potassium in the soil in Spring 2016 prior to treatment, and after harvest in fall of 2016 and 2017...... 40 Figure 4. Baseline concentration of phosphorus in the soil in Spring 2016 prior to treatment, and after harvest in fall of 2016 and 2017...... 42 Figure 5. Baseline concentration of sulfur in the soil in Spring 2016 prior to treatment, and after harvest in fall of 2016 and 2017...... 43 Figure 6. Baseline concentration of boron in the soil in Spring 2016 prior to treatment, and after harvest in fall of 2016 and 2017...... 44 Figure 8. Baseline concentration of iron in the soil in Spring 2016 prior to treatment, and after harvest in fall of 2016 and 2017...... 46 Figure 9. Baseline concentration of manganese in the soil in Spring 2016 prior to treatment, and after harvest in fall of 2016 and 2017...... 47 Figure 10. Baseline concentration of zinc in the soil in Spring 2016 prior to treatment, and after harvest in fall of 2016 and 2017...... 48 Figure 11. Cabbage leaf nitrogen concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017...... 49 Figure 12. Cabbage leaf calcium concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017...... 50 Figure 13. Cabbage leaf magnesium concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017...... 51 Figure 14. Cabbage leaf potassium concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017...... 52 Figure 15. Cabbage leaf phosphorus concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017...... 53

x Figure 17. Cabbage leaf boron concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017...... 55 Figure 18. Cabbage leaf copper concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017...... 56 Figure 19. Cabbage leaf iron concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017...... 57 Figure 20. Cabbage leaf manganese concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017...... 58 Figure 21. Cabbage leaf zinc concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017...... 59 Figure 22. Cabbage leaf sodium concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017...... 60 Figure 23. Butternut squash leaf nitrogen and calcium concentrations at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017...... 61 Figure 24. Butternut squash leaf magnesium and potassium concentrations at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017...... 62 Figure 25. Butternut squash leaf phosphorus and sulfur concentrations at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017...... 63 Figure 26. Butternut squash leaf boron and copper concentrations at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017...... 64 Figure 27. Butternut squash leaf iron and manganese concentrations at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017...... 65 Figure 28. Butternut squash leaf zinc and sodium concentrations at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017...... 66 Figure 29. Weeds field counts at the same plot...... 71 Figure 30. Baseline weed density per farm, from Spring 2016 soil samples...... 71 Figure 31. Baseline density of broadleaf and grass weeds per farm, from Spring 2016 soil samples...... 72 Figure 32. Treatment effect on weed density at Farm B, based on soil weed seed bank samples collected in Spring 2017...... 72

xi Effects of Soil Balancing Treatments on Soils, Vegetable Crops and Weeds in Organically Managed Farms

Introduction

Achieving an ‘ideal soil’ by the practice of balancing mineral nutrients was proposed more than 100 years ago. One of the main proponents was Oscar Carl Benedict Löw, a former student of Justus von Liebig (notable German chemist who popularized the Law of the

Minimum) at the University of Munich, Germany (Oesper, 1930). According to Löw (1901),

“the plants thrive best when the ratio of lime to magnesia does not pass certain limits. Too little magnesia in relation to lime may retard development, while too much magnesia in relation to lime may injure the crop still more”. As described in his book, “The Relation of Lime and

Magnesia to Plant Growth”, Löw reported the observations of farmers in New Jersey (Löw,

1901), who noticed that limestone from Belvidere quarries compared to limestone from Oxford quarries, fairly close townships, showed injurious or beneficial effects when used in the same field. Samples were taken to the USDA for analysis, and “injurious limestone” had 38-42% magnesium carbonate while “beneficial limestone” contained just 1% magnesium carbonate.

The more formal concept of soil balancing, referred to technically as base cation saturation ratio (BCSR), was first proposed in 1945 by Bear and his coworkers who conducted research at Rutgers University in New Jersey. Their research lasted for almost eight years, working with alfalfa (Medicago sativa) in soils with the same concentration of exchangeable magnesium (Mg) and hydrogen (H), and different concentrations of exchangeable potassium (K) and calcium (Ca.). The aim of their work was to determine how best to decrease the overconsumption of K on New Jersey soils (Brown et al., 1987; Chaganti & Culman, 2017).

Hunter et al. (1943) stated that alfalfa yield was greatly depressed when the level of K in the

1 plant was 1%, Ca exceeded 2%, or the Ca-K ratio was 4:1 or greater (Wallace & Bear, 1949).

These observations led to the conclusion that the “ideal soil” would have 65% Ca, 10% Mg, 5%

K and 20% H on the soil cation exchange sites (Bear et al., 1945; Chaganti & Culman, 2017;

McLean, 1977; Rehm, 1994). Later, Graham (1959), who wrote a bulletin explaining the theory and methods of soil testing, proposed a modified ideal base saturation of 65-85% Ca, 6-12% Mg and 2-5% K. Eventually, William Albrecht, a colleague of Graham, emphasized the importance of having a high saturation percentage of Ca and established that as one of the main principles for the “balancing soil” concept (Kopittke & Menzies, 2007).

Albrecht, president of the Soil Science Society of America (SSSA) from 1938-1939, was a strong believer that animals had protein and calcium deficiencies due to low quality crops

(Albrecht, 1945). He noticed that in high rainfall regions of the United States (southern states east of the Mississippi River), the soils were greatly weathered and their fertility was leached. In contrast, regions with moderate rainfall (western and central states) had black soils that were not highly weathered and leached of mineral content. Those western and central states were known for grain production and supplying most of the animal protein in the country, and historically for being the home of the American bison. He observed that highly weathered, acidic soils had a narrow ratio of calcium to potassium, and similar ratios were reflected in crops grown in those soils. He associated those soil characteristics with highly carbonaceous, less protein-rich vegetation, and livestock requiring protein supplements. In contrast, more calcareous soils had a higher ratio of calcium to potassium and produced vegetation with higher concentrations of minerals, proteins and vitamins (Albrecht, 1948). Still later, he stated that plant nutrition would be balanced with soil base saturation of: 10% H, 60-75% Ca, 10- 20% Mg, 2-5% K, 0.5-5% Na, and other cations at a total of 5%.

2 Soil balancing has been quite controversial among and mostly unaccepted by the scientific community. After Bear and his coworkers defined the “ideal soil”, several researchers attempted to achieve “balanced soil” with the aim of correlating it with an increase in yield.

Chaganti & Culman (2017) provide a recent review of the major studies that have been completed, along with the results of a survey quantifying the perceptions and beliefs of soil scientists. Despite decades of somewhat sporadic efforts, researchers have not been able to demonstrate that the use of the proposed cation ratios per se, has a positive impact on crop yield.

Moreover, the soil balancing practice is known to be an expensive way to farm compared to soil fertilization strategies that aim to correct deficiencies in specific essential nutrients (Chaganti &

Culman, 2017; Eckert, 1987; Kopittke & Menzies, 2007). Beyond the BCSR, there are two prevalent methods to interpret soil test and generate fertilization recommendations (Chaganti &

Culman, 2017). One, the sufficient level of available nutrient (SLAN) method, is based on a non- response of a crop to fertilization when a critical levels of each nutrient is exceeded. The other, is the build-up and maintenance method, were nutrients levels are built up above critical levels and maintained over time. One of the main reasons why most soil scientists have responded negatively to the BCSR concept, is the failure to demonstrate beneficial yield effects and the high costs associated with its practice. After completing a 6-year field project in a corn-soybean- wheat-alfalfa rotation examining the relationship between yield and ratios of Ca/Mg and Mg/K

McLean et al. (1983) concluded that a favorable BCSR did not exist. McLean, who at one time worked with Albrecht, concluded that there is no “ideal” basic cation saturation ratio or range for crops on the whole. He went on to say that crop response was an adequate test to determine the sufficient level of each basic cations; while excessive concentrations should be avoided (Eckert

& McLean, 1981). Chaganti & Culman (2017) noted that overall there is a persistent lack of

3 understanding of the relationship between soil balancing and nutrient interactions, and that the approach merits further investigation. Furthermore, it is noteworthy that Eckert (1987) mentioned “it is unfortunate that Bear did not express the concept as “reasonable saturations” instead of ideal ratios…because the approach of addressing individual saturations rather than ratios may lead to reasonable soil test interpretations”.

Despite the skepticism of academic soil scientists, the soil balancing approach grounded in the BCSR hypothesis has a history of being followed by many farmers, private soil testing laboratories, crop consultants and agronomists (Chaganti & Culman, 2017; Eckert, 1987;

McLean, 1977). Kopittke & Menzies (2007) mentioned that 90-95% of the turf industry in

Australia uses soil balancing concepts to interpret soil data. Acres USA, a national organization founded by Charles Walters, is one the main promoters of the principles of soil balancing among farmers. Walters published several volumes of Albrecht work, which have been the main cores for the eco-agriculture farming model the organization promotes (https://www.acresusa.com).

Persistence of the soil balancing concept amongst farmers may be partially related to their decision-making process. Zwickle et al. (2011) conducted in-depth interviews with 30 certified organic farmers in Ohio and Indiana. The authors recognized that organic farmers (the single largest cohort amongst soil balancing practitioners) often develop and rely upon intuitive, experience-based thought processes, and decision-making “rules of thumb”. Their heuristic decision-making process contrasts with the more deliberative, analytical thought process familiar to scientists. The implication is that farmers will base their decisions on personal experience and local practices, instead of university research and extension information. A crucial difference between researchers and farmers is that decisions of the latter group have financial consequences

4 that affect them directly; and only practices that “work”, in their assessment, are continued

(personal communication, BCSR practitioner).

Contemporary proponents of BCSR state that soil balancing is an effective tool to help manage weeds and other pests, in addition to improving soil quality and crop yield (Chaganti &

Culman, 2017). Respecting farmer perceptions about weeds, Zwickle et al. (2011) found that

55% of the organic farmers interviewed mentioned that different weed species indicate the presence or lack of minerals in the soil. Statements such as “the more you get to know about weeds or foxtail or thistle it’s probably because there’s something not quite in balance with your soil” or “adding calcium to the soil loosens it and we’ve seen a decline in dandelion populations”, were frequent. Research on the subject is scarce. Kelling et al. (1996) mentioned a study conducted in Wisconsin, where Ca:Mg ratios were adjusted on Dakota sandy loam and

Withee silt loam soils for corn and alfalfa; measuring several variables including weediness.

Results showed a positive correlation between the grass population and the exchangeable Mg levels; while a negative correlation with Ca:Mg ratio and exchangeable Ca. These results suggested some validation that high exchangeable Mg and low exchangeable Ca, could increase grass populations (Kelling et al., 1996). In contrast, a study conducted at Virginia and eastern

Tennessee in 1998, in which low- Ca and high-Ca limestone treatments were applied to five organic vegetable farms resulted in no measurable change in weed cover (Schonbeck, 2000).

However, the author observed increased Ca levels in tomato fruits where high-Calcium treatments had been applied. Similarly, Linder (2015) conducted field experiments applying amendments commonly used by soil balancing practitioners (dolomitic limestone, gypsum and customized blends) measuring, among other variables, field weed populations and weed seed bank for two consecutive years. Linder’s results showed no treatment effects in the weed

5 community in both field and seed bank samples. However, a balanced soil was not achieved according to recommended values in the two years of research. Wilson et al. (2008) provided additional relevant insight into how farmers perceive weeds, how they spread, and how they are best controlled. In interviews with 30 Ohio grain and produce farmers respecting weed management perceptions, 70% stated that weeds pressure increased when standing water was present. It is well recognized that Ca applied to the soil profile has the ability to flocculate clay particles, create micro-aggregates, improve water infiltration/retention and aeration of the soil, and facilitate root growth (Mckibben, 2012; Wallace, 1994). In contrast, Mg when hydrated can

“cement” clay particles together leading to surface crusts and tight soils that resist water infiltration; and even exclude Ca (Zimmer, 2017). Considering that an optimum ratio of Ca and

Mg should also optimize water infiltration and thereby reduce ponding of water and anoxic root- zone soil conditions, it is reasonable that farmers should experience reduced weed pressure when soils are balanced. The findings of both Zwickle et al. and Wilson et al., provide a potential theoretical framework in support of the perception that weeds are indicators of imbalances in soil cations.

At the simplest level, a farmer can balance soils according to the BCSR approach by applying limestone and/or gypsum in sufficient quantities to increase Ca base-saturation to 65% or higher, while maintaining or adjusting for appropriate pH, Mg and K concentrations. Calcium is available as high-calcium lime, dolomitic lime, and gypsum. Limestone is used when the soil pH needs to be increased. Dolomite can also increase soil pH, but supplies Mg as well as Ca. In contrast, gypsum will add calcium and sulfur, but has no effect on soil pH (McKibben, 2012).

McKibben, an Ohio- based private agronomist, recommends the following approach: “First, it is necessary to have a pH between 6.2-6.4. Then, base saturation of Ca and Mg needs to be 65%

6 and 15%, respectively. P should be at 125 ppm, and K at 4% of base saturation. Boron (B) should be between 0.6 – 1 ppm, and foliar application of trace minerals may be needed. Trace minerals and P are not specifically addressed in the BCSR approach.”.

Gypsum is one of the most common amendments use by soil balancing practitioners. As

Chen and Dick (2011) mention, it has been use in agricultural practices for more than 250 years and it is one of the first mined fertilizers used in United States agriculture. It provides readily available Ca and S as sulfate (SO4) ions, both of which are essential for plant growth, and has the ability to impact the soils’ physical and chemical properties. Physically, gypsum promotes soil aggregation which preventing dispersion of soil particles. It reduces soil erosion and increases water infiltration, and chemically can mitigate aluminum toxicity and subsoil acidity (Chen &

Dick, 2011; Chen et al., 2010; Oster, 1982; Zhang, Liu, & Lal, 2016). It is thought that Calcium promotes soil aggregation by forming a cation bridge between clay particles and organic matter

(Inagaki et al., 2016; Oades, 1988). Calcium promotes aggregate stability by replacing disruptive agents like Mg and sodium (Na) cations (Bronick & Lal, 2005).

Through the interactions of our research team with members of Green Field Farms

Cooperative, a central Ohio cooperative of Plain and Mennonite organic farmers, we have learned that soil balancing implies, for their members, much more than the use of Ca rich soil amendments. As described by a member of the cooperative, soil balancing recognizes three main axes in soil fertility. First, the soil mineral status, which is dictated by the individual saturation ranges of the BCSR approach. Second, soil biology, addressed by the application of microbial stimulants and inoculants. Finally, the soil’s physical structure, addressed primarily by growing cover crops and amending with compost. When all three axes are balanced, fewer weeds and reduced pest pressure are observed. Soil cation exchange capacity (CEC) increases and overall

7 soil quality improves. These changes are accompanied by improvements in crop yield and quality, including an increase in vegetable crop shelf life (personal communication). Most organic farmers stress the importance and practice of building soil organic matter through application of carbon-sourced amendments together with minerals. Zimmer (2017) outlined three purposes of this practice. One is to maintain soluble nutrients in forms available to plants for a longer period, by binding the fertilizer to a complex carbon source (humates or compost) with exchangeable sites. Secondly, a carbon source like molasses can feed bacteria, thus increasing bacterial activity and nutrient cycling. Third, carbon sources like molasses contains potassium, trace elements and secondary compounds that may help mobilize the plant´s own protective compounds against bacteria, fungus and/or insects.

Crop quality is a highly complex trait mainly controlled by genetic and physiological factors that vary depending on the species, cultivar, plant organ and tissues (Marschner &

Marschner, 2012). Quality in plants with the same genetics can be modified by natural exogenous factors (climate or pest pressure), and by anthropogenic factors, including fertilization, soil cultivation, harvest and processing (Martínez-Ballesta et al., 2010). The authors state, that quality depends on the fruits and vegetables physical characteristics, that influence marketable yields; and on the chemical composition, that regulates the nutritional and sensory compounds. Crop quality can be improved when high concentrations of essential nutrients, amino acids, lipids, secondary compounds and carbohydrates, are present. Research has shown the relevant influence of plant nutrition on fruit quality. For instance, increasing K supply to potato plants resulted in lower starch content, mainly due a lower osmotic potential rather than a decrease of starch synthesis (Beringer et al., 1983). Similarly, Ca is frequently associated with fruit firmness and delay in softening during storage (Marschner & Marschner, 2012; Sams,

8 1999). Ca has the ability to cross-link with pectin, thereby, strengthening plant cell wall (Sams,

1999). Boron (B) deficiencies can increase bitterness and fiber formation in broccoli (Petracek &

Sams, 1987); and brown head, watercore tissues and heart rot in cauliflower (Marschner &

Marschner, 2012). Nitrogen (N) and Mg fertilization can modify anthocyanin, chlorophyll and carotenoids levels in fruit and vegetables to achieve different appealing colors (Marschner &

Marschner, 2012; Reay et al. 1998).

Soil biology plays a crucial role in soil fertility and consequently in crop quality. Soil microorganisms influence plant nutrition by affecting root growth, plant physiology and nutrient availability and uptake (Marschner & Marschner, 2012). Microorganisms are capable of mineralizing nutrients for plant uptake, fixing atmospheric nitrogen for plant supply, mobilizing minerals in the soil, and protecting plants from pathogenic microorganisms (Lambers et al.,

2009). For instance, a manganese (Mn)-dependent defense reaction of plants to root pathogens is to produce phenolic and lignin-rich compounds to limit fungal spread in the roots. As a consequence, available Mn in the soil is crucial; and bacteria from Fluorescent Pseudomonas species, that increase available Mn, are actually abundant in the rhizosphere of Mn-efficient wheat (Rengel et al., 1993; Rengel, 1997). Furthermore, the root pathogen Gaeumannomyces graminis var. tritici strongly reduces Mn availabilility, which diminish the defense reaction and facilitate roots infection (Marschner et al., 1991). Microorganisms also contribute to plant nutrition by promoting carbonic acid formation through the reaction of released CO2 with soil moisture. Consequently, carbonic acid will erode parent rock materials into soil particles and mineral chelates for plant uptake will be formed (Brunetti, 2014).

The importance of soil biology has inspired the development of products containing live and dormant beneficial microorganisms. These developments are aimed to improve the plant

9 nutrient status, increase plant growth promoting compounds and boost the plant response to abiotic stresses (Laudick et al., 2017). However, there are constraints on their effectiveness in field conditions. Microorganisms need to adapt to the new soil characteristics and compete with the local microorganisms for organic carbon sources (Gyaneshwar et al., 2002; Marschner &

Marschner, 2012; Postma et al., 1990). Competition for available microhabitats is likely more difficult for foreign species. Zimmer (2017) mentioned that modern inoculants and bio-stimulant products contain more competitive strains. Sometimes trace minerals, nitrogen, sugars, enzymes, humic acids or organic acids are added to ensure their survival.

Methods to identify the soil physical, chemical and biological properties are quite diverse and overall commonly used. Yet, to accurately assess the soil fertility status of a particular field, is a difficult task. It requires selection of a group of measurements to predict the soil nutrients availability, soil mineralization status or soil organic matter accumulation, and then evaluate/ recommend previous and future management. As Dick and Culman (2016) mentioned, the goal of producers’ is to achieve the highest crop yields and manage the soil for long-term sustainability. They indicated that fertility tests that are biologically based may be a better reflection of the crop´s field experience. Some of the biologically based methods include the use of enzymes to better measure the soil pH, redox potential or specific nutrient availability; to quantify different carbon fractions (in addition to the commonly measured organic matter); the use of artificial roots (that mimic natural roots by collecting inorganic and organic nutrient from the soil) during a growing season; or to quantify active fractions of other elements besides carbon (Dick & Culman, 2016).

The following microbiological assays were used in this research. Microbial Biomass assay quantifies the total biomass of microorganisms present in the soil. This statistic represents

10 the soil living component highly related to mineralization. Thus Microbial Biomass acts as an efficient indicator of changes in the soil conditions (Brookes, 2001; Brookes et al., 2008; De

Araujo, 2010). The Permanganase Oxidazable Carbon test (POXC), is based on the chemical oxidation of organic matter as a measure of the more active or labile C pool closely associated with nutrient cycling. POXC is thought to indicate if a management practice induced changes in the organic matter (Culman et al., 2012; Hurisso et al., 2016). Mineralizable carbon, also known as the soil respiration test, measures release of CO2 after dry soils are rewetted and aerobically incubated. Mineralizable carbon is correlated to nutrient mineralization, particulate organic matter and the microbial biomass C assay previously described, indicating management-induced changes in the soil total C (Hurisso et al., 2016). The soil protein extraction test is based on the extraction the putative glycoprotein produced by active growing hyphae of arbuscular mycorrhizal fungi (AMF). Such glycoproteins are known to improve soil aggregation and be part of the stable organic matter (Wright & Upadhyaya, 1996). The modified assay has been further linked with the labile organic nitrogen (N) bound to the soil organic matter, and with N storage and release (Moebius-Clune, 2016).

For the organic farmers who have adopted this relatively input-intensive approach to balancing, the prescribed amendments can be costly, as much as US $1000-1250/ha in the first year (Amendment vendor, Personal Communication). In addition to the amendments to the soil, foliar fertilizer- and stimulant-sprays are often used during the growing season, adding more expense. The farmers we have spoken with believe they are benefiting from these expenditures, but there is no objective confirmatory evidence. The objectives of this research were to monitor the impact of gypsum applications, with and without a microbial stimulant/inoculant, on a range

11 of soil chemical, physical and biological variables, crop nutrient levels, yield and quality, and soil weed seed banks.

Materials & Methods

Description of Sites, Treatments and Field Experimental Design

Field studies were established in 2016 at six farms located near Wooster, OH, USA

(40º48´N, 81º 56´W). Farms were certified organic for vegetable production and growers were members of the Ohio Plain Community. During the growing season, the accumulative precipitation from April 1st to the end of October in the region was ca. 432 mm in 2016 and ca.

711 mm in 2017. The minimum air temperature recorded was in April for both years, ca. -8ºC

(2016) and ca. -3ºC (2017). The maximum air temperature recorded was ca. 35ºC in July 2016 and 33ºC in September 2017. Each farm maintained their own regular agricultural practices, including cover crop use, tillage frequency and manure applications (Table 1).

Soil samples were taken in Spring of 2016 to establish the soil baseline characteristics for each farm and to guide the formulation of a mineral fertilizer blend (MB) application to be used on all farms (Table 2). Samples were analyzed by Spectrum Analytics Inc. (1087 Jamison Rd

NW, Washington Court House, OH 43160).

Relying upon the results from the baseline soil samples, treatments and MB formulations were planned in consultation with two practitioners experienced in using the BCSR approach.

The same treatments, with modifications as outlined in Table 3, were applied to identical plot layouts on each farm both years. The experimental design was a randomized complete block.

Plots had an average area of 54 m2, and treatments were replicated four times. All plots received a mineral fertilizer blend (MB) at the same time that the soil balancing treatments were applied.

12 The four treatments were: a control that received only the MB application, a gypsum treatment

(GYP), “biological stimulants” treatment (BIO), and their combination (GYP+BIO) (Table 3).

BIO, as described in Table 3., included a microbial inoculant and a number of naturally-sourced micronutrients similar to those that the cooperating farmers customarily used to enhance and stimulate biological activity. To verify he presence of microbes, both products were cultured on a

Trypticase Soy Agar and Martin’s Rose Bengal medias in an incubator at 25 ºC (Vieira & Nahas,

2005). Many colony forming units (CFU) of apparently diverse bacteria were cultured from the

Bio-Aid inoculant. Incubation quantified 6.84 x 10 8 CFU g of product-1. In contrast, only 5.08 x

10 4 bacteria CFU g of product-1 were cultured from the stimulant Florastim. Additionally, 2833 fungal CFUs g of product-1 formed following incubation of the Bio-Aid product. The bacteria and fungi associated with Bio-Aid were not identified. The MB was sent for mineral analysis and the results are described in Table 4.

In 2016, five of six farms planted cabbage “Grand Vantage” or “Bronco”. Acorn squash

“Autumn Delight” was planted on the remaining farm. In 2017, four farms planted butternut squash “Quantum”, one farm green bell pepper “Aristotle” and the other corn “Doeblers 605”.

Treatments were spread uniformly by hand across each plot, about three days before transplanting. The farmer incorporated the amendments by shallow harrowing before transplanting. Cabbage plant spacing was 0.38 m between plants and 1.8 m between rows, planted in two rows per bed. Butternut squash spacing was 0.61 m between plants and 1.8 m between rows. Bell pepper transplants were 0.61 m apart and 1.8 m spacing between rows, planted in two rows per bed. While corn plants were direct seeded with a spacing of 0.7 m between rows. Only data for the main crops from each year (Cabbage in 2016, Butternut Squash

13 in 2017) are included in the results. Butternut squash fruit and cabbage head mineral analysis was not discussed, but data can be found in the Appendix section.

Soil Sampling Timing and Procedures

Additional soil samples were collected each growing season to quantify the soil weed- seed bank, and to assess effects of soil amendments on soil chemistry and biology. For the weed seed bank, eight 20-cm deep, 2.54-cm diameter cores, were taken from each plot in early spring before germination began in the field and before the amendments were applied. Samples were taken from each block in 2016, and from each plot in 2017. Individual cores were randomly located within the plots. Sampling in early spring, before seed germination occurs, has been shown to provide a reliable estimate of weed community speciation and density reflecting contributions made to the weed seed bank during the previous growing season minus losses due to predation and seed decay. It also provides a measure of the potential weed community for the current growing season. Cores were placed in plastic bags and were stored in a cold room at 4ºC immediately after returning from the field until further processing. Seed bank samples were processed by sieving each through a 6-mm screen. Samples were partially air-dried if necessary to allow for sieving. Each sample was spread in a rectangular tray (20 by 12 by 10 cm) lined with capillary matting and then placed on a flood table lined with capillary matting in a greenhouse.

The greenhouse was maintained at 20ºC, 60% RH, and only natural daylight was used. Trays were watered every other day from the bottom to minimize surface crust formation. Every 2-3 weeks, emerged weed seedlings were identified, counted and removed. Trays of soil were kept in the greenhouse until weed emergence ceased. At that point, moist soil samples were returned to plastic bags, and stored in the cooler for an additional two months at 8ºC to induce seed

14 stratification. After the stratification treatment, each sample was re-sieved, spread in the same trays and placed in greenhouse conditions as described previously, with the purpose of completing a second run, and preferably a third. As of this writing, two runs have been completed on samples collected in 2016, and one run on samples collected in 2017.

Soil samples for mineral analysis, active carbon, protein content, respiration and microbial biomass, were a composite of ten cores (0-20 cm depth) removed randomly from the center of each plot. Samples were collected three times; 20 days after harvest in fall 2016, 14 days after treatment application in spring 2017 and 20-30 days after harvest in fall 2017. Samples were transported from the farm to the laboratory in a cooler with ice to avoid an increase in microbial activity due to temperature and light. Promptly upon arriving at the laboratory, an aliquot of each sample was placed at -20ºC for future microbial biomass analysis. After the aliquot was taken, the rest of each sample was air dried before sieving through a 6-mm screen.

Once samples were completely dry, 100 g sub-samples were sent to Spectrum Analytics Inc. for mineral analysis, and 200 g were ground for further soil active carbon, protein content and respiration analysis.

Harvest, Measurements and Data Collection

Cabbage leaf tissue samples were collected at the cupping stage 60 days after planting

(DAP) in 2016. In 2017, butternut squash leaf samples were collected 70 DAP at the fruiting stage. Leaf samples were air dried and sent for mineral analysis to Spectrum Analytics Inc.

Harvest time was based on the farmers’ schedules. Cabbage and butternut squash were each harvested once from the plots. In 2016, 10 adjacent cabbage heads were collected (80-90 DAP) from the center rows of each plot. Wrapper leaves were removed, cabbages were weighed, and

15 then heads were separated in two groups, classifying them as marketable and unmarketable, according to the quality control required by the farmers’ vendor. The classification criteria considered the presence of insect feeding marks, head firmness and head diameter. Each marketable and unmarketable group was weighed, and five heads were randomly selected from the marketable group for the following measurements adapted from methods of Radovich et al.

(2005). First, four leaves were removed, followed by individual measurements of head weight and head equatorial and polar diameters. Two heads were sent to Spectrum Analytics Inc. for mineral analysis; while two heads from the same plot proceeded to the next procedure. Heads were cut longitudinally, and a representative slice was carefully cut, wrapped in aluminum foil, placed in liquid nitrogen and saved for future analysis. The other half was weighed and placed in the air drier at 60 ºC for one week for head moisture content measurement.

In 2017, five adjacent butternut squash plants were marked and carefully harvested (100

DAP) from the center row of each plot. Fruits were separated into marketable and unmarketable groups according to the farmers’ vendor requirements. The criteria required freedom from any crack (split open or exposure of the flesh) and insect feeding marks. Marketable fruits were further sorted by length into three groups that were then counted and weighed (long fruits are more valuable than short fruits). Five ripe fruits were then randomly selected from the largest group for the measurement of individual fruit weight, basal and polar diameter, pulp color and dry weight. The following measurements adapted from Bumgarner & Kleinhenz (2012). For pulp color and dry weight, squash was cut transversally between the top and the bottom of the neck.

The mesocarp (exposed edible flesh) color was immediately measured with a!Minolta CR-400

Colorimeter (Minolta Camera Co., Ltd., Ramsey, NJ). A 1-cm slice was then removed and halved. One half was weighed and placed in the air drier at 60 ºC for two weeks and sent to

16 Spectrum Analytics Inc. for mineral analysis. The remaining half was immediately peeled and diced-cut. Diced-cuts from five fruits were then mixed and stored in plastic bags at -20ºC for future brix content measurements. Brix content was measured with a handheld digital refractometer Atago PAL-1TM.

Soil Biological Tests Methods and Procedures

A series of analyses were conducted to measure the impact of treatments on the soil biology of each farm.

Microbial Biomass Carbon. Microbial biomass was determined by the microwave irradiation and K2SO4 extraction method (Islam & Weil, 1998). As described by Chen et al.

(2013), a 50-ml centrifuge tube was filled with 10 g of oven-dried equivalent (ODE) field- moisture soil, and adjusted to 80% water-filled porosity with deionized water. A total of 800 J of microwave energy was applied g-1 of ODE soil. Heat pockets within tubes of the moist soil samples were minimized by mixing the samples after an initial application of 400 J g-1 of soil.

Microwaved and control samples received 25 ml 0.5 M K2SO4 (pH 7.0) and were extracted by shaking horizontally for 60 min at 250 rpm. Concentration of C in the soil-free filtrate extracts was measured by the rapid oxidation spectrophotometric method (Islam & Weil, 1998).

Soil Active Carbon. Soil active carbon was determined by the Permanganate Oxidizable

Carbon method first proposed by Weil et al. (2003) and modified by Culman et al. (2012).

Samples (2.5 g) of air-dried soil were weighed into 50-ml centrifuge tubes. Eighteen ml of deionized water and 2 ml of 0.2 M KMnO4 stock solution were added. Tubes of soil and reagents were placed on an oscillation shaker at 240 oscillations m-1for 2 min and allowed to

17 settle for 10 min. After settling, 0.5 ml of supernatant were transferred and mixed with 49.5 ml of deionized water in a 50 ml centrifuge tube. A 96-well plate containing standard solutions

-1 (0.00005, 0.0001, 0.00015, and 0.0002 mol L KMnO4), blanks of deionized water, a soil standard and a laboratory reference standard solution were loaded with 200 µL of each sample.

All standards were replicated. Absorbance was read with a SpectraMax M5 using Softmax Pro software (Molecular Devices, Sunnyvale, CA) at 550 nm.

Soil Protein. Soil protein was determined by the “easily extractable glomalin” fraction extraction method of Wright & Upadhyaya (1996). A modification of the method, described in

Moebius-Clune (2016), 3 g samples of air dried soil were placed in an autoclavable glass screw- top tube. Twenty-four ml of the extracting solution (0.02 mol L–1 sodium citrate, pH 7) were added to the tube and shaken for 5 min at 180 rpm. Tubes with soil and extracting solution were then autoclaved for 30 min at 121°C, and centrifuged for 15 min at 3100 × g. Once samples cooled, 2 ml of supernatant were removed and placed in microcentrifuge tubes. Contents were then centrifuged for three minutes at 10,000 g. Protein concentration in the supernatant was quantified by the Bradford assay (Bio-Rad, Hercules, CA) as described in Wright and

Upadhyaya (1996, 1998). Ten µL of the supernatant sample was placed into individual wells in a

96-well microliter plate for the standard colorimetric protein quantification assay (Thermo Pierce

BCA Protein Assay). Each well was then filled with 200 µL of undiluted Bradford reagent and promptly sealed. After 60 min of incubation at 60ºC, and a 60 min resting period, the plate was unsealed and sample absorbance was read with a BioTek spectrophotometric plate reader. The protein concentration in each sample extract was then calculated by comparing the absorbance values to a standard curve of 0-500 µg mL-1 bovine serum albumin (BSA) in 0.02 mol L–1 sodium citrate at pH 7 which was also diluted in PBS (Phosphate Buffered Saline) solution.

18 Mineralizable Carbon. Mineralizable carbon was determined by measuring soil respiration following the methods of Franzluebbers et al. (2000) and Haney et al. (2001). Soil respiration was measured after 24 hours. Ten grams of each soil sample were measured into 50 mL polypropylene screw-top centrifuge tubes. Soil was rewetted at 50% water-filled pore space with deionized water, previously determined gravimetrically (Haney & Haney, 2010). Tubes were capped tightly and incubated at 22ºC in the dark. Concentration of CO2 was determined with an LI-840A CO2/H20 infrared gas analyzer.

Data Analysis

Data were subjected to analysis of variance (ANOVA) using PROC GLM in SAS (v.

9.3). The Fisher LSD test (α=0.05) was used to detect significant differences among treatments.

When the raw data did not meet the assumptions of ANOVA, especially the homogeneity of variances, logarithmic or square-root transformations were made. Moreover, if there were missing data, mean comparisons were made using the least square mean (LSM) test (α=0.05).

Data tables with respective standard errors for soil and crop mineral analysis can be found in the

Appendix section.

Results and Discussion

Effect of soil balancing amendments on soil chemistry

Five farms were certified organic and one was in transition to organic (Table 1). Soils varied from a sandy loam at Farm C to silt loams at Farms A, B and D, to clay loams at Farms E and F (Table 2). Similar management as outlined in Table 1 had been practiced but with some important distinctions in fertility inputs, especially in 2016 prior to planting. Analysis of soil

19 samples taken in early spring 2016 for the purpose of establishing a baseline for each nutrient provided the following insights (Table 2). CEC ranged from approximately 8 to just over 10 cmol kg -1. Calcium base saturation was much higher than predicted by the crop consultant who selected the farms, and with the exception of Farm E was already in the range of balance according to the BCSR concept. Soils on all farms except E were near neutral pH. Likewise, magnesium was optimally balanced except on Farm C where it was borderline deficient (<10%) according to BCSR. In contrast, potassium base saturation was deficient at all except Farm B.

Concentration of individual nutrients measured as ppm was also determined in spring

2016 (Table 2), and provides an alternative approach to the BCSR as a means to assess the suitability of soils at each farm for vegetable production. For the macronutrient calcium, magnesium, potassium, phosphorus and sulfur, adapted SLAN guidelines for vegetable crops as outlined by Maynard et al. (2007) were used to characterize measured levels as deficient, optimum or excessive. Deficient denotes insufficient mineral supply for optimum yields, optimum signifies sufficient levels for maximum crop yield, and excessive indicates no crop response to additional fertilization would occur. As for micronutrients, boron, copper, iron, manganese and zinc critical levels were based on Rutgers New Jersey Agricultural Experiment

Extension recommendations (Heckman et al., 2003). In that system the excessive ranking includes the potential for levels that may be toxic to crops. Following the SLAN approach, not only was the calcium level balanced, it was present in excessive amounts, even at Farm E where it was deficient according to BCSR. Like calcium, magnesium concentration at each farm was excessive, with the exception of Farm C that had optimal levels but according to BCSR was borderline deficient. Potassium levels and base saturation, were mostly deficient or borderline deficient with the exception of Farm B and E. Concentration of phosphorus was deficient at

20 Farms A, D, E and F, optimum at Farm C, and excessive at Farm B. Baseline sulfur levels in spring 2016 were mostly excessive or at the high end of the optimum range. Boron concentration was optimum at three farms (Farms A, B, D), and deficient at the others. Copper was adequately supplied in the soil of all farms, iron was present in excessive quantities. Manganese was present in optimum or excessive amounts depending on the farm. Zinc baseline concentrations were optimum at all farms.

Tables 3 and 4 describe the components of each amendment treatment, and contributed amounts of each nutrient, respectively. When considering the following findings, it is helpful to recall that major differences in quantities existed only for calcium, magnesium, sulfur, boron, copper, zinc and sodium. After two years, calcium levels (ppm) were clearly higher at Farms C and F, lower at A, B, and D, and more or less unchanged at Farm E (Figure 1). Calcium base saturation followed mostly identical trend lines. Considering the quantities of calcium added, ranging from 134 kg ha-1 in the control (MB) treatment, to 274 kg ha-1 in the GYP + BIO amendment it is not surprising that the nutrient level mostly was unchanged from, or lower than the baseline. Considering crop removal, calculated with the average total yield, head dry matter and foliage/head calcium content, cabbage in 2016 removed from 79 to 317 kg ha-1 of calcium from Farms C and A, respectively (Table 5 & 6). Which is consistent to the higher and lower calcium levels (Farm C and A respectively) found in the soil in fall 2016 when compare to their baseline levels. After two years, magnesium concentration and base saturation were either unchanged from, or lower than the spring 2016 concentration, with the exception of Farm C where an additional 25 kg ha-1 of magnesium as Epsom salts was included in the MB applied to all the plots (Figure 2). Potassium, after two years increased at all farms (Figure 3). Both ppm and base saturation follow identical trend lines. All plots received about 190 kg ha-1 of potassium

21 as potassium oxide (K2O). Additionally, all farmers applied organic-source potassium as either chicken or horse manure, compost or seaweed in 2016. Moreover, at Farms A and F additional inorganic potassium as commercial NPK fertilizer was applied by the farmer in 2016 and 2017

(Table 1). Despite the potassium inputs described, Farms A, C and D were still deficient or borderline deficient in ppm or base saturation in autumn 2017. According to Havlin (2004), replacement and exchanges in the cation exchange sites are influenced by the adsorption strength of each cation. The strength of adsorption depends first on the cation´s charge density and second on its size when hydrated. Adsorption to exchange sites is higher for calcium, followed by magnesium and then potassium. Thus excessive amounts of calcium and magnesium can compete advantageously with potassium ions for the soil cation exchange sites.

After two years of amendments, phosphorus levels at farms A, E and F were higher than they were in spring 2016, lower at farm C, and more or less unchanged at farms B and D (Figure

4). Similar to the situation with potassium, phosphorus was added mainly in the MB applied to

-1 all plots and farms; adding 132 kg ha of phosphorus as phosphorus pentoxide (P2O5) through the MB and an additional 4 kg ha-1 to plots treated with BIO. It is also important to acknowledge that the farmer added additional phosphorus inputs, especially in 2016 prior to planting, that we were unable to quantify (Table 1). NPK and P commercial fertilizers were used at Farms A & F, and Farm E, respectively. Sulfur concentration increased each year following amendments at A,

B, C and D and was overall higher than the baseline at Farms E and F (Figure 5). Sulfur applied to the plots varied from 88 to 169 kg ha-1 in the control and GYP amended plots, respectively, with an additional 5 kg ha-1 applied as Epsom salts at Farm C (deficient in Mg in spring 2016).

Boron (Figure 6) and copper (Figure 7) concentrations increased after two seasons of amendments. The MB and BIO amendments included 2.2 and 0.2 kg ha-1 of boron, respectively,

22 and 1.0 kg ha-1 of copper each. The BIO amendment added 17 kg ha-1 of iron and 1.4 kg ha-1 of manganese. Despite the scarce amount of manganese and iron included in the MB amendment

(0.2 kg ha-1 and 12 kg ha-1, respectively), iron concentration was elevated across the board at

Farms B, C, E and F and was lower than the baseline at Farms A and D (Figure 8). A somewhat analogous situation was observed with manganese that was higher at one farm, unchanged from the baseline at three farms and lower at two (Figure 9). After two years, zinc clearly increased from the spring 2016 baseline, except at Farm B where its concentration was mostly unchanged

(Figure 10). Zinc additions through the amendments were 0.7 kg ha-1 in the MB and an additional 1.2 kg ha-1 through the BIO amendment.

Readily distinguished effects of treatments on base saturation and soil nutrient levels were few and far between. Statistically significant differences, as measured by the LSD test, were more often than not unrelated to any theoretical foundation for how the amendments were expected to work (Maynard et al., 2007). All of the amendments used in this research contained relatively high amounts of calcium, varying from 134 kg ha-1 in the MB to 274 kg ha-1 total calcium in the GYP + BIO treatment (Table 4). However, only at Farm B was measured a significantly higher amount of calcium when GYP and GYP+BIO treatments were applied

(Figure 1 & Table 10). Continuing with magnesium, only at Farm C, where an additional 25 kg ha-1 of magnesium was applied as Epsom salts in the MB, was an effect on the nutrient measured. At that farm the additional 4 kg ha-1 supplied with the BIO amendment resulted in an increase relative to the control (Figures 2 & Table 10 ). Soil sampling in the spring of 2016 indicated a deficiency of potassium at farms A, C, D, and F (Figures 3 & Table10). All farms were deficient in potassium base saturation except farm B. To address this 190 kg ha-1 of potassium was supplied in the MB and an additional 10 kg ha-1 was supplied with the BIO

23 treatment in addition to the composts, manures and commercial fertilizers applied by the farmer.

Although all farms showed an increase in potassium levels from the 2016 baseline there was no treatment effect; not surprising considering the mere 5% difference in nutrient level between the different amendments. As for phosphorus, a significantly higher level was found in BIO treated plots at farms A and F (Figure 4 & Table 11). However, no clear cause and effect could be ascertained. BIO amendments applied only 20% more phosphorus compared to the control and

GYP treated plots. Furthermore, plots treated with GYP + BIO were always the lowest in phosphorus level, despite including the BIO amendments. The MB used on control plots as well as all other plots, provided 88 kg ha-1 of sulfur (Figures 5, Tables 4 & 11). Gypsum treated plots

(GYP or GYP + BIO) received an additional 81 kg ha-1 of S for a total of 39. The additional sulfur in the GYP amended plots significantly elevated sulfur level in 50% of the data points.

Boron tended to be deficient or just barely optimum in the spring of 2016 (Figures 6 & Table

12). Each of the amendments, including the control, included boron with the BIO supplying about 8% more boron than the MB. Though only a single example, it is noteworthy that BIO and

GYP + BIO resulted in elevated boron at Farm B. At Farm E, boron was significantly higher in the Gypsum amended plot. A treatment effect on copper levels was noted at farms A, C and E

(Figures 7 & Table 12). As mentioned before, 1.0 kg ha-1 of copper were added to all plots and farms in the MB. The BIO amendments supplied 65% more copper than the MB; however, it would be difficult to conclude from these results that the additional copper in BIO was reflected in the amounts found in the soil. Iron (Figures 8 & Table 12) and manganese (Figures 9 & Table

12) were both included in the BIO treatment and absent from the MB and from GYP. There were no treatment effects on either nutrient. An effect of 50% more zinc in the BIO treatment compared to the MB fertilizer, resulted in significantly higher zinc levels in the BIO and GYP +

24 BIO treatments when compared to control treated plots (Figures 10 & Table 12). Treatment effects were found at farms D, E and F.

Effect of soil balancing amendments on cabbage and squash nutrient levels, yield and quality

I relied upon leaf analysis levels for each crop and nutrient followed sufficiency ranges established by Bryson et al. (2014), to identify nutrient concentration ranges for specific plant parts and growth stages required for optimum plant growth and yield. Deficient levels signify inadequacy for optimal growth, optimum denotes sufficient levels, and a rank of excessive indicates excessive fertilization and not necessarily phytotoxicity.

Cabbage – 2016 Season. Nutrient levels measured in cabbage foliage sampled 60 days

AP, at the cupping stage of development, were in the optimum to excessive ranges for nitrogen

(Figure 11) and calcium (Figure 12), with no treatment effect measured. Magnesium was deficient in cabbage leaves from Farms B and C (Figure 13). Inclusion of Epsom salts in the MB amendment applied to all plots at Farm C apparently did not adequately resolve the nutrients deficiency identified in spring of 2016. Respecting Farm B although the baseline saturation and concentration of magnesium in the soil were both satisfactory, it is noteworthy that levels measured in autumn of 2016 and 2017 were 18 and 12 % less, respectively, than the measure in spring 2016. Cation content in plant material is dependent on the cation availability in the growing medium (Epstein, 1972), and generally the excess of one cation reduces the net uptake of other cations. This phenomenon is known as cation antagonism (Fageria, 2002). Magnesium deficiency in cabbage leaves at farm B and C may have been caused by high calcium concentrations in the soil (spring 2016). Magnesium uptake by plants is known to be decreased at high soil calcium concentration (Havlin, 2014; Fageria, 2002). Potassium was deficient in

25 cabbage leaves at all farms, reflecting the inadequate levels measured in the soil in the spring of

2016, and what appears to have been an unsuccessful effort to supply adequate potassium through any of the treatments (Figure 14). As is the case with magnesium, potassium content in plants is affected by both calcium and magnesium availability in the soil, with a strong mutual antagonism between potassium and calcium cations for plant uptake (Bryson et al., 2014). As previously mentioned, all farms presented soil high in calcium and magnesium concentration in spring 2016; thus both of those cations may have adversely affected potassium uptake by the cabbage crop.

Despite the persistent soil deficiency in phosphorus at Farms A, D, E and F, adequate levels of the nutrient were measured in cabbage foliage at all farms (Figure 15). Sulfur levels in the soil were in the excessive range and that was reflected in the cabbage foliage (Figure 16).

There was an effect of gypsum on sulfur levels at Farm A and GYP + BIO at Farm E. Boron in cabbage foliage was optimum at all farms, even at farms presenting deficient soil levels in spring

2016 (Figure 17). Even though copper concentration in the soil baseline was adequate, borderline deficiency in cabbage foliage was noted with the exception of Farm E (Figure 18). Iron concentration in cabbage foliage was optimum at all the farms (Figure 19). Soil manganese concentration was in the excessive range for farms A, B and D (Figure 20); and in the adequate range in cabbage foliage at all of the farms. No clear treatment effect was noted. Optimum zinc levels measured in cabbage foliage at all farms was consistent with spring 2016 soil levels

(Figure 21). Again, significant differences noted in zinc levels between treatments at Farm A are not obviously related to the treatments used. Excessive sodium concentration was noted in cabbage foliage from three farms (Figure 22). Sodium was present in all treatments, but mainly in the BIO amendment that added 69 kg ha-1. Consequently, a BIO treatment effect was detected

26 at farms A, D and E; where BIO amended plots were significantly higher in sodium in two cases, and the GYP + BIO treatment was higher at one farm.

Cabbage yield was effected by farm (P value <0.0001). Yields averaged 50979 kg ha-1 and 40316!kg ha-1 at farms A and B, respectively (Table 6). Lower yields were obtained at

Farm C (26406 kg ha-1); and more similar yields, 31568 and 37973 kg ha-1, at farms D and E, respectively. Differences between treatments were not detected regarding total and marketable yields, or individual head weight. In contrast, head ratio and/or moisture were affected by treatments at all farms. Head polar and equatorial diameters, deviated less from the ideal ratio

(1.0) on plots treated with GYP + BIO treatment at farms A, D and E (all planted with the Grand

Vantage variety). Radovich et al. (2004) mentioned that cabbage head polar:equatorial diameter ratio of 1.0 is the optimal head shape for marketability. Cabbage head moisture at farms C and D, was elevated in plots amended with BIO. Percentage of moisture (PM) is an important measure for fresh cabbage, related to the product juiciness and crispness (Radovich et al., 2004).

Additionally, head size and PM have been linked to glucosinolate concentration (pungency characteristic) affecting sensory quality (Radovich et al., 2004, Radovich et al., 2005)

Butternut squash – 2017 Season. Data on squash foliage nutrient levels and yield were available only from Farms B, D and F. Nutrient levels were measured in squash foliage sampled

70 days AP at the fruiting stage. Sulfur (Figure 25), boron (Figure 26), iron, manganese (Figure

27), zinc and sodium (Figure 28) were measured in adequate or excessive amounts at all farms and with the exception of boron at Farm D were unaffected by treatments. Foliar boron was higher in plots treated with GYP and BIO at Farm D than the level in leaves from the Control plots. Levels of nitrogen, calcium, potassium, phosphorus and calcium varied more respecting adequacy, with foliage from Farm F in particular, showing deficiencies in nitrogen, potassium,

27 phosphorus and copper. Farmer F applied additional natural-source NPK fertilizer prior to transplanting (Table 1) but this did not adequately address the infertility of the soil. Squash leaves from Farm D were low in calcium, an observation that was somewhat surprising considering the high base saturation and total concentration of the nutrient (Figure 1). Beyond the significant effect noted with boron at Farm D, potassium, and phosphorus were also affected significantly by soil amendments. In each case the nutrient level was highest either in the

GYP+BIO or in the BIO treatments.

Butternut squash yield varied between farms (P value <0.0001). Yield was 38381, 35129 and 13685 kg ha-1 at farms B, D and F, respectively (Table 7). The low yield at farm F was no doubt caused, at least partially, by deficiencies in several nutrients (Figures 1 – 10). Total yield and fruit number were significantly higher in the control plots compared to BIO treated plots at

Farm B. There is no treatment-based explanation for the observation. At Farm D, marketable yield was significantly higher in GYP + BIO treated plots compare to plots applied with gypsum alone. However, marketable fruit weight and length were greater in fruits from plots treated with

GYP compared to the control. No treatment effects on fruit color or fruit sucrose content were detected.

Effect of soil balancing amendments on soil biology on vegetable farms

Effects of amendments applied in Spring 2016 and 2017 on soil biology varied (Table 8) greatly among farms. Baseline organic matter content of all farms was low, ranging from 1.5 to

2.4 %. Microbial biomass, measured only in fall 2016, showed average values to be higher at farm C (333 mg C kg soil-1) and lower at farm D (154 mg C kg soil-1). Microbial biomass at farm

F denoted a treatment effect in control plots compared to GYP treated plots. Studies have shown

28 an increase or decrease in microbial biomass C when gypsum is added (Carter, 1986; Wong, et al., 2009). In Wong et al. (2008) study, the authors stated that limitation in microbial biomass were not caused by gypsum amendment, but due to the small labil C pool and the scarce carbon input to the system. Finding that were confirmed by the addition of organic materials and the resulted increase in microbial biomass. However, the addition of gypsum alone did not increase microbial biomass (Wong et al., 2008). A similar pattern was observed with POXC values among farms. Average values were consistently higher at farm C, while lower at farm F. Also, fall measurement values were overall higher than measurements made in spring. Treatment effects were noticed at two farms. POXC values at Farm B were higher in plots amended with

GYP alone when compared to the rest. However, at farm D, GYP treated plots were only significantly higher than GYP+BIO and control treated plots (spring 2017), while in fall 2017,

BIO treated plots presented a high level compared to control plots. It would be expected that plots treated with BIO amendments resulted in the highest values, as POXC is greatly influence by compost additions (Culman et al., 2013). As for Total mineralized C, all farms but Farm E, showed lower values in fall 2016 and higher values in fall 2017. In contrast, Farm E presented higher levels in fall 2016 and lower levels in fall 2017. Only at one farm (Farm E) a treatment effect was detected, where control plots were significantly higher than BIO and GYP+BIO treated plots. The resulted treatment effect may be explained by the fact that Farm E was the only farm receiving HiCal lime on both seasons, due to the low pH measured in spring 2016. HiCal lime is known to contribute to the carbon dioxide emissions to the atmosphere (West & McBride,

2005). Thus, there is a great probability that most of the CO2 released during the analysis correspond to calcium carbonate dissolution. Similar to POXC, protein extracted levels were overall higher in fall samples compared to spring samples at each farm. Differences among

29 treatments varied greatly from farm to farm. There was no clear association between the treatments and the effects, with the exception of farm C, where extracted protein was consistently lower in plots treated with gypsum alone.

Despite determination of several statistically different means, it is difficulty to infer cause and effect amongst these relationships. The fact that there was an extremely low amount of microbes included in the BIO treatment is important to recall. For instance, according to incubation essays 6.84 x 1010 bacteria were spread per hectare through BIO or GYP + BIO treatments. However, according to McNear (2013) 30 ml of soil has more microbes than the number of people on the Earth (McNear, 2013). It is likely that the ‘biology’ directly contributed by the BIO treatments was less than trivial. Moreover, in addition to the infinitesimal amount of microorganisms added, the incubation assays used to quantify them grow the microorganisms in optimum conditions; conditions certainly not representative in the field environment.

Effect of soil balancing amendments on weed communities

Weed communities were diverse and characteristic of each farm. The farmers were quite successful in controlling weeds during both growing seasons as reflected by low density numbers recorded at the fields (Figure 29). Organic farmers and particularly the group in this study, have weeding practices that consist of inter-row cultivation as soon as weeds germinate and before individuals reach 10 cm in length. In addition, the use of plastic mulch for vegetable production was a common practice among these farmers; diminishing even more the weed community and its competition with the crop. This group of farmer’s inter-row cultivated three to four times during the growing season and hand-weeded the transplanting holes in the plastic, or random weeds ocurring close to the crop. Quantifying emerged weeds was attempted in 2016 but this

30 was soon abandoned as it was not possible to coordinate counts with the farmers’ plans to cultivate/ hand weed.

Ultimately analysis of the effect of soil balancing treatments focused on measurement of the soil weed seed bank from samples collected before the amendment applications. Samples from Spring 2016 indicated a great diversity of species at on each farm. There was a total of 29 species and of which 80% were broadleaf weeds (Table 9, Figures 30 and 31). With the exception of Oxalis spp, Galinsoga parviflora and Chenopodium alba, occurrence and density data were insufficient for any species to enable analysis at the species level. No treatment effects were noted on the three species mentioned above (data not reported). For that reason, further analysis was performed on total weeds observed across all species. Farms differed significantly from each other regarding weed seed bank density and community composition. Farm D had the lowest density (ca 20 m-2) and Farm C the largest weed density (ca 70 m-2) (Figure 30).

While farms were significantly different regarding broadleaf weeds communities, all farms had a small community of grasses (Figure 31). Considering that all of the farmers were soil balancers it is worth reflecting on the data of Kelling et al. (1996) who reported a correlation between grass populations and exchangeable magnesium. Analysis of our weed seed bank data at the plot level showed that one farm (B) had a statistically significant difference between treatments, and total weed densities (Figure 32). Plots treated with gypsum alone or with BIO amendments had lower densities when compared to the control (MB) or MB + BIO treatment.

31 Tables and Graphs

Table 1. Agricultural practices at each farm (A-F) during the study period.

A B C D E F Organic.Status Certified Certified Certified In*transition Certified Certified Previous.Gypsum.applications Yes No Yes No Yes No 2016 Crop.Rotation.BT Cabbage*2*Kale Oats2Hay Cabbage2Peas2Radish2Rye2Clover Corn*2Rye Hay2Clover2Timothy*grass2Alfalfa Cover.Crops.BT Oats*2*Clover No Buckwheat No No Alfalfa Soil.amendment.BT Gypsum,*chicken*manure,* Chicken*manure,*Floristem* Compost Raw*chicken*manure High*calcium*limestone,*gypsum,* N,P,K,*humate,*seaweed,* molasses,*humates,*S,*Zn,*Cu,*B,* (3*consecutive*years) P,*compost mollasses,*fish*oil,*sea*salt N,*P,*K Plastic.mulch Yes Yes No Yes No Yes Transplant.solution No No No Yes*2*fish*oil*and*MSR No Yes*2*humates,*seaweed,* mollasses,*fish*oil*and*sea*salt Chilean*nitrate*before* Soil.amendment.DT N,*P,*K*under*plastic N,*P,*K*under*plastic transplanting* Irrigation Drip*tape Drip*tape Drip*tape Drip*tape No*irrigation Drip*tape Bacillus*thuringiensis,*sugar,* Bacillus*thuringiensis,*organic* Bacillus*thuringiensis,*organic* Insecticide.or.foliar.application Spinosad Bacillus*thuringiensis adjuvant*anti2icing*product pyrethrum pyrethrum,*plants*oil* 2017 Cover.Crops.BT Rye No Rye Soil.amendment.BT Compost Horse*manure

Plastic.mulch Yes Yes Yes Transplant.solution Yes*2*fish*oil*and*MSR Yes*2*MSR Yes2fish*oil,*SP1,*MSR

Soil.amendment.DT N,*P,*K*under*plastic N,*P,*K*under*plastic Irrigation Drip*tape Drip*tape Drip*tape Drip*tape No*irrigation Drip*tape*2*late*irrigation Insecticide.or.foliar.application Organic*pyrethrum No Organic*pyrethrum Calcium,*cytokinin*hormone

Abbreviattions:*BT,*before*treatment;*DT,*during*treatment;*MSR,*micronize*soft*rock;*SP21,*liquid*with*bacteria,*fungae*and*algae*

32 Table 2. Analysis of soil samples collected at the block level of each farm in Spring of 2016.

a OM2222222222222222CEC222222222222222 2Saturation2(%) Texture2Particles2(%) Soil Farm Soil2pH % cmol/kg2 Ca Mg K Clay Silt Sand Texture Location A 7.0 1.5 7.9 67 18 1.6 15 41 45 Loam Polk,3OH B 7.0 1.5 10.2 67 17 2.6 15 47 39 Loam Fredericksburg,3OH C 6.8 2.4 10.3 73 10 1.3 11 37 53 Sandy3Loam Wooster,3OH D 7.4 2.2 8.2 79 19 1.8 17 41 43 Loam Mount3Eaton,3OH E 5.8 2.0 10.1 57 18 1.7 27 37 37 Clay3Loam West3Salem,3OH F 6.9 1.8 9.6 67 17 1.6 25 33 43 Loam Polk,3OH

Melich232(ppm) b, c Farm Ca2 Mg2 K2 2P2 S B Cu Fe Mn Zinc2 A 1412 194 58 20 17 0.5 1.1 171 126 2.8 B 1826 235 125 89 13 0.5 2.8 151 114 4.6 C 1999 135 62 57 15 0.4 2.2 167 53 2.2 D 1733 215 67 17 9 0.5 1.8 112 149 2.3 E 1527 246 80 9 14 0.3 1.8 188 51 1.7 F 1718 216 73 8 7 0.3 1.1 146 61 1.1

Abbreviations:3OM,3organic3matter;3CEC,3cation3exchange3capacity;3D,3deficient;3A,3adequate;3E,3excessive a:3Recommended3base3saturation3according3to3BCSR:3Ca3(65V85);3Mg3(10V20);3K3(2V5) b:3Recommended3mineral3levels3for3Mehlich33:3pH3(6.5V7.2);3OM3(1V6);3(D,O,E3):3Ca3(<700,3701V895,3>895);3Mg3(<71,372V147,3>147); b:3Recommended3mineral3levels3for3Mehlich33:3(D,A,E3):3Ca3(<700,3701V895,3>895);3Mg3(<71,372V147,3>147); 3333K3(<72,373V138,3>138).Source:Maynard3et3al.(2007) c:3Recommended3mineral3levels3for3Mehlich33:3(D,O,E):3P3(3<35,336V68,3>68);3(D,O,E):3S3(0V6,37V12,3>12);3(D,E):3B3(<0.5,3>20);3Cu3(<0.5,3>20);3Fe3(<50,3>100);3 c:3Recommended3mineral3levels3for3Mehlich33:3(D,A,E):3P3(3<35,336V68,3>68);3(D,A,E):3S3(0V6,37V12,3>12);3(D,E):3B3(<0.5,3>20);3Cu3(<0.5,3>20);3Fe3(<50,3>100);3 3333Mn3(<25,3>100);3Zn3(<10,3>50)3Source:Maynard3et3al.3(2007),3Rutgers3New3Jersey3Agricultural3Experiment3Extension3(Heckman3et3al.,32003)

33 Table 3. Soil amendments applied according to treatment and including individual ingredients.

Soil%Amendment Mineral%Component Brand%Name%/%Source kg/ha Soft%Rock%Phosphate (01310) Green%Field%Farms 673 Sulfate%of%Potash%(010150) (010150) Green%Field%Farms 336 Mineral%%%%%%%%%% Super%Sulfur%Potash 70175%%SO4%1%315%%Ca%1%8115%%K2O Green%Field%Farms 168 Fertilizer%%%%%%%%%% Boron 10%%B Green%Field%Farms 22 Blend%%%%%%%%%%%%%%%%%%% (MB) Zinc 36%%Zn Green%Field%Farms 11 Copper 25%%Cu Green%Field%Farms 6 Pelletized%High%Calcium%Lime%(a) 33%%Ca% Green%Field%Farms 1681 Epsom%Salt%(b) 11%%Mg%1%16%%S Green%Field%Farms 224

Gypsum Pelletized%Gypsum 21%%Ca%1%16%%S 560

Kelp%Meal Tidal%Organics 56 Humates Mesa%Verde 112 Compost Harvey's%Incred1A1Soil% 336 Biological% Dry%molasses Ultralyx 56 Stimulants%%%%%%%%%%%%%%%%%% (BIO) Liquid%Fish%(gal/acre) Neptune’s%Harvest %%5* Sea%minerals%and%trace%elements Sea%90%/%SeaAgri 112 Microbial%Soil%Inoculant Bio%Aid%WS%/%AgriEnergy%Resources 0.1 Clay%based%trace%minerals Flora%Stim%/%Restora1Life%Minerals 336 (a)%Only%applied%to%farm%E (b)%Only%applied%to%farm%C *Liters/hectare

34 Table 4. Mineral composition of each treatment, including analysis of the mineral blend applied to all plots.

Kg/ha

Treatments) N Ca K2 O Mg P2O5 S B Cu Fe Mn Zn Na Control 134 188 5 132 88 2.2 1 12 0.2 0.7 7 GYP 2 258 188 17 132 169 2.2 1 13 0.2 0.7 8 BIO 7 150 198 9 136 88 2.4 2 29 1.6 2.9 76 GYP)+)BIO 9 274 198 21 136 169 2.4 2 30 1.6 2.9 77 Additional4for4Farm4C 25 5 Additional4for4Farm4E 555

Table 5. Average nutrient removal for cabbage.

Calcium'Content Total' Crop' Average'Yield Head'Moiture Head'Dry'Matter Fruit Leaves Yield'Dry' Nutrient' Farm kg/ha % % % Kg kg/ha Farm%A 50979 86 14 0.11 4.2 7356 317 Farm%C 26406 87 13 0.15 2.1 3512 79

35 Figure 1. Baseline concentration / base saturation of calcium in the soil in Spring 2016

prior to treatment, and after harvest in fall of 2016 and 2017. Shading is used to indicate

relative sufficiency of nutrients according to SLAN or BCSR; pink shading indicates excessive

levels, green indicated adequacy, and yellow indicates a deficiency.

701$895 Calcium/Farm/A Calcium/Farm/A

2800 100 2600 90 2400 2200 80 2000

ppm 1800 70 1600

1400 Base/Saturation/ (%) 60 1200 1000 50 Spring/2016/ Fall/2016 Fall/2017 Spring/2016 Fall/2016 Fall/2017

Control GYP BIO GYP/+/BIO Control GYP BIO GYP/+/BIO

Calcium/Farm/B Calcium/Farm/B

2800 100 2600 90 2400 2200 80 2000

ppm 1800 70 1600

1400 Base/Saturation/ (%) 60 1200 1000 50 Spring/2016/ Fall/2016 Fall/2017 Spring/2016 Fall/2016 Fall/2017

Control GYP BIO GYP/+/BIO Control GYP BIO GYP/+/BIO

Calcium/Farm/C Calcium/Farm/C

2800 100 2600 2400 90 2200 80 2000

ppm 1800 70 1600

1400 Base/Saturation/ (%) 60 1200 1000 50 Spring/2016/ Fall/2016 Fall/2017 Spring/2016 Fall/2016 Fall/2017

Control GYP BIO GYP/+/BIO Control GYP BIO GYP/+/BIO

36 Figure 1. continued

Calcium.Farm.D Calcium.Farm.D

2800 100 2600 2400 90 2200 80 2000

ppm 1800 70 1600 1400 Base.Saturation. (%) 60 1200 1000 50 Spring.2016. Fall.2016 Fall.2017 Spring.2016 Fall.2016 Fall.2017

Control GYP BIO GYP.+.BIO Control GYP BIO GYP.+.BIO

Calcium.Farm.E Calcium.Farm.E

2800 100 2600 90 2400 2200 80 2000

ppm 1800 70 1600

1400 Base.Saturation. (%) 60 1200 1000 50 Spring.2016. Fall.2016 Fall.2017 Spring.2016 Fall.2016 Fall.2017

Control GYP BIO GYP.+.BIO Control GYP BIO GYP.+.BIO

Calcium.Farm.F Calcium.Farm.F

2800 100 2600 90 2400 2200 80 2000

ppm 1800 70 1600

1400 Base.Saturation. (%) 60 1200 1000 50 Spring.2016. Fall.2016 Fall.2017 Spring.2016 Fall.2016 Fall.2017

Control GYP BIO GYP.+.BIO Control GYP BIO GYP.+.BIO

37 Figure 2. Baseline concentration/ base saturation of magnesium in the soil in Spring 2016

prior to treatment, and after harvest in fall of 2016 and 2017. Shading is used to indicate

relative sufficiency of nutrients according to SLAN or BCSR; pink shading indicates excessive

levels, green indicated adequacy, and yellow indicates a deficiency.

72#147 Magnesium/Farm/A Magnesium/Farm/A

300 25

250 20

200 15 ppm

150 10 Base/Saturation/ (%)

100 5 Spring/2016 Fall/2016 Fall/2017 Spring/2016 Fall/2016 Fall/2017

Control GYP BIO GYP/+/BIO Control GYP BIO GYP/+/BIO

Magnesium//Farm/B Magnesium//Farm/B

300 25

250 20

200 15 ppm

150 10 Base/Saturation/ (%)

100 5 Spring/2016 Fall/2016 Fall/2017 Baseline Fall/2016 Fall/2017

Control GYP BIO GYP/+/BIO Control GYP BIO GYP/+/BIO

Magnesium//Farm/C Magnesium//Farm/C

300 25

250 20

200 15 ppm

150 10 Base/Saturation/ (%)

100 5 Spring/2016 Fall/2016 Fall/2017 Baseline Fall/2016 Fall/2017

Control GYP BIO GYP/+/BIO Control GYP BIO GYP/+/BIO

38 Figure 2. continued

Magnesium,Farm,D Magnesium,Farm,D

300 25

250 20

200 15 ppm

150 10 Base,Saturation, (%)

100 5 Spring,2016 Fall,2016 Fall,2017 Baseline Fall,2016 Fall,2017

Control GYP BIO GYP,+,BIO Control GYP BIO GYP,+,BIO

Magnesium,Farm,E Magnesium,Farm,E

300 25

250 20

200 15 ppm

150 10 Base,Saturation, (%)

100 5 Spring,2016 Fall,2016 Fall,2017 Baseline Fall,2016 Fall,2017

Control GYP BIO GYP,+,BIO Control GYP BIO GYP,+,BIO

Magnesium,Farm,F Magnesium,Farm,F

300 25

250 20

200 15 ppm

150 10 Base,Saturation, (%)

100 5 Spring,2016 Fall,2016 Fall,2017 Baseline Fall,2016 Fall,2017

Control GYP BIO GYP,+,BIO Control GYP BIO GYP,+,BIO

39 Figure 3. Baseline concentration/ base saturation of potassium in the soil in Spring 2016

prior to treatment, and after harvest in fall of 2016 and 2017. Shading is used to indicate

relative sufficiency of nutrients according to SLAN or BCSR; pink shading indicates excessive

levels, green indicated adequacy, and yellow indicates a deficiency.

73#138 Potassium1Farm1A Potassium1Farm1A 160 4 3.5 140 3 120 2.5 100 2 ppm 1.5 80 1 60 Base1Saturation1 (%) 0.5 40 0 Spring12016 Fall12016 Fall12017 Spring12016 Fall12016 Fall12017

Control GYP BIO GYP1+1BIO Control GYP BIO GYP1+1BIO

Potassium1Farm1B Potassium1Farm1B

160 4 3.5 140 3 120 2.5 100 2 ppm 1.5 80 1 60 Base1Saturation1 (%) 0.5 40 0 Spring12016 Fall12016 Fall12017 Spring12016 Fall12016 Fall12017

Control GYP BIO GYP1+1BIO Control GYP BIO GYP1+1BIO

Potassium1Farm1C Potassium1Farm1C

160 4 3.5 140 3 120 2.5 100 2 ppm 1.5 80 1 60 Base1Saturation1 (%) 0.5 40 0 Spring12016 Fall12016 Fall12017 Spring12016 Fall12016 Fall12017

Control GYP BIO GYP1+1BIO Control GYP BIO GYP1+1BIO

40 Figure 3. continued

Potassium-Farm-D Potassium-Farm-D

160 4 3.5 140 3 120 2.5 100 2 ppm 1.5 80 1 60 Base-Saturation- (%) 0.5 40 0 Spring-2016 Fall-2016 Fall-2017 Spring-2016 Fall-2016 Fall-2017

Control GYP BIO GYP-+-BIO Control GYP BIO GYP-+-BIO

Potassium-Farm-E Potassium-Farm-E

160 4 3.5 140 3 120 2.5 100 2 ppm 1.5 80 1 60 Base-Saturation- (%) 0.5 40 0 Spring-2016 Fall-2016 Fall-2017 Spring-2016 Fall-2016 Fall-2017

Control GYP BIO GYP-+-BIO Control GYP BIO GYP-+-BIO

Potassium-Farm-F Potassium-Farm-F

160 4 3.5 140 3 120 2.5 100 2 ppm 1.5 80 1 60 Base-Saturation- (%) 0.5 40 0 Spring-2016 Fall-2016 Fall-2017 Spring-2016 Fall-2016 Fall-2017

Control GYP BIO GYP-+-BIO Control GYP BIO GYP-+-BIO

41 Figure 4. Baseline concentration of phosphorus in the soil in Spring 2016 prior to

treatment, and after harvest in fall of 2016 and 2017. Shading is used to indicate relative

sufficiency of nutrients according to SLAN or BCSR; pink shading indicates excessive levels,

green indicated adequacy, and yellow indicates a deficiency.

36#68 Phosphorus/ Farm/A Phosphorus/ /Farm/D

120 120

100 100

80 80 60

60 ppm ppm 40 40

20 20

0 0 Spring/2016 Fall/2016 Fall/2017 Spring/2016 Fall/2016 Fall/2017

Control GYP BIO GYP/+/BIO Control GYP BIO GYP/+/BIO

Phosphorus/ Farm/B Phosphorus/ Farm/E

120 120

100 100

80 80 60

60 ppm ppm 40 40

20 20

0 0 Spring/2016 Fall/2016 Fall/2017 Spring/2016 Fall/2016 Fall/2017

Control GYP BIO GYP/+/BIO Control GYP BIO GYP/+/BIO

Phosphorus/ Farm/C Phosphorus/ Farm/F

120 120

100 100

80 80

60 60 ppm ppm 40 40

20 20

0 0 Spring/2016 Fall/2016 Fall/2017 Spring/2016 Fall/2016 Fall/2017

Control GYP BIO GYP/+/BIO Control GYP BIO GYP/+/BIO

42 Figure 5. Baseline concentration of sulfur in the soil in Spring 2016 prior to treatment, and

after harvest in fall of 2016 and 2017. Shading is used to indicate relative sufficiency of

nutrients according to SLAN or BCSR; pink shading indicates excessive levels, green indicated

adequacy, and yellow indicates a deficiency.

Sulfur0 Farm0A Sulfur0 Farm0D

120 120

100 100 17 80 80

60 60 ppm ppm 40 40

20 20

0 0 Spring02016 Fall02016 Fall02017 Spring02016 Fall02016 Fall02017

Control GYP BIO GYP0+0BIO Control GYP BIO GYP0+0BIO

Sulfur0 Farm0B Sulfur0 Farm0E

120 120

100 100 13 80 80 60

60 ppm ppm 40 40

20 20

0 0 Spring02016 Fall02016 Fall02017 Spring02016 Fall02016 Fall02017

Control GYP BIO GYP0+0BIO Control GYP BIO GYP0+0BIO

Sulfur0 Farm0C Sulfur0 Farm0F

120 120 15 100 100

80 80

60 60 ppm ppm 40 40

20 20

0 0 Spring02016 Fall02016 Fall02017 Spring02016 Fall02016 Fall02017

Control GYP BIO GYP0+0BIO Control GYP BIO GYP0+0BIO

43 Figure 6. Baseline concentration of boron in the soil in Spring 2016 prior to treatment, and

after harvest in fall of 2016 and 2017. Shading is used to indicate relative sufficiency of

nutrients according to SLAN or BCSR; pink shading indicates excessive levels, green indicated

adequacy, and yellow indicates a deficiency.

Boron-Farm-A Boron-Farm-D

2.5 2.5

2 2

1.5 1.5 ppm ppm 1 1

0.5 0.5

0 0 Spring-2016 Fall-2016 Fall-2017 Spring-2016 Fall-2016 Fall-2017

Control GYP BIO GYP-+-BIO Control GYP BIO GYP-+-BIO

Boron-Farm-B Boron-Farm-E

2.5 2.5

2 2

1.5 1.5 ppm ppm 1 1

0.5 0.5

0 0 Spring-2016 Fall-2016 Fall-2017 Spring-2016 Fall-2016 Fall-2017

Control GYP BIO GYP-+-BIO Control GYP BIO GYP-+-BIO

Boron-Farm-C Boron-Farm-F

2.5 2.5

2 2

1.5 1.5 ppm ppm 1 1 0.4 0.5 0.5

0 0 Spring-2016 Fall-2016 Fall-2017 Spring-2016 Fall-2016 Fall-2017

Control GYP BIO GYP-+-BIO Control GYP BIO GYP-+-BIO

44 Figure 7. Baseline concentration of copper in the soil in Spring 2016 prior to treatment, and after harvest in fall of 2016 and 2017. Shading is used to indicate relative sufficiency of nutrients according to SLAN or BCSR; pink shading indicates excessive levels, green indicated adequacy, and yellow indicates a deficiency.

Copper- Farm-A Copper- Farm-D

10 10

8 8

6 6 ppm ppm 4 4

2 2

0 0 Spring-2016 Fall-2016 Fall-2017 Spring-2016 Fall-2016 Fall-2017

Control GYP BIO GYP-+-BIO Control GYP BIO GYP-+-BIO

Copper- Farm-B Copper- Farm-E

10 10

8 8

6 6 ppm ppm 4 4

2 2

0 0 Spring-2016 Fall-2016 Fall-2017 Spring-2016 Fall-2016 Fall-2017

Control GYP BIO GYP-+-BIO Control GYP BIO GYP-+-BIO

Copper- Farm-C Copper- Farm-F

10 10

8 8

6 6 ppm ppm 4 4

2 2

0 0 Spring-2016 Fall-2016 Fall-2017 Spring-2016 Fall-2016 Fall-2017

Control GYP BIO GYP-+-BIO Control GYP BIO GYP-+-BIO

45 Figure 8. Baseline concentration of iron in the soil in Spring 2016 prior to treatment, and after harvest in fall of 2016 and 2017. Shading is used to indicate relative sufficiency of nutrients according to SLAN or BCSR; pink shading indicates excessive levels, green indicated adequacy, and yellow indicates a deficiency.

Iron+Farm+A Iron+Farm+D

250 250

200 200

150 150 ppm ppm

100 100

50 50 Spring+2016 Fall+2016 Fall+2017 Spring+2016 Fall+2016 Fall+2017

Control GYP BIO GYP+++BIO Control GYP BIO GYP+++BIO

Iron+Farm+B Iron+Farm+E

250 250

200 200

150 150 ppm ppm

100 100

50 50 Spring+2016 Fall+2016 Fall+2017 Spring+2016 Fall+2016 Fall+2017

Control GYP BIO GYP+++BIO Control GYP BIO GYP+++BIO

Iron+Farm+C Iron+Farm+F

250 250

200 200

150 150 ppm ppm

100 100

50 50 Spring+2016 Fall+2016 Fall+2017 Spring+2016 Fall+2016 Fall+2017

Control GYP BIO GYP+++BIO Control GYP BIO GYP+++BIO

46 Figure 9. Baseline concentration of manganese in the soil in Spring 2016 prior to treatment, and after harvest in fall of 2016 and 2017. Shading is used to indicate relative sufficiency of nutrients according to SLAN or BCSR; pink shading indicates excessive levels, green indicated adequacy, and yellow indicates a deficiency.

Manganese-Farm-A Manganese-D

160 160 140 140 120 120 100 100 ppm ppm 80 80 60 60 40 40 20 20 Spring-2016 Fall-2016 Fall-2017 Spring-2016 Fall-2016 Fall-2017

Control GYP BIO GYP-+-BIO Control GYP BIO GYP-+-BIO

Manganese-B Manganese-E

160 160 140 140 120 120 100 100 ppm ppm 80 80 60 60 40 40 20 20 Spring-2016 Fall-2016 Fall-2017 Spring-2016 Fall-2016 Fall-2017

Control GYP BIO GYP-+-BIO Control GYP BIO GYP-+-BIO

Manganese-C Manganese-F

160 160 140 140 120 120 100 100 ppm ppm 80 80 60 60 40 40 20 20 Spring-2016 Fall-2016 Fall-2017 Spring-2016 Fall-2016 Fall-2017

Control GYP BIO GYP-+-BIO Control GYP BIO GYP-+-BIO

47 Figure 10. Baseline concentration of zinc in the soil in Spring 2016 prior to treatment, and

after harvest in fall of 2016 and 2017. Shading is used to indicate relative sufficiency of

nutrients according to SLAN or BCSR; pink shading indicates excessive levels, green indicated

adequacy, and yellow indicates a deficiency.

1.0$50 Zinc0Farm0A Zinc0Farm0D

12 12

10 10

8 8

6 6 ppm ppm 4 4

2 2

0 0 Spring02016 Fall02016 Fall02017 Spring02016 Fall02016 Fall02017

Control GYP BIO GYP0+0BIO Control GYP BIO GYP0+0BIO

Zinc0Farm0B Zinc0Farm0E

12 12

10 10

8 8

6 6 ppm ppm 4 4

2 2

0 0 Spring02016 Fall02016 Fall02017 Spring02016 Fall02016 Fall02017

Control GYP BIO GYP0+0BIO Control GYP BIO GYP0+0BIO

Zinc0Farm0C Zinc0Farm0F

12 12

10 10

8 8

6 6 ppm ppm 4 4

2 2

0 0 Spring02016 Fall02016 Fall02017 Spring02016 Fall02016 Fall02017

Control GYP BIO GYP0+0BIO Control GYP BIO GYP0+0BIO

48 Figure 11. Cabbage leaf nitrogen concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017. Shading is used to indicate relative sufficiency of nutrients; pink shading indicates excessive levels, green indicated adequacy, and yellow indicates a deficiency. Letters above data points indicate significant differences at

α=0.05, LSD test. Data points that share the same letter are not significantly different.

Nitrogen7Farm7A Nitrogen7Farm7C

6.0 6.0

5.0 5.0

4.0 4.0 % 3.0 % 3.0

2.0 2.0

1.0 1.0

0.0 0.0 Control GYP BIO GYP7+7BIO Control GYP BIO GYP7+7BIO

Summer72016 Summer72016

Nitrogen7Farm7B Nitrogen7Farm7D

6.0 6.0

5.0 5.0

4.0 4.0 a a a b % 3.0 % 3.0

2.0 2.0

1.0 1.0

0.0 0.0 Control GYP BIO GYP7+7BIO Control GYP BIO GYP7+7BIO

Summer72016 Summer72016

Nitrogen7Farm7E

6.0

5.0

4.0

% 3.0

2.0

1.0

0.0 Control GYP BIO GYP7+7BIO

Summer72016

49 Figure 12. Cabbage leaf calcium concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017. Shading is used to indicate relative sufficiency of nutrients; pink shading indicates excessive levels, green indicated adequacy, and yellow indicates a deficiency. Letters above data points indicate significant differences at

α=0.05, LSD test. Data points that share the same letter are not significantly different.

Calcium5Farm5A Calcium Farm5C

6.0 6.0

5.0 5.0

4.0 4.0

% 3.0 % 3.0

2.0 2.0

1.0 1.0

0.0 0.0 Control GYP BIO GYP5+5BIO Control GYP BIO GYP5+5BIO

Summer52016 Summer52016

Calcium Farm5B Calcium Farm5D

6.0 6.0

5.0 5.0

4.0 4.0

% 3.0 % 3.0

2.0 2.0

1.0 1.0

0.0 0.0 Control GYP BIO GYP5+5BIO Control GYP BIO GYP5+5BIO

Summer52016 Summer52016

Calcium Farm5E

6.0

5.0

4.0

% 3.0

2.0

1.0

0.0 Control GYP BIO GYP5+5BIO

Summer52016

50 Figure 13. Cabbage leaf magnesium concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017. Shading is used to indicate relative sufficiency of nutrients; pink shading indicates excessive levels, green indicated adequacy, and yellow indicates a deficiency. Letters above data points indicate significant differences at

α=0.05, LSD test. Data points that share the same letter are not significantly different.

Magnesium4Farm4A Magnesium Farm4C

1.00 1.00

0.80 0.80

0.60 0.60 % % 0.40 0.40

0.20 0.20

0.00 0.00 Control GYP BIO GYP4+4BIO Control GYP BIO GYP4+4BIO

Summer42016 Summer42016

Magnesium Farm4B Magnesium Farm4D

1.00 1.00

0.80 0.80

0.60 0.60 % % 0.40 0.40

0.20 0.20

0.00 0.00 Control GYP BIO GYP4+4BIO Control GYP BIO GYP4+4BIO

Summer42016 Summer42016

Magnesium Farm4E

1.00

0.80

0.60 % 0.40

0.20

0.00 Control GYP BIO GYP4+4BIO

Summer42016

51 Figure 14. Cabbage leaf potassium concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017. Shading is used to indicate relative sufficiency of nutrients; pink shading indicates excessive levels, green indicated adequacy, and yellow indicates a deficiency. Letters above data points indicate significant differences at

α=0.05, LSD test. Data points that share the same letter are not significantly different.

Potassium7Farm7A Potassium Farm7C

6.0 6.0

5.0 5.0

4.0 4.0

% 3.0 % 3.0 a a 2.0 ab b 2.0

1.0 1.0

0.0 0.0 Control GYP BIO GYP7+7BIO Control GYP BIO GYP7+7BIO

Summer72016 Summer72016

Potassium Farm7B Potassium Farm7D

6.0 6.0

5.0 5.0

4.0 4.0

% 3.0 % 3.0

2.0 2.0

1.0 1.0

0.0 0.0 Control GYP BIO GYP7+7BIO Control GYP BIO GYP7+7BIO

Summer72016 Summer72016

Potassium Farm7E

6.0

5.0

4.0

% 3.0

2.0

1.0

0.0 Control GYP BIO GYP7+7BIO

Summer72016

52 Figure 15. Cabbage leaf phosphorus concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017. Shading is used to indicate relative sufficiency of nutrients; pink shading indicates excessive levels, green indicated adequacy, and yellow indicates a deficiency. Letters above data points indicate significant differences at

α=0.05, LSD test. Data points that share the same letter are not significantly different.

Phosphorus4 Farm4A Phosphorus Farm4C

1.00 1.00

0.80 0.80

0.60 0.60 % % 0.40 0.40

0.20 0.20

0.00 0.00 Control GYP BIO GYP4+4BIO Control GYP BIO GYP4+4BIO

Summer42016 Summer42016

Phosphorus Farm4B Phosphorus Farm4D

1.00 1.00

0.80 0.80

0.60 0.60 % % 0.40 0.40

0.20 0.20

0.00 0.00 Control GYP BIO GYP4+4BIO Control GYP BIO GYP4+4BIO

Summer42016 Summer42016

Phosphorus Farm4E

1.00

0.80

0.60 % 0.40

0.20

0.00 Control GYP BIO GYP4+4BIO

Summer42016

53 Figure 16. Cabbage leaf sulfur concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017. Shading is used to indicate relative sufficiency of nutrients; pink shading indicates excessive levels, green indicated adequacy, and yellow indicates a deficiency. Letters above data points indicate significant differences at

α=0.05, LSD test. Data points that share the same letter are not significantly different.

Sulfur4 Farm4A Sulfur Farm4C

2.5 2.5 a ab a 2.0 b 2.0

1.5 1.5 % % 1.0 1.0

0.5 0.5

0.0 0.0 Control GYP BIO GYP4+4BIO Control GYP BIO GYP4+4BIO

Summer42016 Summer42016

Sulfur Farm4B Sulfur Farm4D

2.5 2.5

2.0 2.0

1.5 1.5 % % 1.0 1.0

0.5 0.5

0.0 0.0 Control GYP BIO GYP4+4BIO Control GYP BIO GYP4+4BIO

Summer42016 Summer42016

Sulfur Farm4E

2.5

2.0 a a a 1.5 b % 1.0

0.5

0.0 Control GYP BIO GYP4+4BIO

Summer42016

54 Figure 17. Cabbage leaf boron concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017. Shading is used to indicate relative sufficiency of nutrients; pink shading indicates excessive levels, green indicated adequacy, and yellow indicates a deficiency. Letters above data points indicate significant differences at

α=0.05, LSD test. Data points that share the same letter are not significantly different.

Boron Farm6A Boron Farm6C

80 80 70 70 60 60 50 50 40

40 ppm ppm 30 30 20 20 10 10 0 0 Control GYP BIO GYP6+6BIO Control GYP BIO GYP6+6BIO

Summer62016 Summer62016

Boron Farm6B Boron Farm6D

80 80 70 70 60 60 50 50 40 40 ppm ppm 30 30 20 20 10 10 0 0 Control GYP BIO GYP6+6BIO Control GYP BIO GYP6+6BIO

Summer62016 Summer62016

Boron Farm6E

80 70 60 50 40 ppm 30 20 10 0 Control GYP BIO GYP6+6BIO

Summer62016

55 Figure 18. Cabbage leaf copper concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017. Shading is used to indicate relative sufficiency of nutrients; pink shading indicates excessive levels, green indicated adequacy, and yellow indicates a deficiency. Letters above data points indicate significant differences at

α=0.05, LSD test. Data points that share the same letter are not significantly different.

Copper Farm2A Copper Farm2C

20.0 20.0

15.0 15.0

10.0 10.0 ppm ppm

5.0 5.0

0.0 0.0 Control GYP BIO GYP2+2BIO Control GYP BIO GYP2+2BIO

Summer22016 Summer22016

Copper Farm2B Copper Farm2D

20.0 20.0

15.0 15.0

10.0 10.0 ppm ppm

5.0 5.0

0.0 0.0 Control GYP BIO GYP2+2BIO Control GYP BIO GYP2+2BIO

Summer22016 Summer22016

Copper Farm2E

20.0

15.0

10.0 ppm

5.0

0.0 Control GYP BIO GYP2+2BIO

Summer22016

56 Figure 19. Cabbage leaf iron concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017. Shading is used to indicate relative sufficiency of nutrients; pink shading indicates excessive levels, green indicated adequacy, and yellow indicates a deficiency. Letters above data points indicate significant differences at α=0.05, LSD test. Data points that share the same letter are not significantly different.

Iron Farm2A Iron Farm2C

300 300

250 250

200 200

150 150 ppm ppm 100 100

50 50

0 0 Control GYP BIO GYP2+2BIO Control GYP BIO GYP2+2BIO

Summer22016 Summer22016

Iron Farm2B Iron Farm2D

300 300

250 250

200 200

150 150 ppm ppm 100 100

50 50

0 0 Control GYP BIO GYP2+2BIO Control GYP BIO GYP2+2BIO

Summer22016 Summer22016

Iron Farm2E

300

250

200

150 ppm 100

50

0 Control GYP BIO GYP2+2BIO

Summer22016

57 Figure 20. Cabbage leaf manganese concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017. Shading is used to indicate relative sufficiency of nutrients; pink shading indicates excessive levels, green indicated adequacy, and yellow indicates a deficiency. Letters above data points indicate significant differences at

α=0.05, LSD test. Data points that share the same letter are not significantly different.

Manganese Farm4A Manganese Farm4C

300 300

250 250

200 200

150 150 ppm ppm

100 ab b ab a 100 50 50

0 0 Control GYP BIO GYP4+4BIO Control GYP BIO GYP4+4BIO

Summer42016 Summer42016

Manganese Farm4B Manganese Farm4D

300 300

250 250

200 200

150 150 ppm ppm 100 100

50 50

0 0 Control GYP BIO GYP4+4BIO Control GYP BIO GYP4+4BIO

Summer42016 Summer42016

Manganese Farm4E

300

250

200

150 ppm 100

50

0 Control GYP BIO GYP4+4BIO

Summer42016

58 Figure 21. Cabbage leaf zinc concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017. Shading is used to indicate relative sufficiency of nutrients; pink shading indicates excessive levels, green indicated adequacy, and yellow indicates a deficiency. Letters above data points indicate significant differences at α=0.05, LSD test. Data points that share the same letter are not significantly different.

Zinc Farm4A Zinc Farm4C

300 300

250 250

200 200

150 150 ppm ppm 100 100 a b b ab 50 50

0 0 Control GYP BIO GYP4+4BIO Control GYP BIO GYP4+4BIO

Summer42016 Summer42016

Zinc Farm4B Zinc Farm4D

300 300

250 250

200 200

150 150 ppm ppm 100 100

50 50

0 0 Control GYP BIO GYP4+4BIO Control GYP BIO GYP4+4BIO

Summer42016 Summer42016

Zinc Farm4E

300

250

200

150 ppm 100

50

0 Control GYP BIO GYP4+4BIO

Summer42016

59 Figure 22. Cabbage leaf sodium concentration at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017. Shading is used to indicate relative sufficiency of nutrients; pink shading indicates excessive levels, green indicated adequacy, and yellow indicates a deficiency. Letters above data points indicate significant differences at

α=0.05, LSD test. Data points that share the same letter are not significantly different.

Sodium Farm4A Sodium Farm4C

3500 a 3500

3000 a 3000

2500 2500 b b ppm ppm 2000 2000

1500 1500

1000 1000 Control GYP BIO GYP4+4BIO Control GYP BIO GYP4+4BIO

Summer42016 Summer42016

Sodium Farm4B Sodium Farm4D

3500 3500 a 3000 3000 a

2500 2500

ppm 2000 ppm 2000 b b

1500 1500

1000 1000 Control GYP BIO GYP4+4BIO Control GYP BIO GYP4+4BIO

Summer42016 Summer42016

Sodium Farm4E

3500

3000 a 2500 ppm 2000 b b b 1500

1000 Control GYP BIO GYP4+4BIO

Summer42016

60 Figure 23. Butternut squash leaf nitrogen and calcium concentrations at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017. Shading is used to indicate relative sufficiency of nutrients; pink shading indicates excessive levels, green indicated adequacy, and yellow indicates a deficiency. Letters above data points indicate significant differences at α=0.05, LSD test. Data points that share the same letter are not significantly different.

BUTTERNUT'SQUASH

Nitrogen'Farm'B Calcium6Farm6B

7.00 7.00 6.00 6.00 5.00 5.00 4.00 4.00 % % 3.00 3.00 2.00 2.00 1.00 1.00 0.00 0.00 Control GYP BIO GYP'+'BIO Control GYP BIO GYP6+6BIO

Summer'2016 Summer62016

Nitrogen'Farm'D Calcium Farm6D

7.00 7.00 6.00 6.00 5.00 5.00 4.00 4.00 % % 3.00 3.00 2.00 2.00 1.00 1.00 0.00 0.00 Control GYP BIO GYP'+'BIO Control GYP BIO GYP6+6BIO

Summer'2016 Summer62016

Nitrogen'Farm'F Calcium Farm6F

7.00 7.00 6.00 6.00 5.00 5.00 4.00 4.00 % % 3.00 3.00 2.00 2.00 1.00 1.00 0.00 0.00 Control GYP BIO GYP'+'BIO Control GYP BIO GYP6+6BIO

Summer'2016 Summer62016

61

Figure 24. Butternut squash leaf magnesium and potassium concentrations at farms A, B,

C, D, and E in samples collected at the cupping growth stage in 2016 and 2017. Shading is used to indicate relative sufficiency of nutrients; pink shading indicates excessive levels, green indicated adequacy, and yellow indicates a deficiency. Letters above data points indicate significant differences at α=0.05, LSD test. Data points that share the same letter are not significantly different.

Magnesium3Farm3B Potassium7Farm7B

3.00 6.00

2.50 5.00

2.00 4.00 %

% 1.50 3.00

1.00 2.00

0.50 1.00

0.00 0.00 Control GYP BIO GYP3+3BIO Control GYP BIO GYP7+7BIO

Summer32016 Summer72016

Magnesium Farm3D Potassium Farm7D

3.00 6.00

2.50 5.00 a ab ab 2.00 4.00 b % % 1.50 3.00

1.00 2.00

0.50 1.00

0.00 0.00 Control GYP BIO GYP3+3BIO Control GYP BIO GYP7+7BIO

Summer32016 Summer72016

Magnesium Farm3F Potassium Farm7F

3.00 6.00

2.50 5.00

2.00 4.00 % % 1.50 3.00

1.00 2.00

0.50 1.00

0.00 0.00 Control GYP BIO GYP3+3BIO Control GYP BIO GYP7+7BIO

Summer32016 Summer72016

62 Figure 25. Butternut squash leaf phosphorus and sulfur concentrations at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017. Shading is used to indicate relative sufficiency of nutrients; pink shading indicates excessive levels, green indicated adequacy, and yellow indicates a deficiency. Letters above data points indicate significant differences at α=0.05, LSD test. Data points that share the same letter are not significantly different.

Phosphorus4 Farm4B Sulfur2 Farm2B 2.50 1.40 2.00 1.20 1.00 1.50

0.80 % % 1.00 0.60

0.40 0.50 0.20 0.00 0.00 Control GYP BIO GYP2+2BIO Control GYP BIO GYP4+4BIO Summer22016 Summer42016

Phosphorus Farm4D Sulfur Farm2D 2.50 1.40 1.20 2.00 a 1.00 ab b ab 1.50

0.80 % % 0.60 1.00 ab ab b a 0.40 0.50 0.20 0.00 0.00 Control GYP BIO GYP4+4BIO Control GYP BIO GYP2+2BIO

Summer42016 Summer22016

Phosphorus Farm4F Sulfur Farm2F

2.50 1.40 1.20 2.00 1.00 1.50 0.80 % % 0.60 1.00 0.40 0.50 0.20 0.00 0.00 Control GYP BIO GYP4+4BIO Control GYP BIO GYP2+2BIO

Summer42016 Summer22016

63 Figure 26. Butternut squash leaf boron and copper concentrations at farms A, B, C, D, and

E in samples collected at the cupping growth stage in 2016 and 2017. Shading is used to indicate relative sufficiency of nutrients; pink shading indicates excessive levels, green indicated adequacy, and yellow indicates a deficiency. Letters above data points indicate significant differences at α=0.05, LSD test. Data points that share the same letter are not significantly different.

Copper Farm4B Boron Farm1B

200 100.0

150 80.0 60.0

100 ppm ppm 40.0

50 20.0

0.0 0 Control GYP BIO GYP4+4BIO Control GYP BIO GYP1+1BIO Summer42016 Summer12016

Boron Farm1D Copper Farm4D

200 100.0

150 80.0

60.0

100 a a ppm ppm ab b 40.0

50 20.0

0 0.0 Control GYP BIO GYP1+1BIO Control GYP BIO GYP4+4BIO

Summer12016 Summer42016

Boron Farm1F Copper Farm4F

200 100.0

150 80.0

60.0 100 ppm ppm 40.0 50 20.0

0 0.0 Control GYP BIO GYP1+1BIO Control GYP BIO GYP4+4BIO

Summer12016 Summer42016

64 Figure 27. Butternut squash leaf iron and manganese concentrations at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017. Shading is used to indicate relative sufficiency of nutrients; pink shading indicates excessive levels, green indicated adequacy, and yellow indicates a deficiency. Letters above data points indicate significant differences at α=0.05, LSD test. Data points that share the same letter are not significantly different.

Iron Farm3B Manganese Farm3B

500 100 400 80 300 60 ppm 200 ppm 40

100 20

0 0 Control GYP BIO GYP3+3BIO Control GYP BIO GYP3+3BIO

Summer32016 Summer32016

Iron Farm3D Manganese Farm3D

500 100 400 80 300 60 ppm 200 ppm 40

100 20

0 0 Control GYP BIO GYP3+3BIO Control GYP BIO GYP3+3BIO

Summer32016 Summer32016

Iron Farm3F Manganese Farm3F

4000 3500 100

3000 80 2500 60 2000 ppm ppm 1500 40 1000 20 500 0 0 Control GYP BIO GYP3+3BIO Control GYP BIO GYP3+3BIO

Summer32016 Summer32016

65 Figure 28. Butternut squash leaf zinc and sodium concentrations at farms A, B, C, D, and E in samples collected at the cupping growth stage in 2016 and 2017. Shading is used to indicate relative sufficiency of nutrients; pink shading indicates excessive levels, green indicated adequacy, and yellow indicates a deficiency. Letters above data points indicate significant differences at α=0.05, LSD test. Data points that share the same letter are not significantly different.

Zinc Farm3B Sodium Farm4B

140 100.0 120 80.0 100 80 60.0 ppm 60 ppm 40.0 40 20 20.0 0 0.0 Control GYP BIO GYP3+3BIO Control GYP BIO GYP4+4BIO

Summer32016 Summer42016

Zinc Farm3D Sodium Farm4D

140 100.0 120 80.0 100 80 60.0 ppm 60 ppm 40.0 40 20.0 20 0 0.0 Control GYP BIO GYP3+3BIO Control GYP BIO GYP4+4BIO

Summer32016 Summer42016

Zinc Farm3F Sodium Farm4F

140 100 120 80 100 80 60 ppm 60 ppm 40 40 20 20 0 0 Control GYP BIO GYP3+3BIO Control GYP BIO GYP4+4BIO

Summer32016 Summer42016

66 Table 6. Cabbage total and marketable yield and cabbage head weight, polar:equatorial diameter ratio and % moisture

treatment averages with standard error in parenthesis for Fall 2016.

Marketable-Heads- Weight-per- Marketable-Yield Head-Moisture- Total-Yield Head Head-Ratio- Farm Treatment (kg/ha) (kg/ha) (kg) (%) Control 47841 ( 10882 ) 41795 ( 15493 ) 1.8 ( 0.26 ) 0.95 ( 0.050 ) b 85.8 ( 1.08 ) GYP 57422 ( 5977 ) 47524 ( 12086 ) 2.1 ( 0.20 ) 0.93 ( 0.045 ) b 85.7 ( 0.90 ) A BIO 50417 ( 14906 ) 41271 ( 11955 ) 2.0 ( 0.38 ) 0.94 ( 0.049 ) b 85.2 ( 1.02 ) GYP;+;BIO 48237 ( 10681 ) 34334 ( 9755 ) 1.8 ( 0.44 ) 0.99 ( 0.046 ) a 85.6 ( 0.85 ) Control 37845 ( 3372 ) GYP 37746 ( 8423 ) B BIO 43917 ( 7806 ) GYP;+;BIO 41764 ( 6710 ) Control 25184 ( 4821 ) 18471 ( 6905 ) 1.1 ( 0.21 ) 0.98 ( 0.017 ) 86.5 ( 0.54 ) ab GYP 27206 ( 6750 ) 17551 ( 8429 ) 1.2 ( 0.19 ) 0.97 ( 0.018 ) 86.5 ( 0.51 ) ab C BIO 24303 ( 3169 ) 15921 ( 5035 ) 1.1 ( 0.07 ) 1.00 ( 0.018 ) 87.7 ( 0.51 ) a GYP;+;BIO 28930 ( 3648 ) 20975 ( 4163 ) 1.2 ( 0.28 ) 1.00 ( 0.017 ) 86.1 ( 0.51 ) b Control 28174 ( 5891 ) 20140 ( 9863 ) 1.4 ( 0.16 ) 0.96 ( 0.014 ) b 86.1 ( 0.60 ) GYP 30686 ( 6358 ) 22417 ( 12413 ) 1.5 ( 0.12 ) 0.95 ( 0.014 ) b 86.1 ( 0.40 ) D BIO 31891 ( 4005 ) 20253 ( 8316 ) 1.3 ( 0.18 ) 0.98 ( 0.014 ) ab 86.4 ( 0.78 ) GYP;+;BIO 35520 ( 1687 ) 24841 ( 3478 ) 1.5 ( 0.08 ) 1.00 ( 0.013 ) a 86.1 ( 0.59 ) Control 40467 ( 8562 ) 29516 ( 13667 ) 1.6 ( 0.26 ) 0.96 ( 0.012 ) b 86.9 ( 0.85 ) GYP 38306 ( 3737 ) 27543 ( 8802 ) 1.5 ( 0.07 ) 0.97 ( 0.012 ) ab 87.6 ( 1.38 ) E BIO 35115 ( 6273 ) 21905 ( 10022 ) 1.4 ( 0.22 ) 0.99 ( 0.012 ) ab 87.5 ( 0.79 ) GYP;+;BIO 38005 ( 3361 ) 27504 ( 6034 ) 1.5 ( 0.03 ) 1.00 ( 0.011 ) a 87.6 ( 1.65 ) Abbreviations:;GYP,;gypsum;;BIO,;biological;stimulants a:;Variables;analyzed;per;farm.;Means;with;the;same;letter;are;not;significantly;different;(P=0.05,;LSD) a:;Variables;analyzed;per;farm.;Means;with;the;same;letter;are;not;significantly;different;(P=0.05,;LSD) b:;Farm;B;planted;Grand;Vantage;and;Bronco;varieties.;Varieties;were;blocked.;Data;for;weight/head, b:;Farm;B;planted;Grand;Vantage;and;Bronco;varieties.;Varieties;were;blocked.;Data;for;weight/head, ;;;;ratio;and;moisture;are;not;reported;because;of;insufficient;degrees;of;freedom.;; ;;;;ratio;and;moisture;are;not;reported;because;of;insufficient;degrees;of;freedom.;;

67

Table 7. Butternut squash total and marketable yield, fruit number, fruit weight, length, color and brix treatment average with standard error in parenthesis for Fall 2017.

Marketable-Fruits Total-Yield Fruit-Number- Marketable-Yield Weight-per- Total-lenght- Fruit-Color- Sucrose-% Farm Treatment (kg/ha) per-5-plants (kg/ha) fruit<(kg) (cm) L a b (Brix) Control 45083 ( 12240 ) a 17 ( 3.3 ) a 34295 ( 9245 ) 1.8 ( 0.26 ) 24 ( 0.9 ) 76 ( 1.7 ) 5 ( 2.9 ) 67 ( 6.4 ) 6.4 ( 0.22 ) GYP 37755 ( 5063 ) ab 15 ( 1.8 ) ab 34540 ( 6623 ) 1.7 ( 0.18 ) 23 ( 2.0 ) 77 ( 1.3 ) 4 ( 1.3 ) 67 ( 4.2 ) 6.4 ( 0.53 ) B BIO 31803 ( 8942 ) b 12 ( 2.7 ) b 40090 ( 3309 ) 1.8 ( 0.09 ) 24 ( 0.5 ) 77 ( 0.8 ) 4 ( 1.0 ) 65 ( 1.0 ) 6.2 ( 0.15 ) GYP<+

68 Table 8. Organic matter, microbial biomass, active carbon, total mineralized carbon and protein treatment average with standard error in parenthesis, following harvest in the Fall of 2016, after amendments application in the Spring of 2017, and following harvest in the Fall of 2017.

Organic+Matter Microbial+Biomass Active+Carbon+(POXC) Total+Mineralized+C Protein+Extracted % mg$C/kg$soil mg$C/kg$soil mg/kg$soil mg/kg$soil Farm Treatment Fall+16` Fall+17` Fall+16` Fall+16` Spr+17` Fall+17` Fall+16` Spr+17` Fall+17` Fall+16` Spr+17` Fall+17` Control 1.2 ( 0.13 ) 1.3 ( 0.25 ) 197.4 ( 136.63 ) 458 ( 33.8 ) 403 ( 34.4 ) 648 ( 122.0 ) 37.0 ( 12.29 ) 42.9 ( 7.26 ) 43.5 ( 9.28 ) 5.2 ( 0.25 ) a 4.5 ( 0.49 ) 4.7 ( 0.34 ) GYP 1.2 ( 0.32 ) 1.4 ( 0.24 ) 144.9 ( 55.25 ) 470 ( 26.4 ) 391 ( 32.0 ) 603 ( 198.0 ) 40.3 ( 7.30 ) 38.0 ( 7.54 ) 43.3 ( 11.61 ) 5.0 ( 0.46 ) ab 4.5 ( 0.32 ) 4.6 ( 0.27 ) A BIO 1.2 ( 0.34 ) 1.3 ( 0.19 ) 210.7 ( 159.51 ) 427 ( 42.5 ) 392 ( 35.1 ) 544 ( 99.9 ) 34.9 ( 5.54 ) 37.6 ( 1.67 ) 42.7 ( 7.95 ) 4.9 ( 0.27 ) b 4.3 ( 0.12 ) 4.6 ( 0.15 ) GYP:+:BIO 1.2 ( 0.37 ) 1.2 ( 0.24 ) 141.4 ( 65.35 ) 464 ( 39.5 ) 427 ( 42.6 ) 554 ( 166.6 ) 42.5 ( 3.66 ) 37.0 ( 4.78 ) 46.0 ( 12.03 ) 5.0 ( 0.29 ) ab 4.5 ( 0.21 ) 4.7 ( 0.33 ) Control 1.1 ( 0.28 ) 2.0 ( 0.33 ) 233.2 ( 33.79 ) 497 ( 25.1 ) 435 ( 36.2 ) 563 ( 61.3 ) b 35.0 ( 4.66 ) 37.6 ( 4.00 ) 42.9 ( 8.09 ) 5.2 ( 0.32 ) 4.8 ( 0.38 ) 5.0 ( 0.18 ) GYP 1.4 ( 0.47 ) 1.8 ( 0.77 ) 140.0 ( 127.67 ) 511 ( 66.9 ) 463 ( 38.4 ) 752 ( 134.9 ) a 39.2 ( 12.63 ) 39.0 ( 6.37 ) 41.6 ( 5.92 ) 5.5 ( 0.71 ) 5.0 ( 0.50 ) 5.2 ( 0.44 ) B BIO 1.4 ( 0.17 ) 2.2 ( 1.28 ) 124.0 ( 63.93 ) 440 ( 11.7 ) 449 ( 38.3 ) 544 ( 34.0 ) b 34.7 ( 6.19 ) 40.6 ( 4.34 ) 40.7 ( 7.24 ) 5.0 ( 0.36 ) 5.0 ( 0.60 ) 5.0 ( 0.70 ) GYP:+:BIO 1.5 ( 0.19 ) 1.7 ( 0.08 ) 145.9 ( 25.20 ) 491 ( 82.3 ) 451 ( 58.0 ) 573 ( 57.2 ) b 30.9 ( 6.37 ) 38.2 ( 4.46 ) 37.4 ( 15.50 ) 5.1 ( 0.99 ) 4.9 ( 0.46 ) 4.8 ( 0.40 ) Control 1.5 ( 0.22 ) 2.0 ( 0.30 ) 449.4 ( 318.22 ) 649 ( 49.9 ) 549 ( 28.4 ) 594 ( 30.1 ) 32.3 ( 17.50 ) 46.4 ( 9.93 ) 43.7 ( 5.84 ) 5.0 ( 0.33 ) ab 3.3 ( 0.39 ) 4.9 ( 0.32 ) a GYP 1.8 ( 0.29 ) 1.7 ( 0.57 ) 204.9 ( 127.16 ) 641 ( 21.6 ) 533 ( 14.0 ) 563 ( 20.2 ) 26.1 ( 12.53 ) 42.2 ( 7.00 ) 44.2 ( 7.60 ) 4.8 ( 0.45 ) b 3.3 ( 0.26 ) 4.4 ( 0.19 ) b C BIO 1.8 ( 0.10 ) 1.9 ( 0.36 ) 399.7 ( 186.45 ) 672 ( 20.4 ) 571 ( 69.3 ) 643 ( 49.1 ) 27.5 ( 13.97 ) 40.7 ( 4.21 ) 40.5 ( 7.86 ) 5.0 ( 0.43 ) ab 3.5 ( 0.21 ) 4.8 ( 0.33 ) ab GYP:+:BIO 1.7 ( 0.12 ) 1.8 ( 0.48 ) 276.9 ( 152.02 ) 692 ( 69.7 ) 557 ( 36.8 ) 642 ( 101.5 ) 39.5 ( 6.49 ) 41.6 ( 6.44 ) 46.0 ( 9.18 ) 5.3 ( 0.40 ) a 3.4 ( 0.20 ) 4.8 ( 0.16 ) ab Control 1.5 ( 0.44 ) 1.6 ( 0.14 ) 147.9 ( 177.44 ) 574 ( 44.0 ) 511 ( 25.5 ) c 495 ( 46.4 ) b 30.6 ( 10.31 ) 30.9 ( 0.88 ) 37.2 ( 7.00 ) 5.1 ( 0.19 ) ab 4.9 ( 0.20 ) 4.8 ( 0.16 ) GYP 1.6 ( 0.26 ) 1.5 ( 0.14 ) 195.6 ( 54.20 ) 601 ( 45.2 ) 558 ( 9.5 ) a 527 ( 62.4 ) ab 25.0 ( 4.10 ) 26.6 ( 0.87 ) 35.5 ( 1.38 ) 5.1 ( 0.16 ) ab 4.6 ( 0.20 ) 5.0 ( 0.23 ) D BIO 1.6 ( 0.17 ) 1.6 ( 0.22 ) 134.7 ( 57.42 ) 566 ( 93.0 ) 549 ( 18.9 ) ab 591 ( 101.0 ) a 26.1 ( 5.28 ) 28.7 ( 4.84 ) 33.0 ( 2.63 ) 4.9 ( 0.15 ) b 4.9 ( 0.22 ) 4.8 ( 0.42 ) GYP:+:BIO 1.6 ( 0.31 ) 1.6 ( 0.33 ) 138.4 ( 47.21 ) 578 ( 38.4 ) 523 ( 24.4 ) bc 538 ( 12.9 ) ab 31.6 ( 5.94 ) 27.9 ( 4.53 ) 32.9 ( 6.05 ) 5.4 ( 0.20 ) a 4.8 ( 0.34 ) 4.8 ( 0.07 ) Control 1.7 ( 0.26 ) 1.7 ( 0.10 ) 118.4 ( 22.89 ) 475 ( 45.7 ) 372 ( 71.4 ) 484 ( 87.0 ) 82.7 ( 14.03 ) a 60.9 ( 11.89 ) 60.2 ( 4.46 ) 5.2 ( 0.36 ) b 3.4 ( 0.46 ) 4.5 ( 0.23 ) GYP 1.7 ( 0.29 ) 1.9 ( 0.33 ) 219.2 ( 195.67 ) 532 ( 33.1 ) 378 ( 57.5 ) 431 ( 77.2 ) 72.6 ( 6.17 ) ab 63.9 ( 11.37 ) 58.0 ( 14.95 ) 5.5 ( 0.17 ) a 3.6 ( 0.31 ) 4.8 ( 0.26 ) E BIO 1.4 ( 0.30 ) 1.7 ( 0.10 ) 156.5 ( 22.75 ) 515 ( 68.9 ) 381 ( 59.4 ) 483 ( 45.6 ) 68.4 ( 9.10 ) b 59.5 ( 10.25 ) 51.8 ( 3.33 ) 5.4 ( 0.19 ) ab 3.4 ( 0.22 ) 4.9 ( 0.33 ) GYP:+:BIO 1.8 ( 0.29 ) 1.6 ( 0.35 ) 130.8 ( 25.99 ) 487 ( 25.4 ) 370 ( 8.1 ) 453 ( 41.9 ) 56.3 ( 4.87 ) c 61.0 ( 7.76 ) 64.6 ( 3.09 ) 5.1 ( 0.22 ) b 3.4 ( 0.15 ) 4.6 ( 0.38 ) Control 1.5 ( 0.43 ) 1.4 ( 0.21 ) 287.6 ( 229.71 ) a 418 ( 33.4 ) a 290 ( 87.6 ) 502 ( 42.0 ) 41.3 ( 7.68 ) 30.4 ( 3.62 ) 46.8 ( 6.06 ) 3.5 ( 0.29 ) 2.2 ( 0.36 ) 4.0 ( 0.51 ) GYP 1.6 ( 0.14 ) 1.5 ( 0.22 ) 115.2 ( 29.65 ) b 421 ( 18.1 ) b 324 ( 80.1 ) 581 ( 92.5 ) 30.6 ( 5.52 ) 35.0 ( 3.95 ) 35.9 ( 10.70 ) 3.6 ( 0.30 ) 2.4 ( 0.34 ) 3.8 ( 0.23 ) F BIO 1.4 ( 0.21 ) 1.7 ( 0.22 ) 236.9 ( 180.17 ) ab 406 ( 30.3 ) ab 334 ( 32.0 ) 481 ( 105.9 ) 38.9 ( 4.01 ) 35.5 ( 5.58 ) 43.8 ( 14.34 ) 3.5 ( 0.29 ) 2.4 ( 0.24 ) 4.2 ( 0.46 ) GYP:+:BIO 1.5 ( 0.26 ) 1.5 ( 0.40 ) 215.8 ( 149.71 ) ab 411 ( 70.9 ) ab 302 ( 62.2 ) 563 ( 79.3 ) 34.4 ( 10.11 ) 34.5 ( 7.06 ) 42.8 ( 20.45 ) 3.6 ( 0.82 ) 2.2 ( 0.61 ) 4.1 ( 0.79 ) Abbreviations::MB,:mineral:blend;:GYP,:gypsum;:BIO,:biological:stimulants;:POXC,:Permanganate:Oxidizable:Carbon a::Variable:analyzed:each:year:separately.:Means:with:the:same:letter:are:not:significantly:different:(P=0.05,:LSD)

69

Table 9. Weeds present in soil weed seed bank samples, collected in spring 2016 at

each farm.

Weeds%Species Farms Common%Name Scientific%Name A B C D E F 4 Annual'Bluegrass Poa$annua x x x x 1 Annual'Fleabane Erigeron$annuus x 1 Barnyardgrass Echinochloa$crus0galli x 4 Broadleaf'Plantain Plantago$major x x x x 1 Canada'Thistle Cirsium$arvense x 1 Carpetweed Mollugo$verticillata x 1 Common'Dandelion Taraxacum$officinale x 3 Common'Lambsquarter Chenopodium$album x x x 1 Common'Purslane Portulaca$oleracea x 3 Common'Ragweed Ambrosia$artemisiifolia x x x 1 Curly'Dock Rumex$crispus x 2 Eastern'Black'Nightshade Solanum$ptycanthum x x 5 Green'Foxtail Setaria$viridis x x x x x 1 Hairy'Bittercress Cardamine$hirsuta x 3 Hairy'Galinsoga Galinsoga$ciliata x x x 1 Healall Prunella$vulgaris x 2 Large'Crabgrass Digitaria$sanguinalis x x 2 Mouse'ear'Chickweed Cerastium$vulgatum x x 5 Oxalis Oxalis$spp. x x x x x 4 Pennsylvania'smartweed Polygonum$pensylvanicum x x x x 3 Pennycress Thlaspi$arvense x x x 2 Purslane'Speedwell Veronica$peregrina x x 2 Red'root'Pigweed Amaranthus$retroflexus x x 3 Smooth'Crabgrass Digitaria$ischaemum x x x 2 Venice'Mallow Hibiscus$trionum$L. x x 4 Virginia'Copperleaf Acalypha$virginica x x x x 4 White'Clover Trifolium$repens x x x x 3 Wild'Raspberry Rubus$idaeus x x x 3 Yellow'Foxtail Setaria$glauca x x x

70

Figure 29. Weeds field counts at the same plot. First weed count (A) and 3 weeks later second weed count (B). Representative of the overall weed management at the farms.

A B

Figure 30. Baseline weed density per farm, from Spring 2016 soil samples. Letters above columns indicate significant differences at α=0.05, LSD test. Columns that share the same letter are not significantly different.

90 ) 2 a

m 80 70 ab 60 50 bc bc 40 c 30 c 20 10 0 Total&Weed&Density&(plants/ A B C D E F Farms

71 Figure 31. Baseline density of broadleaf and grass weeds per farm, from Spring

2016 soil samples. Letters above columns indicate significant differences at α=0.05,

LSD test. Columns that share the same letter are not significantly different.

90 ) 2 80 m 70 a 60 a 50 40 Broadleaf b 30 b Grasses 20 b b

Weed$Density$(plants/ 10 0 A B C D E F Farms

Figure 32. Treatment effect on weed density at Farm B, based on soil weed seed bank samples collected in Spring 2017. Letters above columns indicate significant differences at α=0.05, LSD test. Columns that share the same letter are not significantly different.

90 80 a a 70 )

2 60 ab m 50 b 40

(plants/ 30 20 Total&Weeds&Density& 10 0 MB MB+GYP MB+BIO MB+GYP+BIO Treatment

72 Conclusions

Differences in soils, crops and weed communities were mainly a factor of the farm. Even though the soils were relatively similar in many respects, their previous management influenced the outcomes during this short, two-year study. Three farms were deficient in potassium from a

SLAN perspective and only at Farm B was potassium base saturation in the optimum range.

Phosphorus concentration was deficient for optimum plant growth at all but two farms and the amount applied in experimental treatments was inadequate to address those deficiencies.

Detecting treatment differences was further complicated because all farms were essentially balanced respecting calcium base saturation and nutrient differences applied in the three treatments were modest. For example the cabbage crop harvested in 2016 withdrew greater quantities of calcium than any of the treatments supplied, with the exception of the MB at Farm

D. Overall, the data tend to support a primary conclusion of previous students of BCSR, such as

Eckert, who had concluded that achieving the ideal saturations would result in unneeded and uneconomic applications of base cations. Calcium at Farm D illustrates nicely that generalization. At 57% base saturation in the spring of 2016, it was in the excessive range respecting adequacy for plant nutrition. Excessive levels of calcium at all of the farms may have contributed to the general deficiency in potassium. A small number of statistically significant treatment effects were noted throughout the data sets; however, with few exceptions those effects were not supported by any theoretical framework for how the nutrient(s) involved would impact soils or crops. Exceptions to that generalization included increases in magnesium and sulfur concentration in the soils, respectively, when Epsom salts or gypsum were applied. Zinc

73 inclusion in the BIO treatment also was reflected positively in soil concentration at several farms.

Overall, excessive application of amendments containing calcium, magnesium or potassium appear to have created unfavorable amounts of cations in the soil exchange site for the crop growth. An explicit example is calcium. Fageria & Baligar (1999) noticed that the uptake of

K, Mg, Cu, Mn, Fe and B in dry bean was significantly affected when Ca concentration in the growing medium was high. As previously mentioned, the replacement and exchange of cations in the soil, are greatly influenced by each cations adsorption strength. Calcium has the greatest adsorption strength, followed by magnesium and then potassium.

Similar to the observations respecting soil nutrient base saturation and absolute concentrations there were few measured effects on soil biology. Of those that were significant it was generally not obvious the cause and effect relationships. Differences in biology were mostly related to the farm, a factor that was highly significant, and no doubt reflected management prior to the start of this research. Moreover, the small amount of microorganisms included in the BIO treatment was unlikely to cause any effect in the local microorganism community present at each field before conduct research. It is also important to recall that even during the conduct of these experiments the farmers independently added a number of differing treatments, many of which may have affected not only soil biology but the overall soil health.

Crop measurements were mainly influenced by each farm historical management and farmers’ agricultural practices for each crop. For example, butternut squash total and marketable yields at Farm F was extremely low compared to Farm B and D. Yield differences were with no doubt due to the insect pressure and a soil-borne fungus Fusarium oxysporum present at farm F.

Crop quality measurements gave some positive results regarding cabbage head and butternut

74 squash traits; however, they were not necessarily of biological or agronomic significance.

Nutrient supply influence both yield and crop quality. Moreover, Marschner & Marschner (2012) described that maximum quality of the crop can be obtained either before or after the crop reaches the maximum dry or fresh matter yield. The authors mention, that a synchronized pattern between yield and quality is rather an exception. For instance, to achieve wheat high in gluten content for baking purposes, maximum quality will require a higher nutrient supply than the recommended for maximum yield. In contrast, when maximum yields have been achieved by an intensive nitrogen supply, nutritional compounds can be diminished (Marschner & Marschner,

2012).

The farmers involved in this project were very effective weed managers. Weed speciation in all of the fields was impoverished relative to most Ohio crop fields that have been assessed and overall weed densities were low. The farmers all practiced intensive physical control methods (tillage, cultivation and weeding by hand), and most made use of plastic mulch. It is noteworthy that grass weeds were infrequent in keeping with a finding of Kelling et al (1996), and the anecdotal observation of many farmers that grass weeds in particular decline in balanced soils. Total weed density was lower in gypsum amended plots at Farm B. This finding is in keeping with the reported observations of soil balancers who sometimes sprinkle gypsum directly over the seed line to help reduce weed intensity. However, it would be reasonable to have observed some impact of gypsum on the soils at Farm B and none was apparent, except an overall decline in magnesium concentration over the two years of the research.

Overall these experiments failed to detect major trends in modifying soils, crops or weeds following the use of soil balancing treatments, or provide support of efforts to do so.

Nevertheless, the sites and treatments applied had limitations that may have reduced the

75 probability of detecting changes due to treatments; all sites were already nearly in balance according to BCSR guidelines, and differences in nutrient levels between treatments were very modest and probably insufficient to induce change especially in short time. The impact of uncertain and poorly defined farmer practices may also have played a role in masking treatment effects.

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83 Appendices: Tables

Table 10. Average soil calcium, magnesium and potassium content with standard errors in parenthesis. Soil samples were collected in Spring 2016 prior to treatment, and after harvest in fall of 2016 and 2017.

Calcium Magnesium Potassium % ppm % ppm % ppm % ppm % ppm % ppm Farm Treatment Fall*16` Fall*17` Fall*16` Fall*17` Fall*16` Fall*17` Control 69 ( 5.9 ) 1414 ( 113 ) 66 ( 4.3 ) 1321 ( 109 ) 19 ( 2.8 ) 195 ( 24.4 ) 17 ( 0.4 ) 173 ( 12.8 ) 2.9 ( 0.44 ) 101 ( 10.3 ) ab 2.1 ( 0.05 ) a 72 ( 6.2 ) ab GYP 67 ( 5.3 ) 1400 ( 98 ) 66 ( 1.8 ) 1305 ( 52 ) 17 ( 1.6 ) 180 ( 28.0 ) 16 ( 1.1 ) 162 ( 8.7 ) 2.5 ( 0.38 ) 89 ( 11.4 ) b 2.0 ( 0.18 ) ab 69 ( 7.6 ) ab A BIO 66 ( 3.8 ) 1492 ( 59 ) 66 ( 1.3 ) 1252 ( 70 ) 17 ( 0.6 ) 196 ( 11.1 ) 16 ( 0.8 ) 158 ( 14.5 ) 2.4 ( 0.25 ) 93 ( 8.6 ) ab 1.9 ( 0.19 ) b 63 ( 9.4 ) b GYP:+:BIO 66 ( 4.8 ) 1443 ( 90 ) 66 ( 1.1 ) 1360 ( 65 ) 17 ( 1.3 ) 194 ( 24.5 ) 16 ( 1.5 ) 171 ( 23.2 ) 2.8 ( 0.21 ) 105 ( 7.6 ) a 2.0 ( 0.15 ) ab 73 ( 5.2 ) a Control 67 ( 4.7 ) 1373 ( 103 ) b 63 ( 2.4 ) 1617 ( 175 ) 19 ( 0.4 ) 202 ( 23.2 ) 16 ( 0.6 ) 213 ( 29.6 ) 1.9 ( 0.30 ) 70 ( 16.8 ) 2.8 ( 0.49 ) b 127 ( 38.0 ) GYP 71 ( 5.3 ) 1495 ( 153 ) a 63 ( 3.1 ) 1616 ( 226 ) 18 ( 1.9 ) 190 ( 6.6 ) 15 ( 1.6 ) 199 ( 14.4 ) 2.0 ( 0.33 ) 75 ( 18.8 ) 2.6 ( 0.45 ) b 115 ( 18.8 ) B BIO 65 ( 4.4 ) 1368 ( 126 ) b 64 ( 7.6 ) 1573 ( 70 ) 18 ( 1.0 ) 195 ( 11.4 ) 17 ( 3.3 ) 212 ( 12.9 ) 1.9 ( 0.40 ) 70 ( 14.9 ) 3.2 ( 0.35 ) a 140 ( 16.2 ) GYP:+:BIO 70 ( 3.2 ) 1505 ( 145 ) a 63 ( 1.2 ) 1635 ( 233 ) 17 ( 1.7 ) 188 ( 23.8 ) 15 ( 0.4 ) 200 ( 29.9 ) 2.0 ( 0.40 ) 75 ( 19.4 ) 3.2 ( 0.63 ) a 146 ( 41.3 ) Control 83 ( 7.1 ) b 2101 ( 337 ) 82 ( 7.8 ) 2334 ( 459 ) 11 ( 0.4 ) 142 ( 15.4 ) 11 ( 1.2 ) 156 ( 19.9 ) b 1.2 ( 0.15 ) 53 ( 6.9 ) 1.6 ( 0.17 ) 82 ( 15.2 ) GYP 87 ( 2.3 ) ab 2430 ( 279 ) 84 ( 5.4 ) 2384 ( 437 ) 10 ( 0.3 ) 147 ( 18.0 ) 11 ( 0.8 ) 157 ( 13.1 ) b 1.1 ( 0.18 ) 52 ( 3.5 ) 1.6 ( 0.46 ) 79 ( 18.7 ) C BIO 88 ( 1.4 ) a 2375 ( 675 ) 82 ( 9.5 ) 2504 ( 760 ) 11 ( 1.3 ) 149 ( 22.6 ) 11 ( 1.4 ) 163 ( 12.7 ) ab 1.1 ( 0.21 ) 48 ( 6.3 ) 1.6 ( 0.30 ) 81 ( 13.5 ) GYP:+:BIO 85 ( 4.3 ) ab 2575 ( 1272 ) 80 ( 10.2 ) 2665 ( 1142 ) 11 ( 1.0 ) 155 ( 47.5 ) 11 ( 1.5 ) 176 ( 28.6 ) a 1.2 ( 0.21 ) 58 ( 13.0 ) 1.6 ( 0.32 ) 86 ( 10.0 ) Control 78 ( 0.5 ) 1582 ( 65 ) 71 ( 2.0 ) 1423 ( 70 ) 20 ( 0.6 ) 205 ( 6.6 ) 18 ( 0.2 ) 186 ( 12.0 ) 2.2 ( 0.13 ) ab 76 ( 7.9 ) ab 2.3 ( 0.14 ) a 80 ( 7.3 ) GYP 76 ( 3.7 ) 1674 ( 133 ) 71 ( 2.7 ) 1509 ( 131 ) 20 ( 1.9 ) 222 ( 38.8 ) 19 ( 1.2 ) 207 ( 35.4 ) 1.8 ( 0.10 ) b 70 ( 7.4 ) ab 2.0 ( 0.17 ) b 73 ( 7.5 ) D BIO 77 ( 1.0 ) 1616 ( 132 ) 71 ( 1.5 ) 1488 ( 124 ) 20 ( 1.3 ) 218 ( 26.1 ) 19 ( 1.4 ) 206 ( 28.7 ) 2.3 ( 0.45 ) a 83 ( 11.2 ) a 2.2 ( 0.19 ) ab 79 ( 5.1 ) GYP:+:BIO 77 ( 3.5 ) 1591 ( 116 ) 73 ( 5.6 ) 1475 ( 152 ) 19 ( 1.3 ) 199 ( 18.4 ) 18 ( 1.1 ) 181 ( 10.7 ) 1.9 ( 0.23 ) b 69 ( 10.7 ) b 2.0 ( 0.25 ) b 70 ( 13.6 ) Control 65 ( 0.6 ) ab 1556 ( 74 ) 60 ( 3.7 ) 1637 ( 49 ) 20 ( 0.9 ) 240 ( 21.1 ) 18 ( 0.3 ) 248 ( 17.7 ) 2.3 ( 0.13 ) b 96 ( 1.5 ) b 2.4 ( 0.31 ) 112 ( 7.7 ) GYP 69 ( 5.2 ) a 1614 ( 143 ) 61 ( 2.7 ) 1619 ( 103 ) 22 ( 2.6 ) 257 ( 17.7 ) 17 ( 0.3 ) 232 ( 17.1 ) 2.8 ( 0.13 ) a 112 ( 6.9 ) a 2.3 ( 0.06 ) 104 ( 6.5 ) E BIO 57 ( 3.7 ) b 1469 ( 196 ) 60 ( 6.0 ) 1598 ( 110 ) 19 ( 1.3 ) 252 ( 43.7 ) 18 ( 1.6 ) 248 ( 26.1 ) 2.2 ( 0.26 ) b 96 ( 12.2 ) b 2.3 ( 0.16 ) 108 ( 6.5 ) GYP:+:BIO 64 ( 7.2 ) ab 1526 ( 187 ) 61 ( 2.5 ) 1598 ( 111 ) 21 ( 2.4 ) 253 ( 28.5 ) 18 ( 1.1 ) 238 ( 18.2 ) 2.3 ( 0.25 ) b 97 ( 11.1 ) b 2.2 ( 0.00 ) 99 ( 8.8 ) Control 68 ( 4.8 ) 1944 ( 189 ) 65 ( 4.6 ) 1664 ( 128 ) 16 ( 0.7 ) 232 ( 31.7 ) 15 ( 1.1 ) 190 ( 18.9 ) 2.2 ( 0.31 ) 107 ( 9.5 ) 2.4 ( 0.10 ) 106 ( 6.6 ) GYP 75 ( 4.7 ) 2200 ( 215 ) 65 ( 3.2 ) 1831 ( 105 ) 19 ( 2.4 ) 279 ( 43.5 ) 15 ( 0.6 ) 220 ( 19.8 ) 2.3 ( 0.35 ) 116 ( 22.9 ) 2.3 ( 0.13 ) 112 ( 8.1 ) F BIO 66 ( 7.7 ) 2110 ( 251 ) 66 ( 3.8 ) 1829 ( 125 ) 17 ( 2.7 ) 272 ( 35.5 ) 15 ( 0.2 ) 215 ( 20.3 ) 2.1 ( 0.40 ) 115 ( 12.4 ) 2.4 ( 0.10 ) 117 ( 15.6 ) GYP:+:BIO 66 ( 6.4 ) 1908 ( 206 ) 67 ( 4.4 ) 1782 ( 195 ) 17 ( 4.0 ) 252 ( 64.5 ) 16 ( 1.4 ) 219 ( 49.7 ) 1.9 ( 0.29 ) 97 ( 18.5 ) 2.3 ( 0.18 ) 106 ( 8.8 ) Abbreviations::MB,:mineral:blend;:GYP,:gypsum;:BIO,:biological:stimulants;:D,:deficient;:A,:adequate;:E,:excessive a::Variable:analyzed:each:year:separately.:Means:with:the:same:letter:are:not:significantly:different:(P=0.05,:LSD) b::Recommended:base:saturation:according:to:BCSR::Ca:(65[85);:Mg:(10[20);:K:(2[5) c::Recommended:mineral:levels:for:Mehlich:3::(D,A,E:)::Ca:(<700,:701[895,:>895);:Mg:(<71,:72[147,:>147);::K:(<72,:73[138,:>138).::Source:Maynard:et:al.:(2007).

84 Table 11. Average soil pH, CEC, phosphorus, and sulfur content with standard error in parenthesis. Soil samples were collected in Spring 2016 prior to treatment, and after harvest in fall of 2016 and 2017.

CEC2 Phosphorus Sulfur pH cmol/kg!soil !ppm !ppm Farm Treatment Fall216` Fall217` Fall216` Fall217` Fall216` Fall217` Fall216` Fall217` Control 6.8 ( 0.37 ) 6.9 ( 0.19 ) 7.7 ( 0.65 ) b 7.5 ( 0.46 ) 36 ( 7.2 ) ab 28 ( 2.9 ) 27 ( 19.1 ) 55 ( 8.4 ) GYP 6.9 ( 0.22 ) 6.9 ( 0.13 ) 7.8 ( 0.73 ) ab 7.4 ( 0.36 ) 38 ( 5.5 ) ab 29 ( 4.1 ) 29 ( 10.9 ) 77 ( 13.1 ) A BIO 6.9 ( 0.20 ) 6.8 ( 0.05 ) 8.5 ( 0.22 ) a 7.2 ( 0.52 ) 40 ( 8.2 ) a 29 ( 1.2 ) 38 ( 9.7 ) 65 ( 15.6 ) GYP:+:BIO 6.9 ( 0.26 ) 6.8 ( 0.10 ) 8.3 ( 0.73 ) ab 7.7 ( 0.49 ) 35 ( 7.1 ) b 31 ( 4.6 ) 27 ( 17.2 ) 83 ( 26.4 ) Control 7.0 ( 0.16 ) 6.6 ( 0.16 ) ab 7.7 ( 0.81 ) 9.7 ( 1.34 ) 75 ( 3.8 ) 76 ( 5.7 ) 11 ( 1.8 ) b 67 ( 15.6 ) b GYP 7.1 ( 0.20 ) 6.7 ( 0.17 ) ab 8.0 ( 0.90 ) 9.6 ( 0.98 ) 106 ( 44.6 ) 102 ( 43.1 ) 23 ( 10.0 ) ab 89 ( 16.1 ) ab B BIO 6.9 ( 0.15 ) 6.5 ( 0.05 ) b 7.9 ( 0.39 ) 9.4 ( 1.16 ) 76 ( 30.2 ) 83 ( 39.6 ) 12 ( 2.1 ) b 86 ( 5.0 ) ab GYP:+:BIO 7.1 ( 0.06 ) 6.7 ( 0.08 ) a 8.1 ( 0.79 ) 9.7 ( 1.44 ) 92 ( 43.1 ) 91 ( 39.7 ) 38 ( 18.5 ) a 106 ( 32.5 ) a Control 7.3 ( 0.32 ) b 7.2 ( 0.26 ) 9.5 ( 1.12 ) 10.7 ( 1.60 ) 44 ( 5.1 ) 46 ( 7.5 ) 21 ( 2.6 ) b 89 ( 44.2 ) GYP 7.5 ( 0.17 ) ab 7.3 ( 0.18 ) 10.4 ( 1.01 ) 10.6 ( 1.38 ) 45 ( 10.6 ) 41 ( 4.3 ) 43 ( 13.5 ) ab 77 ( 7.0 ) C BIO 7.5 ( 0.16 ) a 7.3 ( 0.45 ) 10.1 ( 2.72 ) 11.4 ( 2.47 ) 43 ( 4.8 ) 48 ( 8.7 ) 26 ( 7.4 ) ab 59 ( 52.2 ) GYP:+:BIO 7.3 ( 0.22 ) b 7.2 ( 0.40 ) 10.8 ( 4.09 ) 12.0 ( 3.43 ) 47 ( 10.4 ) 52 ( 5.9 ) 55 ( 35.9 ) a 66 ( 36.4 ) Control 7.4 ( 0.05 ) 7.1 ( 0.05 ) 7.6 ( 0.29 ) 7.5 ( 0.54 ) 16 ( 1.4 ) 16 ( 0.5 ) 12 ( 2.4 ) 51 ( 13.8 ) GYP 7.3 ( 0.14 ) 7.1 ( 0.05 ) 8.3 ( 0.93 ) 8.0 ( 0.93 ) 18 ( 6.0 ) 17 ( 2.6 ) 21 ( 10.5 ) 53 ( 5.1 ) D BIO 7.3 ( 0.05 ) 7.1 ( 0.05 ) 7.8 ( 0.63 ) 7.9 ( 0.71 ) 16 ( 2.1 ) 16 ( 1.8 ) 18 ( 6.5 ) 52 ( 9.9 ) GYP:+:BIO 7.3 ( 0.13 ) 7.1 ( 0.15 ) 7.8 ( 0.70 ) 7.6 ( 0.64 ) 17 ( 2.0 ) 17 ( 1.8 ) 23 ( 20.7 ) 61 ( 13.1 ) Control 6.0 ( 0.05 ) 6.5 ( 0.13 ) 9.0 ( 0.41 ) 10.2 ( 0.83 ) 14 ( 1.0 ) 18 ( 5.6 ) 40 ( 11.2 ) b 32 ( 2.9 ) GYP 6.0 ( 0.17 ) 6.6 ( 0.15 ) 8.8 ( 0.84 ) 10.0 ( 0.55 ) 19 ( 9.5 ) 15 ( 2.1 ) 76 ( 27.5 ) a 35 ( 7.7 ) E BIO 5.9 ( 0.42 ) 6.5 ( 0.26 ) 9.7 ( 1.48 ) 10.2 ( 1.36 ) 16 ( 2.2 ) 18 ( 3.5 ) 33 ( 3.7 ) b 33 ( 8.9 ) GYP:+:BIO 5.9 ( 0.13 ) 6.6 ( 0.13 ) 9.0 ( 1.40 ) 9.8 ( 0.96 ) 12 ( 1.9 ) 13 ( 2.1 ) 50 ( 10.1 ) ab 38 ( 4.8 ) Control 6.6 ( 0.50 ) 6.8 ( 0.25 ) 10.8 ( 1.36 ) 9.6 ( 0.55 ) 28 ( 10.5 ) ab 37 ( 9.0 ) 61 ( 15.4 ) b 44 ( 6.3 ) b GYP 6.4 ( 0.34 ) 6.8 ( 0.19 ) 11.0 ( 0.65 ) 10.6 ( 0.87 ) 22 ( 5.9 ) b 36 ( 5.9 ) 111 ( 18.7 ) a 64 ( 22.4 ) a F BIO 6.6 ( 0.17 ) 6.8 ( 0.22 ) 12.1 ( 2.45 ) 10.4 ( 0.92 ) 41 ( 15.7 ) a 44 ( 5.0 ) 68 ( 16.3 ) b 42 ( 3.7 ) b GYP:+:BIO 6.6 ( 0.25 ) 6.9 ( 0.17 ) 10.8 ( 1.08 ) 10.0 ( 1.51 ) 22 ( 3.2 ) b 38 ( 3.9 ) 67 ( 15.5 ) b 56 ( 3.7 ) ab Abbreviations::MB,:mineral:blend;:GYP,:gypsum;:BIO,:biological:stimulants;:CEC,:cation:exchange:capacity;:D,:deficient;:A,:adequate;:E,:excessive a::Variable:analyzed:each:year:separately.:Means:with:the:same:letter:are:not:significantly:different:(P=0.05,:LSD) b::Recommended:values::pH:(6.5\7.2);:OM:(1\6) c::Recommended:mineral:levels:for:Mehlich:3::(D,A,E)::P:(:<35,:36\68,:>68);:(D,A,E)::S:(0\6,:7\12,:>12):Source:Maynard:et:al.:(2007)

85 Table 12. Average soil boron, copper, iron, manganese and zinc content with standard error in parenthesis. Soil samples were collected in Spring 2016 prior to treatment, and after harvest in fall of 2016 and 2017.

-----Boron- Copper Iron- Manganese- Zinc !ppm !ppm !ppm !ppm !ppm Farm Treatment Fall-16` Fall-17` Fall-16` Fall-17` Fall-16` Fall-17` Fall-16` Fall-17` Fall-16` Fall-17` Control 1.2 ( 0.25 ) 1.2 ( 0.15 ) 3.7 ( 0.84 ) a 2.7 ( 0.33 ) ab 150 ( 8.3 ) 163 ( 11.0 ) a 99 ( 7.3 ) 96 ( 2.2 ) b 9.8 ( 1.52 ) a 7.5 ( 1.53 ) GYP 1.1 ( 0.16 ) 1.1 ( 0.12 ) 2.7 ( 0.52 ) b 2.4 ( 0.24 ) b 143 ( 6.1 ) 154 ( 9.1 ) ab 107 ( 11.4 ) 112 ( 13.3 ) a 6.6 ( 0.84 ) b 7.0 ( 2.00 ) A BIO 1.2 ( 0.22 ) 1.1 ( 0.13 ) 2.8 ( 0.39 ) b 3.0 ( 0.26 ) a 142 ( 8.4 ) 150 ( 10.1 ) b 110 ( 13.2 ) 109 ( 11.5 ) ab 5.6 ( 1.10 ) b 7.3 ( 1.87 ) GYP:+:BIO 1.2 ( 0.10 ) 1.3 ( 0.13 ) 3.0 ( 0.37 ) ab 3.1 ( 0.72 ) a 145 ( 10.7 ) 155 ( 6.8 ) ab 108 ( 8.5 ) 111 ( 11.2 ) a 6.3 ( 1.16 ) b 8.0 ( 1.39 ) Control 0.7 ( 0.12 ) 1.4 ( 0.30 ) ab 3.1 ( 0.41 ) 4.2 ( 0.54 ) 129 ( 10.7 ) 168 ( 8.1 ) 94 ( 10.4 ) 126 ( 11.9 ) 3.8 ( 0.74 ) 4.2 ( 0.52 ) GYP 0.8 ( 0.19 ) 1.3 ( 0.22 ) b 3.0 ( 0.69 ) 4.4 ( 1.73 ) 132 ( 15.7 ) 162 ( 22.6 ) 96 ( 6.1 ) 116 ( 16.8 ) 4.7 ( 1.15 ) 4.9 ( 1.08 ) B BIO 0.9 ( 0.13 ) 1.7 ( 0.26 ) a 3.2 ( 0.48 ) 5.1 ( 0.83 ) 125 ( 13.3 ) 165 ( 15.0 ) 84 ( 7.7 ) 111 ( 11.7 ) 3.8 ( 1.06 ) 5.0 ( 1.63 ) GYP:+:BIO 0.8 ( 0.13 ) 1.7 ( 0.28 ) ab 3.3 ( 0.66 ) 4.9 ( 1.01 ) 131 ( 13.1 ) 158 ( 12.0 ) 93 ( 8.8 ) 115 ( 23.1 ) 4.8 ( 1.44 ) 5.3 ( 0.70 ) Control 0.7 ( 0.13 ) 1.7 ( 0.36 ) 3.4 ( 0.62 ) 8.2 ( 2.17 ) a 150 ( 33.4 ) 190 ( 32.5 ) 69 ( 10.6 ) 110 ( 10.4 ) a 5.5 ( 2.33 ) 7.2 ( 1.89 ) GYP 0.8 ( 0.06 ) 1.8 ( 0.18 ) 3.3 ( 0.51 ) 6.0 ( 1.47 ) b 136 ( 5.8 ) 173 ( 16.5 ) 73 ( 3.6 ) 94 ( 9.9 ) b 5.9 ( 1.17 ) 6.6 ( 1.53 ) C BIO 0.9 ( 0.32 ) 1.7 ( 0.30 ) 3.8 ( 0.49 ) 6.7 ( 0.95 ) ab 153 ( 30.6 ) 198 ( 32.0 ) 75 ( 9.3 ) 94 ( 10.1 ) b 5.0 ( 0.98 ) 7.5 ( 1.59 ) GYP:+:BIO 0.9 ( 0.29 ) 1.6 ( 0.29 ) 3.5 ( 0.90 ) 6.0 ( 0.85 ) b 147 ( 19.3 ) 193 ( 21.8 ) 70 ( 10.4 ) 92 ( 16.8 ) b 5.7 ( 1.83 ) 8.0 ( 1.85 ) Control 0.8 ( 0.17 ) 2.0 ( 0.31 ) 3.5 ( 1.13 ) 2.5 ( 0.16 ) 97 ( 3.0 ) 101 ( 4.1 ) 127 ( 1.7 ) 119 ( 4.6 ) 5.5 ( 3.08 ) 5.1 ( 0.73 ) b GYP 1.0 ( 0.40 ) 1.9 ( 0.19 ) 3.9 ( 1.11 ) 3.0 ( 0.64 ) 102 ( 3.5 ) 101 ( 4.7 ) 132 ( 5.6 ) 122 ( 3.8 ) 6.1 ( 1.80 ) 5.7 ( 1.07 ) ab D BIO 1.2 ( 0.10 ) 2.0 ( 0.10 ) 3.9 ( 0.67 ) 3.0 ( 0.73 ) 97 ( 3.5 ) 100 ( 3.1 ) 129 ( 8.8 ) 122 ( 9.5 ) 8.2 ( 1.43 ) 6.6 ( 0.81 ) a GYP:+:BIO 1.0 ( 0.42 ) 1.9 ( 0.21 ) 3.0 ( 0.72 ) 2.5 ( 0.28 ) 98 ( 5.0 ) 100 ( 1.3 ) 126 ( 7.4 ) 123 ( 12.9 ) 7.0 ( 2.54 ) 6.6 ( 0.49 ) a Control 1.1 ( 0.25 ) b 0.8 ( 0.00 ) 2.8 ( 0.26 ) 2.6 ( 0.21 ) b 148 ( 13.3 ) 219 ( 29.4 ) a 44 ( 5.7 ) 50 ( 6.5 ) 2.6 ( 0.15 ) 2.6 ( 0.50 ) b GYP 1.3 ( 0.19 ) a 0.8 ( 0.08 ) 2.8 ( 0.92 ) 2.8 ( 0.47 ) ab 152 ( 11.5 ) 194 ( 15.5 ) ab 46 ( 5.0 ) 53 ( 7.6 ) 3.4 ( 1.36 ) 3.3 ( 0.71 ) ab E BIO 0.8 ( 0.13 ) c 0.8 ( 0.05 ) 2.7 ( 0.37 ) 3.0 ( 0.24 ) ab 144 ( 20.7 ) 192 ( 32.9 ) ab 46 ( 9.8 ) 51 ( 11.5 ) 3.4 ( 0.57 ) 3.8 ( 0.47 ) a GYP:+:BIO 0.9 ( 0.05 ) bc 0.8 ( 0.10 ) 3.4 ( 0.25 ) 3.1 ( 0.26 ) a 145 ( 5.8 ) 183 ( 25.9 ) b 46 ( 7.9 ) 52 ( 10.2 ) 3.2 ( 0.24 ) 3.1 ( 0.36 ) ab Control 1.2 ( 0.13 ) 1.0 ( 0.14 ) 2.3 ( 0.21 ) 3.1 ( 0.19 ) 130 ( 10.9 ) 149 ( 10.8 ) 53 ( 8.4 ) 60 ( 3.9 ) 2.9 ( 0.35 ) ab 3.8 ( 0.21 ) b GYP 1.2 ( 0.22 ) 1.0 ( 0.19 ) 2.5 ( 0.34 ) 3.3 ( 0.53 ) 138 ( 9.4 ) 151 ( 12.2 ) 53 ( 6.9 ) 54 ( 9.0 ) 2.8 ( 0.52 ) ab 4.3 ( 0.79 ) ab F BIO 1.1 ( 0.08 ) 1.1 ( 0.06 ) 2.2 ( 0.29 ) 3.1 ( 0.59 ) 131 ( 4.6 ) 154 ( 10.5 ) 53 ( 7.6 ) 57 ( 7.7 ) 3.3 ( 0.64 ) a 5.2 ( 0.90 ) a GYP:+:BIO 1.0 ( 0.22 ) 1.0 ( 0.15 ) 2.1 ( 0.45 ) 3.1 ( 0.25 ) 133 ( 11.8 ) 153 ( 12.2 ) 54 ( 8.2 ) 57 ( 11.2 ) 2.3 ( 0.36 ) b 4.2 ( 0.26 ) ab Abbreviations::MB,:mineral:blend;:GYP,:gypsum;:BIO,:biological:stimulants;:CEC,:cation:exchange:capacity;:D,:deficient;:E,:excessive a::Variable:analyzed:each:year:separately.:Means:with:the:same:letter:are:not:significantly:different:(P=0.05,:LSD) b::Recommended:mineral:levels:for:Mehlich:3::(D,E)::B:(<0.5,:>20);:Cu:(<0.5,:>20);:Fe:(<50,:>100);:Mn:(<25,:>100);:Zn:(<10,:>50):Source:Maynard:et:al.:(2007),:Rutgers:New:Jersey:Agricultural:Experiment:Extension:(Heckman:et:al.,:2003)

86 Table 13. Average nitrogen, calcium, magnesium and potassium content in cabbage foliage (cupping stage) and whole heads, with standard error in parentheses, after harvest in Fall 2016.

Nitrogen-(%) a, b, c Calcium-(%) a, b, c Magnesium-(%)a, b, c Potassium-(%) a, b, c Farm Treatment Leaves Head Leaves Head Leaves Head Leaves Head Control 3.9 ( 0.12 ) 0.18 ( 0.011 ) ab 3.9 ( 0.31 ) 0.12 ( 0.003 ) 0.5 ( 0.02 ) 0.02 ( 0.003 ) 1.5 ( 0.09 ) a 0.16 ( 0.010 ) a A GYP 4.0 ( 0.07 ) 0.17 ( 0.008 ) b 4.2 ( 0.51 ) 0.11 ( 0.003 ) 0.5 ( 0.05 ) 0.01 ( 0.003 ) 1.4 ( 0.09 ) ab 0.14 ( 0.003 ) b BIO 3.9 ( 0.07 ) 0.20 ( 0.009 ) ab 4.3 ( 0.31 ) 0.12 ( 0.004 ) 0.6 ( 0.03 ) 0.02 ( 0.003 ) 1.3 ( 0.13 ) b 0.14 ( 0.009 ) b GYP:+:BIO 4.0 ( 0.12 ) 0.20 ( 0.011 ) a 4.4 ( 0.42 ) 0.11 ( 0.004 ) 0.6 ( 0.03 ) 0.02 ( 0.003 ) 1.5 ( 0.13 ) a 0.15 ( 0.009 ) ab Control 4.8 0.16 ( 0.027 ) 2.2 0.10 ( 0.004 ) 0.4 0.01 ( 0.003 ) 2.4 0.16 ( 0.014 ) B GYP 4.9 0.19 ( 0.012 ) 2.4 0.10 ( 0.000 ) 0.4 0.01 ( 0.000 ) 2.3 0.17 ( 0.016 ) BIO 5.0 0.18 ( 0.019 ) 1.8 0.11 ( 0.003 ) 0.3 0.02 ( 0.003 ) 2.4 0.17 ( 0.006 ) GYP:+:BIO 5.2 0.19 ( 0.030 ) 2.3 0.11 ( 0.006 ) 0.3 0.02 ( 0.003 ) 2.3 0.17 ( 0.014 ) Control 4.2 0.19 ( 0.016 ) 2.2 0.15 ( 0.005 ) 0.3 0.02 ( 0.000 ) 2.1 0.19 ( 0.009 ) C GYP 3.9 0.18 ( 0.006 ) 2.1 0.14 ( 0.005 ) 0.2 0.02 ( 0.000 ) 2.2 0.20 ( 0.003 ) BIO 4.4 0.18 ( 0.005 ) 2.0 0.15 ( 0.014 ) 0.3 0.02 ( 0.000 ) 2.3 0.19 ( 0.015 ) GYP:+:BIO 4.2 0.20 ( 0.016 ) 2.0 0.14 ( 0.008 ) 0.2 0.02 ( 0.000 ) 2.3 0.20 ( 0.006 ) Control 4.3 ( 0.14 ) a 0.19 ( 0.006 ) 3.6 ( 0.24 ) 0.13 ( 0.006 ) 0.4 ( 0.02 ) 0.02 ( 0.000 ) 2.5 ( 0.04 ) 0.17 ( 0.009 ) ab D GYP 4.3 ( 0.08 ) a 0.18 ( 0.014 ) 3.4 ( 0.09 ) 0.12 ( 0.008 ) 0.4 ( 0.01 ) 0.02 ( 0.000 ) 2.2 ( 0.14 ) 0.16 ( 0.003 ) b BIO 3.9 ( 0.16 ) b 0.19 ( 0.011 ) 3.4 ( 0.14 ) 0.11 ( 0.003 ) 0.4 ( 0.01 ) 0.02 ( 0.000 ) 2.3 ( 0.11 ) 0.17 ( 0.005 ) ab GYP:+:BIO 4.3 ( 0.05 ) a 0.19 ( 0.006 ) 3.2 ( 0.07 ) 0.12 ( 0.006 ) 0.4 ( 0.01 ) 0.02 ( 0.000 ) 2.4 ( 0.15 ) 0.18 ( 0.005 ) a Control 3.8 ( 0.07 ) 0.19 ( 0.013 ) 3.2 ( 0.19 ) 0.12 ( 0.003 ) 0.5 ( 0.03 ) 0.02 ( 0.003 ) 2.0 ( 0.08 ) 0.16 ( 0.010 ) E GYP 3.9 ( 0.11 ) 0.18 ( 0.021 ) 3.5 ( 0.22 ) 0.12 ( 0.003 ) 0.5 ( 0.03 ) 0.01 ( 0.003 ) 2.1 ( 0.05 ) 0.17 ( 0.008 ) BIO 3.9 ( 0.13 ) 0.17 ( 0.018 ) 3.5 ( 0.11 ) 0.12 ( 0.004 ) 0.5 ( 0.02 ) 0.02 ( 0.000 ) 1.9 ( 0.16 ) 0.17 ( 0.005 ) GYP:+:BIO 3.9 ( 0.10 ) 0.16 ( 0.008 ) 3.6 ( 0.09 ) 0.12 ( 0.003 ) 0.5 ( 0.03 ) 0.02 ( 0.003 ) 2.1 ( 0.06 ) 0.17 ( 0.006 ) Abbreviations::GYP,:gypsum;:BIO,:biological:stimulants a::Variable:analyzed:each:year:separately.:Means:with:the:same:letter:are:not:significantly:different:(P=0.05,:LSD) b::Recommended:mineral:levels:Cabbage:leaf::N:(3.6Y5.0);:Ca:(1.1Y3.0);:Mg:(0.4Y0.75);:K(3Y5):Source::Bryson:et:al.:(2014) c::Farms:B:and:C,:cabbage:leaf:sample:was:analyzed:as:a:composite:of:the:four:blocks.:

87 Table 14. Average phosphorus, sulfur, boron and copper content in cabbage foliage (cupping stage) and whole heads, with standard error in parentheses, after harvest in Fall 2016.

a, b, c a, b, c a, b, c a, b, c Phosphorus/(%) Sulfur/(%) Boron/(ppm) Copper(ppm) Farm Treatment Leaves Head Leaves Head Leaves Head Leaves Head Control 0.4 ( 0.03 ) 0.03 ( 0.003 ) 1.7 ( 0.04 ) b 0.08 ( 0.005 ) 62 ( 3.4 ) 2.7 ( 0.05 ) 5.3 ( 3.40 ) 0.1 ( 0.00 ) A GYP 0.4 ( 0.02 ) 0.02 ( 0.003 ) 2.0 ( 0.12 ) a 0.07 ( 0.000 ) 62 ( 5.2 ) 2.6 ( 0.10 ) 4.8 ( 5.20 ) 0.1 ( 0.00 ) BIO 0.3 ( 0.02 ) 0.02 ( 0.002 ) 2.0 ( 0.02 ) ab 0.07 ( 0.003 ) 57 ( 1.6 ) 2.6 ( 0.10 ) 4.8 ( 1.59 ) 0.1 ( 0.00 ) GYP:+:BIO 0.4 ( 0.02 ) 0.03 ( 0.003 ) 2.1 ( 0.10 ) a 0.08 ( 0.005 ) 64 ( 5.4 ) 2.5 ( 0.18 ) 5.0 ( 5.43 ) 0.1 ( 0.00 ) Control 0.6 0.02 ( 0.000 ) 1.2 0.05 ( 0.004 ) 56 1.8 ( 0.10 ) 4.3 0.1 ( 0.00 ) B GYP 0.7 0.03 ( 0.003 ) 1.5 0.06 ( 0.006 ) 55 1.9 ( 0.40 ) 4.6 0.2 ( 0.13 ) BIO 0.6 0.03 ( 0.003 ) 1.2 0.06 ( 0.000 ) 54 2.6 ( 0.33 ) 4.3 0.2 ( 0.05 ) GYP:+:BIO 0.7 0.03 ( 0.003 ) 1.5 0.06 ( 0.006 ) 56 2.4 ( 0.37 ) 4.7 0.2 ( 0.08 ) Control 0.5 0.03 ( 0.003 ) 1.3 0.08 ( 0.003 ) ab 47 3.2 ( 0.11 ) 5.0 0.4 ( 0.08 ) C GYP 0.4 0.03 ( 0.000 ) 1.2 0.09 ( 0.003 ) a 42 3.4 ( 0.15 ) 4.0 0.6 ( 0.23 ) BIO 0.5 0.03 ( 0.003 ) 1.4 0.07 ( 0.004 ) b 50 3.1 ( 0.12 ) 5.5 0.4 ( 0.08 ) GYP:+:BIO 0.5 0.03 ( 0.000 ) 1.3 0.08 ( 0.003 ) ab 44 3.2 ( 0.06 ) 4.4 0.3 ( 0.08 ) Control 0.4 ( 0.01 ) 0.02 ( 0.003 ) 1.5 ( 0.10 ) 0.07 ( 0.003 ) ab 64 ( 1.9 ) 3.1 ( 0.06 ) 4.6 ( 0.43 ) 0.2 ( 0.10 ) D GYP 0.4 ( 0.01 ) 0.02 ( 0.000 ) 1.6 ( 0.02 ) 0.07 ( 0.003 ) ab 75 ( 7.6 ) 3.0 ( 0.15 ) 4.6 ( 0.25 ) 0.1 ( 0.01 ) BIO 0.4 ( 0.00 ) 0.02 ( 0.002 ) 1.5 ( 0.06 ) 0.07 ( 0.000 ) b 65 ( 2.7 ) 3.2 ( 0.10 ) 4.7 ( 0.20 ) 0.1 ( 0.03 ) GYP:+:BIO 0.4 ( 0.02 ) 0.02 ( 0.003 ) 1.5 ( 0.03 ) 0.08 ( 0.003 ) a 64 ( 3.3 ) 3.1 ( 0.27 ) 4.5 ( 0.09 ) 0.1 ( 0.03 ) Control 0.4 ( 0.02 ) 0.03 ( 0.000 ) a 1.3 ( 0.03 ) b 0.07 ( 0.004 ) 52 ( 1.4 ) 2.5 ( 0.11 ) 6.1 ( 0.32 ) 1.0 ( 0.06 ) E GYP 0.4 ( 0.02 ) 0.02 ( 0.003 ) b 1.5 ( 0.06 ) a 0.07 ( 0.005 ) 56 ( 2.1 ) 2.5 ( 0.17 ) 6.4 ( 0.10 ) 1.0 ( 0.16 ) BIO 0.4 ( 0.02 ) 0.03 ( 0.000 ) a 1.4 ( 0.04 ) a 0.07 ( 0.003 ) 50 ( 0.4 ) 2.4 ( 0.14 ) 5.9 ( 0.20 ) 0.9 ( 0.11 ) GYP:+:BIO 0.3 ( 0.01 ) 0.03 ( 0.000 ) a 1.4 ( 0.02 ) a 0.08 ( 0.003 ) 53 ( 2.2 ) 2.5 ( 0.09 ) 6.6 ( 0.30 ) 1.0 ( 0.07 ) Abbreviations::GYP,:gypsum;:BIO,:biological:stimulants a::Variable:analyzed:each:year:separately.:Means:with:the:same:letter:are:not:significantly:different:(P=0.05,:LSD) b::Recommended:mineral:levels:Cabbage:leaf:::P:(0.33X0.75);:S:(0.3X0.75);:B:(25X75);:Cu:(5X15):Source::Bryson:et:al.:(2014) c::Farms:B:and:C,:cabbage:leaf:sample:was:analyzed:as:a:composite:of:the:four:blocks.:

88 Table 15. Average iron, manganese, zinc and sodium content in cabbage foliage (cupping stage) and whole heads, with standard error in parentheses, after harvest in Fall 2016.

Iron+(ppm) a, b, c Manganese+(ppm) a, b, c Zinc+(ppm) a, b, c Sodium+(ppm) a, b, c Farm Treatment Leaves Head Leaves Head Leaves Head Leaves Head Control 74 ( 4.6 ) 3.0 ( 0.71 ) 61 ( 3.08 ) ab 1.0 ( 0.00 ) 34 ( 2.0 ) a 2.0 ( 0.00 ) 2193 ( 176 ) b 72 ( 2.0 ) A GYP 91 ( 9.6 ) 3.3 ( 0.25 ) 58 ( 1.75 ) b 1.0 ( 0.00 ) 30 ( 1.5 ) b 2.8 ( 0.75 ) 2098 ( 155 ) b 84 ( 8.5 ) BIO 97 ( 13.8 ) 4.3 ( 1.97 ) 65 ( 3.33 ) ab 1.0 ( 0.00 ) 30 ( 0.4 ) b 1.8 ( 0.25 ) 2748 ( 168 ) a 144 ( 26.0 ) GYP:+:BIO 84 ( 12.0 ) 3.8 ( 0.48 ) 69 ( 2.48 ) a 1.0 ( 0.00 ) 33 ( 1.3 ) ab 2.0 ( 0.00 ) 3218 ( 185 ) a 137 ( 35.7 ) Control 90 2.5 ( 0.29 ) 40 1.0 ( 0.00 ) 32 1.0 ( 0.00 ) 1530 56 ( 11.3 ) ab B GYP 133 2.3 ( 0.75 ) 38 1.3 ( 0.25 ) 41 1.0 ( 0.00 ) 1250 43 ( 15.8 ) b BIO 97 3.0 ( 0.71 ) 36 1.3 ( 0.25 ) 34 1.0 ( 0.00 ) 2430 90 ( 11.5 ) ab GYP:+:BIO 108 2.0 ( 0.71 ) 35 1.3 ( 0.25 ) 38 1.5 ( 0.50 ) 1780 105 ( 28.0 ) a Control 260 7.0 ( 0.71 ) ab 34 1.5 ( 0.29 ) 33 1.5 ( 0.50 ) 2360 143 ( 6.8 ) C GYP 105 9.0 ( 0.91 ) a 28 1.8 ( 0.25 ) 30 1.0 ( 0.00 ) 2620 150 ( 8.0 ) BIO 133 7.0 ( 0.41 ) ab 32 1.5 ( 0.29 ) 37 1.0 ( 0.00 ) 3330 211 ( 26.9 ) GYP:+:BIO 118 6.5 ( 0.87 ) b 28 1.5 ( 0.29 ) 30 1.0 ( 0.00 ) 2950 225 ( 41.8 ) Control 70 ( 6.5 ) 4.0 ( 0.41 ) 45 ( 1.71 ) 1.0 ( 0.00 ) 31 ( 3.0 ) 1.7 ( 0.48 ) b 1728 ( 233 ) b 92 ( 4.8 ) b D GYP 78 ( 4.1 ) 4.3 ( 0.48 ) 43 ( 2.39 ) 1.0 ( 0.00 ) 30 ( 1.3 ) 2.0 ( 0.41 ) ab 1608 ( 49 ) b 95 ( 9.3 ) ab BIO 77 ( 4.5 ) 3.5 ( 0.29 ) 45 ( 2.90 ) 1.0 ( 0.00 ) 32 ( 0.8 ) 2.5 ( 0.65 ) a 2945 ( 192 ) a 98 ( 9.4 ) ab GYP:+:BIO 80 ( 2.7 ) 3.8 ( 1.03 ) 46 ( 3.75 ) 1.0 ( 0.00 ) 33 ( 2.8 ) 2.0 ( 0.41 ) ab 2650 ( 47 ) a 128 ( 16.4 ) a Control 90 ( 10.7 ) 4.3 ( 0.48 ) 62 ( 4.03 ) 1.8 ( 0.25 ) 45 ( 3.5 ) 3.5 ( 1.19 ) 1395 ( 234 ) 60 ( 8.7 ) E GYP 120 ( 44.9 ) 5.5 ( 0.96 ) 67 ( 3.73 ) 1.8 ( 0.25 ) 45 ( 2.7 ) 3.0 ( 0.71 ) 1485 ( 187 ) 64 ( 12.2 ) BIO 84 ( 3.2 ) 5.0 ( 0.41 ) 68 ( 4.53 ) 1.8 ( 0.25 ) 46 ( 1.4 ) 4.0 ( 0.71 ) 1660 ( 96 ) 94 ( 10.2 ) GYP:+:BIO 77 ( 5.8 ) 5.0 ( 0.58 ) 76 ( 4.92 ) 2.0 ( 0.00 ) 46 ( 2.9 ) 3.5 ( 0.50 ) 2420 ( 335 ) 96 ( 21.9 ) Abbreviations::GYP,:gypsum;:BIO,:biological:stimulants a::Variable:analyzed:each:year:separately.:Means:with:the:same:letter:are:not:significantly:different:(P=0.05,:LSD) b::Recommended:mineral:levels:for:Cabbage:leaf::Fe:(30Y200);:Mn(25Y200);:Zn:(20Y200);:Na:(0Y2500):Source::Bryson:et:al.:(2014) c::Farms:B:and:C,:cabbage:leaf:sample:was:analyzed:as:a:composite:of:the:four:blocks.:

89 Table 16. Average nitrogen, calcium, magnesium and potassium content in butternut squash foliage (fruiting stage) and fruit

(mesocarp plus exocarp), with standard error in parentheses, after harvest in Fall 2016.

Nitrogen-(%) Calcium-(%) Magnesium-(%) Potassium-(%) Farm Treatment Leaves Fruit Leaves Fruit Leaves Fruit Leaves Fruit Control 5.9 ( 0.54 ) 1.5 ( 0.14 ) b 1.9 ( 0.64 ) 0.51 ( 0.032 ) a 0.6 ( 0.08 ) 0.20 ( 0.014 ) 3.9 ( 0.40 ) 2.4 ( 0.08 ) a, b a, b a, b a, b B GYP 6.0 ( 0.76 ) 1.5 ( 0.09 ) b 2.2 ( 0.57 ) 0.47 ( 0.014 ) ab 0.7 ( 0.05 ) 0.22 ( 0.013 ) 4.0 ( 0.41 ) 2.7 ( 0.37 ) BIO 6.0 ( 0.17 ) 1.6 ( 0.07 ) b 1.7 ( 0.28 ) 0.45 ( 0.026 ) b 0.6 ( 0.05 ) 0.22 ( 0.010 ) 4.0 ( 0.31 ) 2.6 ( 0.32 ) GYP;+;BIO 5.8 ( 0.58 ) 1.8 ( 0.16 ) a 1.7 ( 0.62 ) 0.46 ( 0.046 ) b 0.6 ( 0.04 ) 0.22 ( 0.017 ) 4.1 ( 0.46 ) 2.8 ( 0.32 ) Control 6.1 ( 0.25 ) 1.6 ( 0.13 ) 1.3 ( 0.10 ) 0.42 ( 0.024 ) 0.5 ( 0.04 ) 0.18 ( 0.006 ) b 3.5 ( 0.23 ) ab 2.4 ( 0.15 ) D GYP 5.9 ( 0.47 ) 1.7 ( 0.04 ) 1.3 ( 0.33 ) 0.44 ( 0.033 ) 0.5 ( 0.03 ) 0.19 ( 0.014 ) a 3.4 ( 0.09 ) b 2.4 ( 0.31 ) BIO 5.8 ( 0.38 ) 1.6 ( 0.10 ) 1.5 ( 0.39 ) 0.46 ( 0.062 ) 0.5 ( 0.07 ) 0.18 ( 0.010 ) ab 3.7 ( 0.25 ) ab 2.4 ( 0.21 ) GYP;+;BIO 6.4 ( 0.30 ) 1.7 ( 0.12 ) 1.2 ( 0.40 ) 0.41 ( 0.017 ) 0.6 ( 0.04 ) 0.19 ( 0.005 ) a 3.8 ( 0.17 ) a 2.4 ( 0.06 ) Control 2.5 ( 0.39 ) 1.1 ( 0.10 ) 5.9 ( 1.22 ) 0.82 ( 0.031 ) 0.7 ( 0.09 ) 0.21 ( 0.010 ) 2.4 ( 0.12 ) 2.9 ( 0.06 ) F GYP 2.5 ( 0.18 ) 1.1 ( 0.09 ) 6.3 ( 0.35 ) 0.75 ( 0.061 ) 0.8 ( 0.07 ) 0.20 ( 0.010 ) 2.3 ( 0.31 ) 2.6 ( 0.17 ) BIO 2.0 ( 0.52 ) 1.2 ( 0.16 ) 6.5 ( 1.33 ) 0.78 ( 0.030 ) 0.8 ( 0.10 ) 0.20 ( 0.017 ) 2.1 ( 0.28 ) 2.7 ( 0.13 ) GYP;+;BIO 2.6 ( 0.65 ) 1.3 ( 0.08 ) 5.0 ( 1.13 ) 0.79 ( 0.037 ) 0.7 ( 0.09 ) 0.21 ( 0.010 ) 2.3 ( 0.31 ) 2.9 ( 0.33 ) Abbreviations:;GYP,;gypsum;;BIO,;biological;stimulants a:;Variable;analyzed;each;year;separately.;Means;with;the;same;letter;are;not;significantly;different;(P=0.05,;LSD) b:;Recommended;mineral;levels;Squash:;N;(4Z6);;Ca;(1Z2.5);;Mg;(0.3Z1);;K(4Z6);Source:;Bryson;et;al.;(2014)

90 Table 17. Average phosphorus, sulfur, boron and copper content in butternut squash foliage (fruiting stage) and fruit

(mesocarp plus exocarp), with standard error in parentheses, after harvest in Fall 2016.

Phosphorus/(%) Sulfur/(%) Boron/(ppm) Cooper/(ppm) a, b a, b a, b a, b Farm Treatment Leaves Fruit Leaves Fruit Leaves Fruit Leaves Fruit Control 0.98 ( 0.120 ) 0.4 ( 0.02 ) 0.49 ( 0.043 ) 0.16 ( 0.010 ) b 73 ( 15 ) 24 ( 2 ) 66 ( 42 ) 8.9 ( 0.65 ) B GYP 1.08 ( 0.308 ) 0.5 ( 0.09 ) 0.48 ( 0.025 ) 0.17 ( 0.010 ) ab 79 ( 17 ) 25 ( 1 ) 101 ( 51 ) 9.4 ( 1.13 ) BIO 1.03 ( 0.167 ) 0.5 ( 0.06 ) 0.47 ( 0.030 ) 0.17 ( 0.010 ) ab 70 ( 8 ) 26 ( 2 ) 68 ( 42 ) 9.7 ( 1.06 ) GYP;+;BIO 1.04 ( 0.182 ) 0.5 ( 0.10 ) 0.47 ( 0.010 ) 0.18 ( 0.013 ) a 67 ( 11 ) 25 ( 1 ) 100 ( 37 ) 9.9 ( 0.67 ) Control 0.87 ( 0.116 ) ab 0.2 ( 0.01 ) b 0.47 ( 0.017 ) ab 0.18 ( 0.006 ) bc 63 ( 3 ) b 24 ( 1 ) 17 ( 1 ) 7.9 ( 0.64 ) b D GYP 0.85 ( 0.048 ) b 0.3 ( 0.02 ) a 0.48 ( 0.024 ) ab 0.19 ( 0.010 ) a 72 ( 11 ) a 25 ( 2 ) 25 ( 16 ) 8.7 ( 0.85 ) a BIO 0.88 ( 0.058 ) ab 0.3 ( 0.03 ) a 0.45 ( 0.028 ) b 0.17 ( 0.006 ) c 73 ( 8 ) a 25 ( 2 ) 16 ( 1 ) 8.7 ( 0.50 ) a GYP;+;BIO 0.97 ( 0.033 ) a 0.3 ( 0.02 ) a 0.50 ( 0.029 ) a 0.18 ( 0.010 ) ab 67 ( 5 ) ab 24 ( 1 ) 18 ( 2 ) 8.8 ( 0.79 ) a Control 0.43 ( 0.034 ) 0.4 ( 0.02 ) 0.38 ( 0.021 ) 0.17 ( 0.012 ) 162 ( 10 ) 30 ( 2 ) 8 ( 2 ) 9.1 ( 0.20 ) F GYP 0.42 ( 0.118 ) 0.4 ( 0.05 ) 0.41 ( 0.065 ) 0.17 ( 0.013 ) 180 ( 25 ) 27 ( 1 ) 8 ( 3 ) 8.7 ( 0.68 ) BIO 0.44 ( 0.081 ) 0.4 ( 0.04 ) 0.39 ( 0.041 ) 0.16 ( 0.013 ) 165 ( 20 ) 28 ( 3 ) 8 ( 3 ) 8.8 ( 0.59 ) GYP;+;BIO 0.52 ( 0.229 ) 0.4 ( 0.03 ) 0.42 ( 0.075 ) 0.17 ( 0.013 ) 146 ( 40 ) 28 ( 1 ) 9 ( 3 ) 8.9 ( 0.49 ) Abbreviations:;GYP,;gypsum;;BIO,;biological;stimulants a:;Variable;analyzed;each;year;separately.;Means;with;the;same;letter;are;not;significantly;different;(P=0.05,;LSD) b:;Recommended;mineral;levels;Squash:;P;(0.35Y1);;S;(0.28Y0.50);;B;(25Y75);;Cu;(6Y25);;Source:;Bryson;et;al.;(2014)

91 Table 18. Average iron, manganese, zinc and sodium content in butternut squash foliage (fruiting stage) and fruit (mesocarp plus exocarp), with standard error in parentheses, after harvest in Fall 2016.

Iron+(ppm) a, b Manganese+(ppm) a, b Zinc+(ppm) a, b Sodium+(ppm) a, b Farm Treatment Leaves Fruit Leaves Fruit Leaves Fruit Leaves Fruit Control 177 ( 71 ) 83 ( 50.3 ) 68 ( 14.1 ) 14 ( 1.3 ) 83 ( 8.3 ) 21 ( 0.8 ) b 3 ( 0.0 ) 3 ( 0.0 ) B GYP 259 ( 179 ) 74 ( 20.5 ) 71 ( 18.8 ) 13 ( 3.6 ) 86 ( 9.9 ) 25 ( 1.0 ) ab 3 ( 0.0 ) 3 ( 0.0 ) BIO 136 ( 10 ) 67 ( 12.9 ) 65 ( 9.9 ) 18 ( 4.2 ) 84 ( 2.2 ) 28 ( 2.6 ) a 3 ( 0.0 ) 3 ( 0.0 ) GYP;+;BIO 138 ( 35 ) 77 ( 37.1 ) 58 ( 12.4 ) 16 ( 2.9 ) 80 ( 6.8 ) 28 ( 4.4 ) a 3 ( 0.0 ) 3 ( 0.0 ) Control 176 ( 27 ) 44 ( 8.7 ) b 77 ( 4.5 ) 17 ( 3.9 ) 80 ( 10.3 ) 21 ( 1.8 ) b 3 ( 0.0 ) 11 ( 9.6 ) D GYP 176 ( 35 ) 60 ( 11.6 ) a 69 ( 11.4 ) 16 ( 2.5 ) 140 ( 117.7 ) 26 ( 2.4 ) a 3 ( 0.0 ) 13 ( 14.2 ) BIO 179 ( 9 ) 44 ( 15.2 ) b 67 ( 11.7 ) 17 ( 7.6 ) 78 ( 7.0 ) 21 ( 3.4 ) b 3 ( 0.0 ) 6 ( 5.0 ) GYP;+;BIO 163 ( 16 ) 49 ( 3.3 ) ab 69 ( 5.0 ) 15 ( 0.8 ) 93 ( 8.7 ) 24 ( 1.7 ) ab 3 ( 0.0 ) 6 ( 4.8 ) Control 1061 ( 730 ) 55 ( 6.6 ) 69 ( 20.6 ) 10 ( 4.0 ) 44 ( 8.1 ) 25 ( 4.2 ) 72 ( 20.2 ) 46 ( 5.6 ) F GYP 3415 ( 2678 ) 52 ( 9.0 ) 102 ( 37.9 ) 7 ( 1.3 ) 52 ( 6.3 ) 21 ( 1.7 ) 100 ( 48.4 ) 45 ( 16.8 ) BIO 2484 ( 1385 ) 61 ( 11.5 ) 86 ( 27.9 ) 8 ( 1.3 ) 48 ( 8.5 ) 23 ( 2.9 ) 55 ( 22.4 ) 33 ( 14.5 ) GYP;+;BIO 2396 ( 2467 ) 50 ( 11.4 ) 66 ( 33.5 ) 7 ( 2.1 ) 52 ( 14.4 ) 24 ( 3.0 ) 86 ( 61.1 ) 68 ( 13.8 ) Abbreviations:;GYP,;gypsum;;BIO,;biological;stimulants a:;Variable;analyzed;each;year;separately.;Means;with;the;same;letter;are;not;significantly;different;(P=0.05,;LSD) b:;Recommended;mineral;levels;Squash:;Fe;(60Z300);;Mn(50Z250);;Zn;(20Z200);;Na;(0Z2500);Source:;Bryson;et;al.;(2014)

92