Defining Regenerative Farm Systems & the Effects Of

DEFINING REGENERATIVE FARM SYSTEMS & THE EFFECTS

OF REGENERATIVE ALMOND PRODUCTION SYSTEMS ON SOIL

HEALTH, BIODIVERSITY, ALMOND NUTRITION, AND PROFIT

A University Thesis Presented to the Faculty

California State University, East Bay

In Partial Fulfillment

of the Requirements for the Degree

Master of Science in Biological Science

Thomas Lewis Dudash Fenster

May 2021

Abstract

Regenerative agriculture aims to improve soil health and promote biodiversity while producing nutritious food profitably. We developed practice-based scoring systems that define regenerative cropland and rangeland, and validate these scoring systems based on whether these scores scaled with regenerative goals on actual farm operations. Natural clusters in the number of regenerative practices used can be used to distinguish regenerative and conventional operations. Almonds are the dominant crop in California agriculture in terms of acreage and revenue generated, and are mostly farmed using practices that degrade the soil, deplete water resources, and reduce biodiversity in the orchard. We examined the soil health, biodiversity, yield, and profit of regenerative and conventional almond production systems that represented farmer-derived best management practices. Regenerative practices included abandoning some or all synthetic agrichemicals, planting perennial ground covers, integrating livestock, maintaining non- crop habitat, and using composts and compost teas. Total soil carbon, soil organic matter, total soil nitrogen, total soil phosphorous, calcium, sulfur, and soil health test scores were all significantly greater in regenerative soils. Water infiltrated regenerative soils six-fold faster than conventional soils. Total microbial biomass, total bacterial biomass, Gram + bacteria, and Actinobacteria were significantly greater in regenerative soils. There was more plant biomass, species diversity, and percent cover in regenerative orchards.

Invertebrate richness and diversity, and earthworm abundance and biomass were significantly greater in regenerative orchards. Pest populations, yields, and nutrient density of the almonds were similar in the two systems. Profit was twice as high in the

regenerative orchards relative to their conventional counterparts. No one practice was responsible for the success of regenerative farms; their success was the result of simultaneously combining multiple regenerative practices into a single, functional farm system. This style of farming may assist in combatting planetary scale problems (e.g., climate change, biodiversity loss, agricultural pollution, chronic human health problems, declining rural communities, etc.) while making farms more profitable and resilient.

Key words: Biodiversity, Economics, Regenerative farming system, Invertebrates, Soil health, Soil microbiology

DEFINING REGENERATIVE FARM SYSTEMS & THE EFFECTS OF

REGENERATIVE ALMOND PRODUCTION SYSTEMS ON SOIL

HEALTH, BIODIVERSITY, ALMOND NUTRITION, AND PROFIT

Thomas Lewis Dudash Fenster

Approved: Date:

Electronic Signatures Available May 14, 2021 Jonathan Lundgren Jonathan Lundgren (May 7, 2021 14:13 CDT) May 7, 2021 Dr. Jonathan Lundgren

Patty Oikawa (May 7, 2021 13:20 PDT) May 7, 2021 Dr. Patty Oikawa

May 10, 2021 Dr. Erica Wildy

Brian A. Perry (May 10, 2021 10:31 PDT) May 10, 2021 Dr. Brian Perry

Acknowledgements

Thank you to Dr. Jonathan Lundgren for working with me to conceive and design this study. Thank you to Dr. Jonathan Lundgren and Dr. Patty Oikawa for their help in developing the methodology, analyzing the results, and preparing the manuscript. Thank you to Dr. Erica Wildy for providing logistical support, reviewing the thesis project proposal, and the thesis itself. Thank you to my committee member, Dr. Brian Perry for reviewing the thesis proposal and the thesis itself. Thank you to Chris Bradley, Charles

Fenster, Ian Lundgren, Ali Mohammedsabri, and Hilary Vanderheiden for helping collect field samples and conducting lab work. Thank you to Dr. Kelton Welch and Mark F.

Longfellow who helped to identify the invertebrates for the analyses. We thank the dozens of other technicians who helped us to collect and enter the data from these projects. Thank you to Natalie Lobue for all her love and support over the course of the project.

Funding

This work was supported by Ecdysis Foundation, Patagonia Foundation, and W-SARE graduate student fellowship GW19-193, USDA-Pest Management Alternatives Program

Grant 2013-34381-21245, NCR SARE Grants GNC16-227, GNC15-207, LNC18-410, and the Globetrotter Foundation.

Table of Contents

Abstract ...... iii

Acknowledgments...... vi

List of Figures ...... ix

List of Tables ...... xi

Chapter 1 Thesis Introduction ...... 1

Chapter 2 Defining and validating regenerative farm systems using a composite of ranked agricultural practices ...... 6

Introduction ...... 6

Methods ...... 9

Results ...... 21

Discussion ...... 26

Tables ...... 34

Figures ...... 38

Chapter 3 The effects of regenerative almond production systems on soil health, biodiversity, almond nutrition, and profit ...... 46

Introduction ...... 46

Methods ...... 50

Results ...... 64

Discussion ...... 73

Tables ...... 85

Figures ...... 94

vii

Chapter 4 Thesis Conclusion ...... 102

References ...... 106

Appendix ...... 127

List of Figures

Figure 2.1. The relationships of regenerative farming intensity on fine particulate

organic matter in cornfields of the Upper Midwest (A) and percent soil

organic matter in California almond orchards (B) ...... 38

Figure 2.2. Total soil carbon as it relates to the number of regenerative practices in

California almond production systems ...... 39

Figure 2.3. Water infiltration rates in the soils of California almond orchards in

relation to the number of regenerative practices on each orchard ...... 40

Figure 2.4. Haney test scores on soils from California almond orchards with

increasing numbers of regenerative practices...... 41

Figure 2.5. Plant species richness on the orchard floor in California almonds (A),

and plant diversity (Shannon H; B) and biomass index (C) in rangelands

of the Northern Plains as they relate to how regenerative a farm is ...... 42

Figure 2.6. The invertebrate species diversity (Shannon H) on the soil surface in

cornfields of the Upper Midwest (A), and on the orchard floors of

California almonds (B), as they relate to the number of regenerative

practices implemented on farms ...... 43

Figure 2.7. Invertebrate species diversity (Shannon H) in dung pats (A) and the

abundance of dung beetles (B) as they relate to regenerative intensity

on ranches in the Northern Plains ...... 44

Figure 2.8. Net profit of California almonds as it relates to the number of

regenerative practices on orchards...... 45

Figure 3.1. Haney soil health scores in regenerative and conventional almond

orchards ...... 94

Figure 3.2. Time required for soils to infiltrate a 5 cm rain event ...... 95

Figure 3.3. The differences in carbon and nitrogen through the 0-6,000 Mg

Equivalent Soil Mass (ESM) layer (~0-60 cm) in regenerative and

conventional almond orchards ...... 96

Figure 3.4. Total microbial biomass collected from the soil in regenerative and

conventional almond orchards ...... 97

Figure 3.5. Invertebrate biomass and diversity (Shannon H) of the epigeal

invertebrate communities in regenerative and conventional orchards ...... 98

Figure 3.6. Net profitability of regenerative and conventional almond production

systems ...... 99

Figure 3.7. Principle Component Analysis of 16 almond orchards based on 8 soil

quality and biological community metrics ...... 100

List of Tables

Table 2.1. A matrix of farm practices in cropland/orchards that can be used to

distinguish regenerative and conventional farms...... 34

Table 2.2. A matrix of farm practices in rangelands that can be used to distinguish

regenerative and conventional ranches...... 36

Table 3.1. The regenerative matrix score is the sum of regenerative practices on

each farm, as determined through grower surveys...... 85

Table 3.2. Soil nutrients in regenerative and conventional orchards ...... 87

Table 3.3. The differences in carbon and nitrogen at different soil depths in

regenerative and conventional almond orchards...... 89

Table 3.4. The soil microbial community in regenerative and conventional

almond orchards ...... 91

Table 3.5. The nutrient levels in the almonds...... 93

Chapter 1 Thesis Introduction

The intensification and expansion of high input monoculture agriculture is a major contributor to the declines in biodiversity (destruction/modification of habitats), as well as global climate change, with 37.5% of the earth’s land area devoted to agriculture

(The World Bank, 2017). From 1870-1980, the conversion of land for agricultural uses accounted for 1/3 of the world’s net CO2 emissions. Agricultural lands have lost 30%-

75% (30-40t C ha-1) of their original soil organic carbon (Lal et al., 2007 , Robertson et al., 2014). These soil losses threaten waterways and accelerate climate change, resulting in a massive depletion of soil quality and a reduction in agricultural productivity. Thus inputs, such as chemical fertilizers, pesticides, and herbicides are the only way for simplified monoculture systems to remain productive.

The footprint of modern agriculture provides an important opportunity for agriculture to be part of the solution in stemming biodiversity losses, sequestering carbon, and restoring productivity, but only if regenerative practices are adopted by farmers.

Regenerative agriculture seeks to improve soil and plant health by fostering biodiversity and the buildup of organic matter in the soil, while profitably improving the nutrient density of farm products ( LaCanne & Lundgren, 2018, Schreefel et al., 2020).

Regenerative operations are unified in their goal and key concepts, but practices vary substantially depending on the farm and region. Regenerative agricultural practices include refraining from use of synthetic pesticides, herbicides, fungicides, and chemical nutrient inputs, while using cover crops (the more diverse and regionally adapted the better), intercropping, residue mulching, no till/reduced tillage (also known as

conservation tillage), the integration of grazers to cropping systems, organic amendments, and diverse crop rotations.

Previous studies suggest that pesticides and artificial nutrient inputs could be replaced with practices such as diverse cover cropping, conservation tillage, grazers, and composts, etc resulting in increases to invertebrate and soil microbial diversity (LaCanne

& Lundgren, 2018, Soto et al., 2021,Vukicevich et al., 2016). However, there is a lack of research into how the variation in management regimes impacts key environmental services and the profitability of regenerative farms. The studies comprising this thesis research proposes a system for defining regenerative agricultural systems while examining the impact of various regenerative management regimes within almond production systems in California.

Almond orchards are California’s highest grossing crop at $6.09 billion, spanning

619,169 ha, producing 80% of the world’s supply and almost 100% of the domestic supply of almonds (California Almond Board, 2016; CDFA, 2019; 2020). The industry is making tentative strides toward being sustainable and reducing its carbon emissions, establishing the California Almond Sustainability program in 2009. As part of these efforts, the board sponsored a life cycle analysis of the industry which found that orchard production practices are responsible for the most greenhouse gas emissions, with nutrient management accounting for 40% of emissions, irrigation accounting for 20% of emissions, and pest and biomass management respectively accounting for ~10% of emissions (Kendall, Marvinney, Brodt, & Zhu, 2015). Organic amendments have the potential to serve as substitutes for chemical inputs while being shown to increase carbon

sequestration in other cropping systems. Therefore, utilizing regenerative practices, such as cover cropping and integrating grazers presents an ideal opportunity for the industry to become carbon neutral or negative. In fact, the Almond Board’s life cycle analysis stated that there has been insufficient research examining organic inputs in orchards and their impacts on carbon sequestration.

The way in which orchard floors (including almonds) are managed affects yield, soil health, and tree health by impacting nutrient and water dynamics, as well as biodiversity (Moore-Kucera et al., 2008). Ground cover plays an important role, serving as a cover crop which machinery can move over while preventing erosion, conserving water, promoting water infiltration, and helping to maintain the soil structure and organic matter (Wilson & Hardestry, 2006). Conversely, the perceived risks of ground covers are that they can quickly increase in biomass and compete with the trees for water and nutrients, while creating a habitat for insect pests, food borne pathogens, and destructive rodents. Typically orchard trees are treated with quick release fertilizers and the ground cover between the rows of trees is managed via mowing (at least 7 times/year) and spraying with herbicides (Hendrickson & Olson, 2006; Moore-Kucera et al., 2008;

Wilson & Hardestry, 2006). However, because of the spacing between the rows of trees, orchards are ideal habitats for grazers. For centuries grazers have been used in orchards with the practice being common in North America up until the 1950s (Wilson &

Hardestry, 2006). A study conducted in Spain found that using sheep in almond orchards resulted in greater soil moisture, plant-available phosphorous, and water-soluble carbon

(Ramos et al., 2010). Today the utilization of practices such as the reintegration of

grazers, cover crops, green manures, compost application, dry farming, and conservation tillage into orchards is slowly gaining traction with organic growers around the United

States, but growers cite the lack of research and technical assistance as being major barriers to more widespread adoption.

Defining what is a regenerative system, while determining to what level various regenerative management practices can reduce the need for quick release fertilizers, insecticides, mowing, and herbicides while meeting the trees’ nutrient demands in an ecologically efficient manner and adding value to orchard acreage are the drivers of this study. The relationship between regenerative orchard management practices and their impact on profit and the many biological communities that compose an agricultural ecosystem remain poorly understood. This study takes an innovative approach to studying agricultural systems by using a systems-level experimental design that examines the biology, agronomic, and socioeconomic aspects of the orchard system. Further, it uses grower-defined and tested systems in order to quantify actual real- world farming systems. Almonds are a large and important food crop, so looking at these questions in the context of almonds is particularly pertinent. As almonds are a perennial crop, requiring considerable spacing between rows, by allowing vegetation instead of maintaining bare orchard floors orchards have the potential to sequester carbon and provide critical habitat. Since California is dealing with increased weather variability due to climate change and is also in the process of building out its carbon trading market, orchards could be an asset to California’s climate goals instead of a liability and resource stressor.

Ongoing efforts attempt to define farms as regenerative to aid marketers, policymakers, farmers, and the like. However, the approach needs to balance precision with function, and must be transparent, simple, scalable, transferable, incorruptible, and replicable. In chapter two we propose a straightforward, transparent, replicable, and empirically backed approach to defining regenerative agricultural systems. We accomplished this by creating a list of questions reflecting the principles of regenerative agriculture as defined by farmers and ranchers (Fenster et al., 2021; LaCanne &

Lundgren, 2018; Pecenka & Lundgren, 2019). Having proposed a criterion for defining regenerative systems in chapter three we undertook an in-depth analysis exploring the differences we found among regenerative and conventional almond orchards. Further, we examined the relationship between soil health, carbon storage, and biodiversity. Chapter four encompasses a summary and a discussion of the findings presented in chapters two and three.

Chapter 2 Defining and validating regenerative farm systems using a composite of ranked agricultural practices.

Introduction

The term “regenerative agriculture” was first coined by Robert Rodale (1983).

The name highlighted how industrialized agriculture was severely reducing its natural resource base, and that without rebuilding that natural resource base, “sustainable agriculture” and “conservation agriculture” were insufficient for supporting the food and natural resource needs of a growing human population. Regenerative agricultural systems increase soil health and promote biodiversity while producing nutritious food profitably.

Some outcomes of a regenerative operation are improved greenhouse gas relationships, balanced water relationships, reduced pollution from agrichemicals, increased resiliency of farms, more nutritionally robust foods, increased ecosystem services, etc. (Elevitch et al., 2018; Francis et al., 1986; Sherwood & Uphoff, 2000). A central challenge for researchers, policy makers, consumers, etc. is defining what a regenerative farm is and having a standardized method for discerning them from conventional farms (Newton et al., 2020; Schreefel et al., 2020).

The principles and philosophy that define regenerative systems have been well formulated by practitioners and educators (Lal, 2020; Schreefel et al., 2020;

UnderstandingAG, 2020; USDA-NRCS, 2020). Essentially, regenerative practices can be distilled down to two central principles: 1) reduce uniform disturbance (such as tillage and agrichemical use), and 2) increase diversity (biodiversity, and the diversity of revenue streams from an operation). The first working formula for these principles was

proposed by LaCanne and Lundgren (2018) and included four principles: 1) reduce or eliminate tillage, 2) never leave bare soil, 3) maximize plant diversity and productivity on a farm, and 4) integrate livestock and cropping operations. Here, we add a fifth principle:

5) reduce or eliminate synthetic agrichemicals. In croplands, practices such as no-till, diverse crop rotations lasting more than four seasons, cover cropping, intercropping and interseeding, and livestock integration all contribute to a regenerative farming model

(LaCanne & Lundgren, 2018; Rhodes, 2017). In rangelands, practices that actualize regenerative principles include reduction/elimination of synthetic agrichemicals and adaptive multi-paddock grazing systems that allow a rangeland to rest following the punctuated disturbance of grazing (Teague & Kreuter, 2020; Wagner et al., 2020).

Regenerative farms are also diverse and complementary in their enterprises, and adaptive in their management choices, ensuring that a farm is resilient and profitable in the face of adversity. These practices are dependent upon one another within a system for them to be optimally successful, and it is the system, not the individual practices, that drives the success of an operation.

Classifying regenerative farming operations has to balance precision with function. Adaptability and innovation are hallmarks of operations that are guided by regenerative principles. Therefore, while a matrix of responses or practices must have sufficient nuance to accurately reflect a regenerative system, it cannot be a rigid formula that inhibits that critical adaptability and ongoing innovation. Nor should a scoring system be so complex as to exclude adoption by regenerative farmers. Classification schema have been developed for other movements within ecological agriculture,

including conservation agriculture (Hendrix et al., 1986; Kassam et al., 2012), organic agriculture (USDA-AMS, 2020), holistic management (Alfaro-Arguello et al., 2010), mob grazing (Gurda et al., 2018). In these systems, the tangible manifestations of core principles are represented by a series of standards or practices. An inherent risk is that regenerative agriculture will follow the same pattern, and come to be defined by specific standards or practices, rather than by the principles and goals of a regenerative system.

There are a growing number of certification programs and definitions for regenerative farming that capture key themes of regenerative agriculture and assemble various practices that may produce intended outcomes (AGW, 2020; Carbon_Underground,

2020; HMI, 2020; ROC, 2020; TerraGenesis, 2020). In the end, any labelling or certification system must be validated empirically to determine whether it results in the intended outcomes.

Regenerative agriculture needs to be defined by principles and supported by empirical data from farms displaying a wide range of management practices to ensure the implemented principles are achieving regenerative farming goals. The approach to defining a regenerative system must be transparent, simple, scalable, transferable, incorruptible, and replicable. Our team has created a series of questions for land managers that encapsulate the principles that drive regenerative agriculture as defined by the farmers and ranchers (Fenster et al., 2021; LaCanne & Lundgren, 2018; Pecenka &

Lundgren, 2019; Schmid & Lundgren, 2021). Each answer receives a score, and higher scores are indicative of more regenerative operations. A regenerative matrix score is attained by summing the results of each question. The first goal of this paper is to

determine whether key response variables pertaining to soil health, biodiversity, ecosystem function, and farm performance (Elevitch et al., 2018; Schreefel et al., 2020) scale with the assignment of scores from our regenerative matrix. Essentially, does a higher regenerative score in cropland and rangeland consistently scale positively with the desired outcomes of regenerative farming? The second goal of this paper is to determine whether there is a threshold score that can be used to distinguish between regenerative and conventional systems? If this matrix performs these functions, then this approach could be used to advance the field of regenerative agriculture by providing a clear, scalable and transferable approach to characterizing a regenerative farm.

Methods

Two independent scoring systems based on a narrow suite of practices were used to define regenerative cropland and rangeland systems. Three food systems were examined in this project, corn in the Upper Midwest (LaCanne & Lundgren, 2018), almond orchards in California (Fenster et al., 2021), and rangelands of the Northern

Plains (Pecenka & Lundgren, 2019; Schmid & Lundgren, 2021). Farms included in these studies represented successful, established systems and were in their respective farming philosophies for at least 3 years. Some of the farms were targeted for their reputation as being leaders in regenerative farming, but the resulting fields and ranches in reality represented a wide continuum from very conventional to very regenerative farming systems. We examined different suites of response variables that are considered representative of a regenerative system. In croplands (Table 2.1), the elimination of

tillage, maintaining ground cover through planting cover crops or fostering resident vegetation, planting hedgerows, and use of organic amendments (compost, manure, mulch, compost teas), grazing, were all considered regenerative (LaCanne & Lundgren,

2018; Soto et al., 2021). Tillage, maintaining bare soil, and spraying synthetic insecticides, herbicides, fungicides, use of chemical fertilizers were all considered conventional practices. Regenerative practices were scored as 1, and conventional practices were scored as 0. In rangelands (Table 2.2), management systems were defined by their stocking density, rotation frequency, and use of ivermectin products. Cattle operations were categorized based upon their combination of these practices to form the different systems (Colley et al., 2019; Teague & Barnes, 2017). Use of ivermectin in the ranches was categorized as high (multiple applications during a year; scored as 0), low

(single annual use, not applied during grazing period; scored as 1) and no ivermectin

(scored as 2). Operations’ stocking densities (animal units [AU] per ha), were categorized as fewer than 5 animal units (AU) per ha (scored as 0), 5-10 AU per ha (scored as 1), and more than 10 AU per ha (scored as 2). Operations were categorized as having a rotation frequency of 30 d or more (scored 0), between 10-30 d (scored as 1), and less than 10 d

(scored as 2). Finally, rest periods on ranches were scored as continuously grazed (no rest; scored as 0), allowed to rest 1 > and < 30 d (1), or >30 d (2) over a growing season.

A simple questionnaire to obtain the necessary information to populate our matrices can be found as Appendix 1.

The study systems

Corn was examined on 20 farms (each with four fields) in North Dakota, South

Dakota, Minnesota, and Nebraska in 2015 and 2016. Fields were a minimum of 4 ha in size, and the sampling area in each field was 61 × 61 m. All samples were taken at least

12 m into the field to minimize border effects. Only three of these farms were certified organic. Genetically modified (Bt hybrid) corn was universally treated with neonicotinoid seed treatments and was regarded as insecticide-treated. In corn, we examined soil organic matter (SOM), fine particulate organic matter (fPOM), soil bulk density, water infiltration, invertebrate communities in the soil, on the soil surface and in the plant canopy, pest abundance, yields, and profits.

Sixteen California almond orchards were studied in 2018 and 2019. Replicate plots (n

= 4; 40 × 40 m) were established in each orchard. Plots were established 20 m into the field to avoid field margin effects. The orchards in the study ranged from the Northern half of the San Joaquin Valley through the Capay Valley to Chico. Orchards were 3-38 y old. Almond orchards are generally planted with at least two different varieties, with over-lapping bloom periods to promote pollination. Therefore, all of the orchards in the study contained at least two varieties, and almond varieties varied among the orchards. In each orchard, we examined a full range of soil and water characteristics, soil microbial, plant, and invertebrate community characteristics, pest damage, yields, and economics.

Dung invertebrate communities were examined on 16 ranches in a 7,935 km2 region in eastern South Dakota during 2015 and 2016. All sites were grazed by cattle for at least

5 y, but annual grazing intensity and grazing period varied. Herds ranged from 20 to 120

individuals, and the cattle differed in size, breed, and administered ivermectin products.

Focal pastures were at least 4 ha in size. The systems were ranked from regenerative to conventional based on several practices (Table 2.1). In our study, the designation of management systems was based upon the grouping of several management practices that were occasionally all present on the same cattle operation.

A second study of cattle ranches examined plant communities and helminth fecal parasites. Selected sites (n = 20 per grazing treatment) focused on rangelands in the

Dakotas during 2019 and 2020. Grazing treatments were assigned based on a ranch management character matrix, which consisted of rancher-defined best management practices for regenerative and conventional ranching systems (Table 2.2). This character matrix was adapted from a similar study conducted in the region by Pecenka and

Lundgren (2019). Grazing treatments were paired within each region and year. Each grazing system had been practiced on the site for at least 4 years.

Soil quality

Soil physical and chemical properties were assessed in corn fields and in almond orchards. SOM and bulk density (BD) were assessed in both of these systems. In cornfields and almonds, four soil cores (8.5 cm deep, 5 cm in diam) were randomly collected from a single field on each of 20 farms; cores were collected at locations that were at least 10 m apart. On each sample, vegetative material was removed from 60 g of soil by hand, and the resulting samples were stored in aluminum containers at 105˚ C overnight. SOM in the soil sample was measured using the weight loss on ignition (LOI)

technique (Davies, 1974). To measure bulk density, four soil cores of known volume per field were weighed before and after drying at 100˚ C for 55 h.

In cornfields, we also measured fine particulate organic matter (fPOM).

Approximately 30 g of soil was weighed in sterile aluminum pans. The soil was soaked in

90 mL of hexametaphosphate for 24 h, mixed for 5 min with a stainless-steel mixer, and were then sieved through screens with 500 μm and then 53 μm holes; the finer screen isolated the fPOM fraction. The isolated sample was then weighed, dried for 24 h at 105˚

C. Samples were then cooled in a desiccator cabinet, weighed, baked in a furnace for 4 h at 450˚ C. The samples were then cooled in a desiccator cabinet and weighed a final time.

In almond orchards, soil samples were collected at random locations in the inter- row area of each plot to determine total soil carbon (TSC) and total soil nitrogen (TSN).

The probe (2.54 cm × 91.44 cm was inserted 60 cm deep. Each core was placed into a plastic bag that was stored on ice until it could be transferred to a paper bag in the laboratory. Samples were weighed, dried, and weighed again. All visible pieces of rock and organic matter were removed from the samples. The samples were ground and were then passed through a sieve with 0.180 mm openings. Elemental analysis was conducted on three subsamples (12-15 mg) that were housed on tin capsules (5 × 9 mm) (ECS 8020,

NC Technologies, Milan, Italy). To control for the relative compaction and other circumstances, the mass (Mg) of TSC per ha was assessed using the Equivalent Soil Mass

(ESM) method, in which a cubic spline of Mg of TSC per depth layer was calculated

(Wendt & Hauser, 2013). This resulted in the assessment of carbon as Mg of TSC/ha at

6,000 Mg (59.2 cm deep).

Soil macro- and micronutrients were also quantified in the almond orchards. The samples were ground to pass a 2 mm sieve and divided into three subsamples (two were 4 g each and one weighed 40 g). The 40 g soil sample is analyzed with a 24 h incubation test at 24o C. This sample is wetted through capillary action by adding 20 mL of deionized water to a 237 mL glass jar and then capped. After 24 h, the gas inside the jar was analyzed using an infrared gas analyzer (IRGA) (Li-Cor 840A, LI-COR Biosciences,

Lincoln NE) for CO2-C. The two 4 g samples were extracted with 40 mL of deionized

3 water and 40 mL of H A to extract the NO3-N, NH4-N, and PO4-P from the samples. The samples were shaken for 10 min, centrifuged for 5 min, and filtered through filter paper

(Whatman 2V, Cytiva, Marlborough, MA). The water and H3A extracts were analyzed on a flow injection analyzer (Lachat 8000, Hach Company, Loveland CO). The water extract was also analyzed on a Teledyne-Tekmar Torch C:N analyzer for water-extractable organic C and total N. The H3A extract was also analyzed on a Thermo Scientific ICP-

OES instrument for P, K, Mg, Ca, Na, Zn, Fe, Mn, Cu, S and Al. The Haney soil health score combines five independent measurements of a soil’s biological properties to provide a general estimate of the overall health of a soil system (Haney et al., 2018). It is calculated as 1-d-Co2-Carbon/10 plus water extractable organic carbon (WEOC)/50 plus water extractable organic nitrogen (WEON)/10.

Water infiltration

We used the rainfall infiltration rate kits of the Natural Resource Conservation

Service (NRCS) to sample water infiltration rates in northern cornfields and California

almonds. A metal ring (15 cm diam, 13.5 cm tall) was hammered 6.5 cm into the soil.

Water (444 mL) was poured into the ring, and the time it required to saturate into the soil was recorded. A second container of 444 mL of water was poured into the ring, and the time to saturation was recorded again. In cornfields, this process was recorded during anthesis.

Microbial diversity

Microbial communities were only sampled in almond orchards using the

Phospholipid fatty acid (PLFA) test. Soil cores (15 cm depth, 1.9 cm diam; N = 16), were taken from four replicates per site per farm during the fruiting period. The samples were taken at random locations within each replicate, at least 5 m apart, using a transect that diagonally bisected the plot. Soil cores for each orchard were combined in a sealed plastic bag and placed in a cooler with dry ice (Drenovsky et al., 2010). Soil samples were stored at -80˚ C until they could be freeze-dried and ground to 2 mm particle sizes.

Phospholipid fatty acid (PLFA) testing provided an index of a soil’s microbial biomass and composition (Frostegård et al., 2011). The microbial biomass and community composition were recorded as Total microbial biomass, Undifferentiated microbial biomass, total bacteria, Gram-positive bacteria, Actinomycetes, Gram-negative bacteria,

Rhizobia bacteria, total fungi, arbuscular mycorrhizal fungi, saprophytic fungi, and

Protozoa.

Plant community structure

Plant community structure was assessed solely as ground cover in the almond study. The ground cover height and composition in each of the replicates/plots was recorded during each of the three sampling periods. The percent ground cover was categorized as 0-25%, 25-50%, 50-75%, and 75-100%. Percent ground cover in the overall orchard was assessed using visual assessments of the percent ground cover in each invertebrate quadrat.

Plant community diversity and green canopy cover were examined during the

2019 and 2020 grazing seasons of the rangeland study, while vegetation biomass was assessed only during the 2020 season. All three plant community metrics were measured during three periods of the grazing season; early June, mid-July, and late-August/early-

September. At each pasture site, two 50 m transect lines were established 15 m apart and perpendicular to the slope of the land. Plant community diversity was assessed using the belt transect method for monitoring native prairie vegetation in the Dakotas (Grant et al.,

2004). Briefly, plant community structure was recorded for a randomly selected

5 × 0.5 m2 belt along each transect line. Vegetation is classified in each 5 m segment according to a hierarchical breakdown of plant groups common to the region (Grant et al.,

2004). Green canopy cover was documented using the smartphone app Canopeo, which quantifies fractal green vegetation canopy cover area per unit area (Patrignani & Oschner,

2015). Quadrats (50 × 25 cm2) were established at 0, 25, and 50 m along transect lines to quantify green canopy cover with Canopeo. Lastly, a disc pasture meter was used to estimate above-ground vegetation biomass along each transect line and within their

immediate surrounding area (<15 m). Compression height of vegetation was recorded by dropping a 0.13 m2 plate weighing 1.34 kg from a height of 183 cm.

Insect diversity

Insect surveys in cornfields and almonds involved sampling the epigeal community from 0.25 m2 sheet-metal quadrats (15 cm tall) inserted into the soil

(Lundgren et al., 2006). Quadrats were placed in randomly selected locations between corn rows (n = 5 per plot) during anthesis. Invertebrates were exhaustively collected from the surface of the soil and beneath plant debris using handheld mouth aspirators. In almonds, sampling of the invertebrate communities occurred during the bloom, fruit development, and harvest periods. The invertebrate communities that could be collected from the soil surface and top 2 cm of the soil with mouth-operated aspirators in 15 min were preserved in 70% ethanol.

In cornfields, we also sampled the foliar invertebrate community. The foliar invertebrate community was sampled using a destructive whole plant assessment. Corn plants (n = 25 per plot) were randomly selected, thoroughly examined, and invertebrates that were not sight-identified were collected using mouth aspirators. Plants were then severed at the ground level and were transported to the field margin where their leaves, stems, ears, whorls, and tassels were inspected and dissected on white sheets.

Soil invertebrate and dung invertebrate communities were assessed using soil cores in corn fields and rangelands. In corn fields, five soil cores (10 cm diameter, 10 cm height) were collected per field in randomly selected locations within and between corn

rows during anthesis. In rangelands, the same core approach was used to collect invertebrates from 2-5 d old dung pats and the soil directly beneath the pat (n = 10 per ranch). Samples were collected monthly from May to September. All cores were refrigerated for < 36 h, and then were placed into a Berlese collection system over 7 d, when they reached a constant weight.

All extracted invertebrates were identified microscopically and cataloged; each unique specimen was identified to the lowest taxonomic level possible representing a functional morphospecies. Each morphospecies was placed into a trophic guild

(coprophage, predator, parasitoid, herbivore, or pest) based upon previous descriptions of the biology of arthropod community in these systems. Voucher specimens are housed in the Mark F. Longfellow Biological Collection at Blue Dasher Farm, Estelline, SD, USA.

Pests

In cornfields, pests included aphids, lepidopteran larvae, and corn rootworm adults that were identified from the foliar bioinventories. In almonds, pest incidence was assessed on 500 almonds per farm in 2018 and 600 almonds per farm in 2019, (< 20 from any one tree) (Bentley et al., 2001; Doll, 2009; Legner & Gordh, 1992). The almonds were each categorized as having no pest damage, navel orange worm damage (Amyelois transitella [Walker]; Lepidoptera: Pyralidae), ant damage (Formicidae), oriental fruit moth damage (Grapholita molesta [Busck]; Lepidoptera: Tortricidae), peach twig borer damage (Anarsia lineatella Zeller; Lepidoptera: Gelechiidae), leaf footed plant bug or

stinkbug damage (Hemiptera: Coreidae, Pentatomidae), and unknown pest damage

(Bentley et al., 2001; Doll, 2009; Symmes, 2018).

Fecal oocyst counts of coccidia and egg counts of gastrointestinal nematodes belonging to the superfamilies Trichostrongyloidea and Strongyloidea were conducted on herds from the 2019 – 2020 rangeland study. Three grams of dung was collected from

2 – 4 d old cattle dung pats present in pastures (n = 5 pats sampled/site). Fecal samples were processed according to the Modified Wisconsin Sugar Float Technique (Cox &

Todd, 1962). Coccidia oocysts and eggs of Trichostrongyles and Strongyles were quantified for 1 g of fecal sample. Sampling for internal parasites in the herds was conducted twice in 2019 (mid-July and late-August/early September) and three times in

2020 (early-June, mid-July, and late-August/early September).

Yield and profit

In cornfields and almonds, responses from producer surveys were used to determine management practices, costs, and revenues that went into the direct net profitability of each operation. Gross profit analysis included yield, return on grain, and additional revenue that included livestock grazing and production. In cornfields, yield information provided by the farmers were confirmed by yields that were hand-gathered from three 3.5-m row sections from each replicate-field. Costs associated with corn production included corn seed, cover crop seed, drying/cleaning grain, crop insurance, tillage, planting corn, planting cover crop, fertilizers, herbicides, and irrigation

(additional information on profit/loss analysis can be found in LaCanne and Lundgren

2018). Operating costs in almonds included winter sanitation, sampling for tree nutrient status and soil salinity, pH, and nutrient levels, irrigation and frost protection, fertilizers, insecticides, herbicides, fungicides, disease treatment sprays, trapping vertebrate pests, cover crop seed, tillage, mowing, flamers, grazers, and harvest.). Harvest in the almonds included the hourly labor to conduct the harvest and the price paid to external contractors

(additional information on the almond profit/loss analysis can be found in Fenster et al in prep). In both systems, only direct costs and revenues were used to calculate profitability.

Data analysis

Unless otherwise described, we used linear regressions to search for correlations between the regenerative matrix scores and response variables. In the almond study, clay percentages of the soils were considered as co-factors in all models that examined TSC.

Plot values were composited across plots into single values in the almond study and linear regression analyses were used to compare regenerative scores and response variables. In instances where resampling a single farm was performed (either spatially or temporally) Linear Mixed Models were used to remove pseudoreplication and account for this dependence; field and farm were included as random factors in the corn models, and month was included as a fixed factor and farm as a random factor in the rangeland studies. All statistics were conducted using Systat 13.1 (Systat Software Inc., San Jose,

CA)

Results

Soil physical and chemical properties. In cornfields, fPOM of the soil was strongly and positively associated with regenerative matrix scores (F1, 17 = 4.61; P = 0.047) (Figure 2.1A), but SOM (F1, 17 = 0.11;

P = 0.75), bulk density (F1, 17 = 2.32; P = 0.15), and water infiltration rates (F1, 11 = 0.47;

P = 0.51) of the soil were not correlated with matrix scores. The slowest water infiltration rates in the higher regenerative scoring farms were those that practiced tillage.

In almond orchards, higher matrix scores correlated to significantly higher levels of TSC and TSN (500-6000 ESM layers) and SOM (15 cm depth). At the 0-6000 Mg

ESM layer, there was a significant correlation between the orchards’ matrix scores and

TSC (model: F2, 13 = 13.77, P = 0.001; score: t = 2.26, P = 0.04; clay: t = 4.73, P < 0.001)

(Figure 2.2). At the 6000 Mg ESM layer there was a significant correlation between the orchards’ matrix scores and TSN (model: F2, 13 = 17.73, P < 0.001; score: t = 1.94, P =

0.08; clay: t = 5.63, P < 0.001). In contrast to what we found in cornfields, in the top 15 cm of soil there was a significant correlation between the orchards’ SOM%, matrix scores, and clay percentage (model: F2, 13 = 47.56, P < 0.001; score: t = 5.50, P < 0.001; clay: t = 8.05, P < 0.001) (Figure 2.1B).

In almonds, higher matrix scores and soil clay percentages correlated to higher levels of WEON (F2, 13 = 9.16, P = 0.003) and WEOC (F2, 13 = 15.61, P = 0.001) in the organic matter of the soils. Total phosphorus (model: F2, 13 = 4.32, P = 0.04; score: t =

2.65, P = 0. 02, clay: t = -1.28, P = 0.23) and inorganic phosphorus (model: F2, 13 = 4.56,

P = 0.03, score: t = 2.74, P = 0.02, clay: t = -1.28, P = 0.22) were significantly and positively correlated with an orchard’s matrix score. Higher matrix scores correlated to

higher levels of Calcium (F2, 13 = 5.97, P = 0.01) and Sulfur (F1, 14 = 10.80, P = 0.01), but lower levels of Aluminum (F1, 14 = 6.13, P = 0.03). There were no correlations between other micronutrients and the regenerative matrix score (P > 0.05). Haney soil health test scores were positively correlated with regenerative matrix scores (model: F2, 13 = 7.82, P

= 0.01, Score t = 2.99, P = 0.01, clay: t = 2.59, P = 0.02) (Figure 2.4). Soil respiration was unaffected by regenerative matrix scores (model: F2, 13 = 2.91, P = 0.09, Score t =

1.62, P = 0.13, clay: t = 1.77, P = 0.10).

Higher regenerative-conventional matrix scores in almonds correlated to lower bulk densities (model F2, 13 = 8.95, P = 0.004; score: t = -3.91, P = 0.002, clay: t = -1.61,

P = 0.13). Water infiltration rates increased alongside matrix scores for these orchards

(model: F2, 5 = 11.43, P = 0.01; score: t = -4.50; P = 0.009, clay: t = 2.56, P = 0.05)

(Figure 2.3).

Soil microbiology In almond orchards, higher matrix scores correlated to higher amounts of bacterial biomass (F1, 14 = 6.25, P = 0.03) and higher Gram (+) biomass (F1, 14 = 9.12, P = 0.01).

The percent composition in the microbial community was not affected by regenerative score (F1, 14 = 2.47, P = 0.14), indicating greater overall microbial biomass rather than bacterial dominance. The remainder of these microbial community metrics were not correlated to the regenerative conventional matrix score (P > 0.05).

Plant community

More regenerative cropland and rangeland supported more diverse and robust plant communities. In almond orchards, higher matrix scores correlated to more ground cover (F1, 14 = 127, P < 0.001), more plant species (F1, 14 = 27.61, P < 0.001) (Figure

2.5A), and greater biomass height (F1, 14 = 9.90, P = 0. 01). In rangelands of the Northern

Plains, there was a significant relationship between matrix score and plant biomass

(Score: F1, 53 = 8.47, P = 0.01; month: F2, 53 = 0.45, P = 0.64) (Figure 2.5B), plant diversity (Score: F1, 106 = 14.22, P < 0.001; month: F3, 106 = 1.02, P = 0.39) (Figure 2.5C), and ground cover (Score: F1, 106 = 10.06, P = 0.002; month: F3, 106 = 14.76, P < 0.001).

Invertebrate community

Across cropland and rangeland, more regenerative farms had more robust invertebrate communities. Several aspects of invertebrate community structure in cornfields were strongly and positively correlated with how regenerative a field was scored. The abundance (F1, 55 = 6.53, P = 0.01), species richness (F1, 55 = 38.31, P <

0.001), and diversity (F1, 55 = 7.34, P = 0.01) (Figure 2.6A) of the epigeal invertebrate community was positively affected by matrix score. Similarly, matrix scores were positively associated with the species richness of the invertebrate community found in the soil column (F1, 56 = 12.96, P = 0.001), but the abundance was only marginally associated with matrix score (F1, 56 = 3.41, P = 0.07), and the diversity of this community was unaffected by matrix score (F1, 56 = 2.45, P = 0.12). Foliar invertebrate abundance (F1, 55 =

0.99, P = 0.32), species richness (F1, 55 = 1.35, P = 0.25), and species diversity (F1, 55 =

0.20, P = 0.66) were not related to the matrix scores.

The invertebrate community on the soil surface was positively affected by the

number of regenerative practices on almond orchards. Invertebrate abundance (F1, 14 =

10.28, P = 0.01), biomass (F1, 14 = 70.10, P < 0.001), species richness (F1, 14 = 14.44, P =

0.002) and species diversity indices (H: F1, 14 = 12.37, P = 0.003; DS: F1, 14 = 11.11, P =

0.01) (Figure 2.6B) were positively correlated with matrix score. There was a positive correlation between earthworm (square rooted to normalize residuals) abundance and matrix score on these orchards (F1, 14 = 9.87, P = 0.007).

In rangelands of the Northern Plains, the invertebrate community associated with cattle dung was strongly and positively associated with the matrix score attained. The species richness (Score F1, 74 = 15.38, P < 0.001; month F4, 74 = 6.54, P < 0.001), and species diversity (Score F1, 74 = 16.10, P < 0.001; Date: F4, 74 = 1.50, P = 0.21) (Figure

2.7A) of the dung invertebrate community were positively associated with matrix score.

Abundance of dung invertebrates was uncorrelated with matrix score (Score F1, 74 = 0.01,

P = 0.99; month: F4, 74 = 4.83, P = 0.001). The abundances of two critical functional groups, dung beetles (Score F1, 60 = 10.05, P = 0. 006; month: F4, 60 = 5.70, P = 0.001)

(Figure 2.7B) and invertebrate predators, (Score F1, 74 = 7.41, P = 0.01; month: F4, 74 =

4.09; P = 0.01) increased as a rangeland became more regenerative.

Pest populations

In all of these systems, pest populations were uniformly below any recognized economic thresholds. Pests were not correlated with the number of regenerative practices used in cornfields (pest abundance: F1, 55 = 2.18, P = 0.15) or in almond orchards (% damaged nuts: F1, 14 = 0.69; P = 0.42). In rangelands, there was no relationship between

the matrix score and Trichostrongyles (Score: F1, 88 = 0.09, P =0.77; month: F3, 88 = 0.39,

P = 0.76) or Coccidia (Score: F1, 88 = 1.78, P = 0.19; month: F3, 88 = 0.70, P = 0.56) fecal parasite loads.

Yields

Corn yields were negatively correlated with the regenerative matrix score (F1, 56 =

3.88, P = 0.05). Despite this, three of the top ten yielding cornfields were regenerative, indicating that regenerative practices are not necessarily tied with yield reductions.

Standard deviations became greater proportions of the mean as a farm became more

2 regenerative (variability in yields were higher among regenerative farms) (R = 0.79; F1, 4

= 5.12, P = 0.11). In almonds there was no relationship between almond yield and how regenerative an orchard was (F1, 12 = 0.86, P = 0.37). On a treatment level, regenerative farms had twice the variance in yield relative to the conventional orchards.

Economics

Although there was a positive relationship between matrix score and profitability in cornfields, this relationship was not significant (F1, 53 = 2.49, P = 0.12). In cornfields, there was a significant increase in the standard deviation of profit as a farm became more

2 regenerative (e.g., standard deviation as a proportion of the mean; R = 0.72; F1, 4 = 7.68,

P = 0.07). This indicates more variability in profits among the more regenerative producers. The top ten highest netting farms were all regenerative farms (scores of four and five). In almonds, profit was strongly and positively correlated with matrix scores

(F1, 11 = 7.41, P = 0.02) (Figure 2.8). Operating costs were unaffected by matrix score (F1,

13 = 0.70, P = 0.42), but net income increased as regenerative scores increased (F1, 11 =

8.50, P = 0.01).

Discussion

Our approach to defining farms based on a small suite of carefully chosen practices was strongly associated with several key metrics that define regenerative systems, including soil health, biodiversity promotion and profitability. With our matrix, we have distilled complex agricultural systems down to fewer than 10 practices that serve as useful indicators of regenerative operations in cropland and rangeland. These practices represent each of the five principles that define regenerative agriculture in cropland and in rangeland. We have determined that these practices are regenerative across cropping systems; the cropland matrix works in row crop systems as well as a perennial system including orchards. Also, it was noteworthy that none of the regenerative farm attributes attained an asymptote, which suggests that farms could become more regenerative if they continue to incorporate additional regenerative practices. This matrix can be used to categorize a farm as regenerative or conventional based on a threshold score.

Regenerative farming practices inherently increase plant biomass and diversity in both cropland and rangeland systems, and this plant community is a central mechanism for improving the soil health, biodiversity, and resilience of these farming operations.

Farmers in this study enhanced their plant diversity and biomass (Figure 2.5) using a variety of tools. In almonds and cornfields, many of the farmers planted annual cover crops to improve the health of their soils. In some regenerative orchards, farmers allowed

the native vegetation to persist. All of the regenerative orchards stopped the use of synthetic herbicides, and this likely contributed to greater plant diversity. In rangelands, short-term, intense grazing stimulates plant communities to diversify and grow if the plant community is allowed to rest following a grazing event (Hillenbrand et al., 2019;

Teague et al., 2011). Plants are a vital part of the carbon cycle, and as such they are the primary means whereby energy enters into an ecosystem (Weil & Brady, 2017).

Additionally, plant communities are requisite directly and indirectly to the genesis of healthy soils (Lucas, 2001). Plant roots stabilize the soil, while providing the needed polysaccharides for microbial communities to grow and perform key nutrient cycling services (Philippot et al., 2013). Physical properties of soils including water infiltration rates, soil aggregate structure, bulk density are also influenced by plants. (Gulick et al.,

1994; Liu et al., 2005). Plant diversity and biomass scales positively with the diversity of nearly every other group of organisms (Bianchi et al., 2006; Saunders et al., 2013; Zak et al., 2003), and thus plant communities are a driver of biodiversity within agricultural habitats. Taken in sum, the relationships between plant communities and regenerative score illustrate an important mechanism for how regenerative farms positively enhance many intended regenerative outcomes.

Animal integration is another important tool for managing farms that improve regenerative outcomes. The most regenerative cropping operations observed in this set of studies always integrated livestock (chickens, sheep, or cattle), and these farms also had the greatest biodiversity, soil health, water infiltration rates, and economic metrics.

Regeneratively-managed livestock in cropland improve soil chemical and physical

properties, increase niche diversity which allows other organisms to thrive, and provides additional revenue and an additional plant management tool that can increase farm resilience (Colley et al., 2019; Delgado et al., 2011; Niles et al., 2018; Teague et al.,

2016). Important logistical constraints influence the circumstances of integrating livestock into cropland, especially in standing crops like almonds. Barriers preventing farmers from integrating livestock into cropland should be removed so this important management tool is more commonly adopted.

Soil chemical and physical properties were positively affected by the number of regenerative practices in cropland. Bulk density was significantly lower in the more regenerative almond orchards. Soil carbon and soil organic matter were strongly associated with more regenerative farms. Although we did not conduct a carbon life cycle assessment on corn or almonds, these results support the idea that regenerative farming systems can help our cropland sequester more carbon and help to offset greenhouse gas emissions (Lal, 2020; Soto et al., 2021). Organic and inorganic phosphorous are also enhanced as almond orchards become more regenerative. Phosphorous, a mined agricultural nutrient, is becoming increasingly rare and is available at significant economic and environmental costs (Alewell et al., 2020), so enhancing the plant- available forms of phosphorous that are present in the soils is an important outcome of regenerative agriculture. Micronutrients calcium and sulfur were also enhanced by increasing the number of regenerative practices in almonds. In almonds, water infiltration rates were significantly improved by regenerative production practices. Water is becoming more scarce (Burri et al., 2019), especially in California (Mall & Herman,

2019), as climates continue to change and ground and surface waters are exhausted from conventional agricultural practices. Absorbing water into the soil when it becomes available is a crucial step in keeping these agricultural areas productive. Similar to previous studies (Pikul & Aase, 2003; Pikul et al., 2009), non-tilled cornfields had faster water infiltration rates and lower bulk density. These data support the argument that no- till practices are an indispensable core component of regenerative agriculture, and argues in favor of mandating that regenerative operations be no-till.

All organismal groups measured were enhanced as farms became more regenerative. There was greater bacterial biomass and Gram+ bacteria from the farms with higher regenerative scores. In general, bacterial dominated soils are undesirable, as they tend to drive faster decomposition and reduce soil organic matter (Hendrix et al.,

1986; Lehman et al., 2015). Bacteria dominated soils are produced through tillage, and by definition, regenerative farms are not tilled. Only two of the almond orchards in this study had been tilled; and this must be considered when comparing bacterial communities in regenerative versus conventional almond systems. Despite increasing bacterial biomass in the regenerative orchard soils, the proportion of bacteria to other microbial life did not increase. Furthermore our soil health metric, the Haney Test that accounts for microbial function in a soil (Haney et al., 2018), was positively associated with the regenerative score of a farm. This suggests that although soil bacterial biomass may be increasing on regenerative almond orchards, it is not at the expense of other microbial taxa and it enhances bacterial function. Invertebrate biomass, diversity, and abundance in the soil, on the soil surface, and in the vegetation were positively affected by regenerative practices

in all three study systems. This was particularly true for the epigeal communities. We hypothesize that promotion of the invertebrate community on the soil surface is a product of enhancing the plant community in this same stratum. Herbicides (Bohnenblust et al.,

2016; Morton et al., 1972), fungicides (Johnson et al., 2013), insecticides (Bredeson &

Lundgren, 2018; Pecenka & Lundgren, 2019; Seagraves & Lundgren, 2012), and synthetic fertilizers (Cuesta et al., 2008; Kromp, 1999) disrupt invertebrate communities.

Regenerative farms that abandon these chemicals may experience enhanced invertebrate communities and biological diversity, which is important for farmers because it provides valuable services, including pest suppression.

Pest populations of rangelands and croplands were below economically damaging levels, but these populations were suppressed using very different means in regenerative and conventional systems. In conventional systems, pests are kept low through genetically modified crops and insecticide applications. Conventional almond orchards in this study experienced up to five insecticide applications annually. All of the conventional corn farms are planted with genetically modified, insect resistant (i.e., Bt) varieties that were treated with neonicotinoids. All of the most conventional ranches applied ivermectins in their animals to reduce fecal parasite loads. Our research shows that regenerative practices produce the same low pest populations as when cattle were treated with ivermectins, but with lower input costs. Other work in agricultural ecosystems has shown that invertebrate diversity is essential to mitigating pest populations (Crowder et al., 2010; Letourneau et al., 2009; Lundgren & Fausti, 2015;

Lundgren & Fergen, 2014). This diversity within agricultural systems results in

biological control performed by invertebrates and pathogens keeping pest populations at sub-economically damaging levels. We will test whether this same pattern was true in dung pats. Predation isn’t the only mechanism at work, and we also hypothesize that host plants and livestock are healthier in regenerative systems, which contributes to fewer pests (Barbosa et al., 2009). Also, in almond orchards the more robust epigeal invertebrate community or livestock may be playing a key role in breaking down fallen mummy nuts in which navel orange worm larvae overwinter. There also are likely synergisms among organisms in regenerative systems that balance communities and resist outbreaks of specific pest populations. But in the end, the complexity of biological network interactions in complex ecosystems prevent us from understanding all of the mechanisms whereby regenerative systems consistently suppress pests without pesticide inputs.

Improved soil health and biodiversity on the studied farms was associated with increased profitability. In almonds, there was a clear relationship between more regenerative practices and the profitability of the orchards. In cornfields, the most regenerative farms were also the most profitable, but there was substantially more variance in this relationship. These higher net profits were the result of lowering seed costs, reducing chemical premiums, and value-added marketing of the most regenerative products. Yields did not follow the same pattern as the profitability with regards to matrix scores. Almond yield was not correlated with how regenerative a farm was, and corn yields were significantly reduced as regenerative practices were added to cornfields. It is important to interpret these results recognizing that regenerative agriculture is still in its

infancy in many production systems. Three of the top 10 yielding corn fields were regenerative, meaning regenerative corn production methods do not necessarily lead to yield reductions. Given the low reliance on corn as a human food source, and the lack of yield reductions in almonds, concerns over regenerative food systems’ ability to feed a growing human population are not supported by our research. All of this is to say that regenerative farming practices are correlated with increased profitability of a farm, thus supporting the notion that we can improve farm resilience while promoting the natural resource base of a habitat (Schipanski et al., 2016).

In looking at the data on these established farm systems, there were consistently two distinct clusters of farms based on regenerative score that could be used to categorize regenerative and conventional farming operations. The natural groupings could be divided at scores of 5 and 3 for cropland and rangeland, respectively. One explanation for this may be that farms have trouble staying in business when they only employ an intermediate number of regenerative practices. Farms in the “regenerative” and

“conventional” categories are not all created equal, since changing the number of regenerative or conventional practices can produce a variance in farm performance within each category. Also, the range of scores in the regenerative ranches were greater than that for in cropland, and could indicate a transition in the adoption of regenerative practices by ranchers of the Northern Plains. The duration that a farm is in its respective system also will likely affect the observed regenerative outcomes, and this temporal factor should be considered in further interpretations of regenerative scores. Uses for this matrix might include regulation based on carbon sequestration, water use efficiency, or

pollution. Certification of regenerative operations and marketing of regenerative labelling might also employ the matrix approach as we have laid out. In Appendix 1, we provide a survey for producers to generate their regenerative scores. We hope that this work will be built upon and tested as we confront the challenge of how to empirically define regenerative farming systems

Chapter 2 Tables

Table 2.1. A matrix of farm practices in cropland/orchards that can be used to distinguish regenerative and conventional farms. Shaded rows refer to corn farms in the Upper Midwest, and unshaded rows refer to almond orchards in California. Fungicide use, organic amendments, and field margins/hedgerows were not described in the cornfields. A ‘1’ was assigned for each practice if it was considered regenerative and ‘0’ if it was considered conventional. Eliminating synthetic fertilizers, herbicides, fungicides, insecticides, and tillage were considered regenerative. Including cover crops, organic amendments (e.g., composts, compost teas, etc), livestock integration, and diversified field margins and hedgerows were also considered regenerative practices.

Field locations Fertilizer Herbicides Fungicides Insecti Tillag Cove Field Organic Grazers Compo

(County or s cides e r margins/he amendments site nearest town, crops dgerows score

State)

Merced, CA 1 1 1 1 0 1 1 1 0 7

Butte, CA 1 1 0 1 1 1 1 1 0 7

Merced, CA 1 1 1 1 1 1 1 1 0 8

Yolo, CA 1 1 0 1 0 1 1 1 1 7

Butte, CA 0 0 0 0 1 1 0 1 0 3

Butte, CA 0 0 0 0 1 0 0 0 0 1

Yolo, CA 0 0 0 0 1 0 0 0 0 1

Butte, CA 1 1 0 1 1 1 1 1 0 7

Merced, CA 1 1 0 1 1 1 0 1 0 6

Merced, CA 0 0 0 0 1 0 0 0 0 1

Yolo, CA 1 1 1 1 1 1 0 0 1 7

Merced, CA 0 0 0 0 1 0 0 1 0 2

Merced, CA 0 0 0 0 1 1 0 1 0 3

Bladen, NE 0 0 0 0 0 0 0

York, NE 0 0 0 0 0 0 0

Bismarck, ND 0 0 0 1 0 0 1

White, SD 0 0 0 0 0 0 0

Pipestone, MN 0 0 0 0 0 0 0

Toronto, SD 0 0 0 0 0 0 0

Gary, SD 0 0 0 0 0 0 0

Arlington, SD 0 0 0 0 0 0 0

Lake Norden, 0 0 0 0 0 0 0

Bladen, NE 1 0 1 1 1 0 4

York, NE 1 0 1 1 1 0 4

Bismarck, ND 0 1 1 1 1 1 5

Bismarck, ND 1 0 1 1 1 1 5

White, SD 1 1 1 0 1 0 4

Pipestone, MN 1 1 1 0 1 0 4

Toronto, SD 1 0 0 1 1 0 3

Gary, SD 1 1 1 0 1 1 4

Arlington, SD 1 0 1 1 1 1 5

Lake Norden, 1 0 1 1 1 0 4

Table 2.2. A matrix of farm practices in rangelands that can be used to distinguish regenerative and conventional ranches. Unshaded rows refer to ranches that were included in the dung invertebrate assessment. Shaded rows refer to ranches that were examined in the fecal parasite and plant community study. Management practices of cattle operations were scored 0-2 based, with higher numbers reflecting practices that promote biodiversity and soil quality. Ivermectin application frequency was divided into multiple applications (0), single application not during grazing season (1), and no ivermectin use (2).

Stocking density (animal units; AU) was divided into <5 AU/ha (0), 5-10 AU/ha (1), and >10

AU/ha (2). Rotation frequency was divided into >30 d rotation (0), 10-30 d rotation (1), and <10 d rotation (2). Rest period was considered as continuously grazed during the growing season (0), a rest period of 1-30 d (1), and a rest of > 30 d (2).

Ranch location Ivermectin Stocking Rotation Rest Composite (Nearest town, State) density frequency period matrix score Bruce, SD 1 0 0 0 1 Castlewood, SD 1 2 1 2 6 Clear Lake, SD 2 2 2 2 8 Estelline, SD 1 1 1 2 5 Estelline, SD 0 1 0 0 1 Flandreau, SD 0 0 0 0 0 Gary, SD 2 2 2 2 8 Goodwin, SD 2 2 2 2 8 Madison, SD 0 1 1 2 4 Milbank, SD 1 1 1 2 5 Milbank, SD 0 0 0 2 2 Sioux Falls, SD 2 2 2 2 8 Summit, SD 1 1 1 2 5 Thomas, SD 1 1 2 2 6 Twin Brooks, SD 2 2 1 2 7 Volga, SD 0 0 0 0 0 Hayti, SD 0 0 0 0 Hayti, SD 0 0 0 0 Strandburg, SD 0 0 0 0 South Shore, SD 0 1 1 2 Estelline, SD 0 0 0 0

Watertown, SD 1 2 2 5 Hayti, SD 1 1 2 4 Estelline, SD 2 1 2 5 Gary, SD 2 1 2 5 Goodwin, SD 1 1 2 4 Tuttle, ND 1 0 0 1 Tuttle, ND 0 0 0 0 Wing, ND 0 0 0 0 Moffit, ND 1 0 0 1 Fort Rice, ND 2 2 2 6 ND7; Wing, ND 2 1 2 5 Bismarck, ND 2 2 2 6 Wing, ND 2 2 2 6 Moffit, ND 2 2 2 6 Summit, SD 0 0 2 2 Summit, SD 0 0 0 0 Milbank, SD 2 1 1 4 Milbank, SD 0 0 0 0 Watertown, SD 2 2 2 6 Castlewood, SD 0 0 0 0 Summit, SD 2 0 2 4 Milbank, SD 0 0 0 0 Castlewood, SD 2 2 2 6 Castlewood, SD 0 0 1 1 Sheldon, ND 0 1 2 3 Sheldon, ND 0 0 0 0 Napoleon, ND 0 2 2 4 Napoleon, ND 0 0 0 0 Ellendale, ND 2 2 2 6 Forbes, ND 1 0 1 2 Forbes, ND 2 2 2 6 Berlin, ND 0 1 1 2 Hayti, SD 0 0 0 0 Hayti, SD 0 0 0 0 Strandburg, SD 0 0 0 0

Chapter 2 Figures

Figure 2.1. The relationships of regenerative farming intensity on fine particulate organic matter in cornfields of the Upper Midwest (A) and percent soil organic matter in California almond orchards (B).

Figure 2.2. Total soil carbon as it relates to the number of regenerative practices in

California almond production systems

Figure 2.3. Water infiltration rates in the soils of California almond orchards in relation to the number of regenerative practices on each orchard.

Figure 2.4. Haney test scores on soils from California almond orchards with increasing numbers of regenerative practices.

Figure 2.5. Plant species richness on the orchard floor in California almonds (A), and plant diversity (Shannon H; B) and biomass index (C) in rangelands of the

Northern Plains as they relate to how regenerative a farm is.

Figure 2.6. The invertebrate species diversity (Shannon H) on the soil surface in cornfields of the Upper Midwest (A), and on the orchard floors of California almonds (B), as they relate to the number of regenerative practices implemented on farms.

Figure 2.7. Invertebrate species diversity (Shannon H) in dung pats (A) and the abundance of dung beetles (B) as they relate to regenerative intensity on ranches in the Northern Plains.

Figure 2.8. Net profit of California almonds as it relates to the number of regenerative practices on orchards.

Chapter 3 The effects of regenerative almond production systems on soil health, biodiversity, almond nutrition, and profit.

Introduction Regenerative agriculture can be defined as an “approach to farming that uses soil conservation as the entry point to regenerate and contribute to multiple provisioning, regulating and supporting services, with the objective that this will enhance not only the environmental, but also the social and economic dimensions of sustainable food production” (Schreefel et al., 2020). The principles of regenerative agriculture are similar to those of conservation agriculture and consist of minimizing soil disturbance, eliminating or reducing agrichemical use, eliminating spatio-temporal bare soil events, maximizing plant diversity, and integrating livestock into a cropping operation (Fenster et al., 2021; Gosnell et al., 2019; LaCanne & Lundgren, 2018; Pecenka & Lundgren, 2019;

Rhodes, 2017; Rodale, 1983). These regenerative practices increase organic matter in the soil, which is an active area of research for soil health and climate mitigation efforts

(Demestihas et al., 2017; LaCanne & Lundgren, 2018; Ryals & Silver, 2013; Soto et al.,

2021; Veenstra et al., 2007). Agriculture is an important cause for reductions in biodiversity (Kovács-Hostyánszki et al., 2017; Sanchez-Bayo & Wyckhuys, 2019;

Stewart et al., 2019), and regenerative practices that enhance soil C, such as maintaining permanent ground cover, can help to promote biodiversity (Demestihas et al., 2017;

Eilers & Klein, 2009; Klein et al., 2012; Kovács-Hostyánszki et al., 2017; LaCanne &

Lundgren, 2018; Lundgren et al., 2006; Vukicevich et al., 2019; Vukicevich et al., 2016).

Adoption of regenerative practices at large scale requires empirical validation across a range of agroecosystems.

An important question is whether established regenerative farms consistently contribute key environmental services and profitability in a diversity of cropping systems.

Research frequently examines the effects of isolated regenerative practices, such as cover cropping or organic amendments on cropping systems, rather than quantifying regenerative systems which integrate and “stack” multiple regenerative practices into a single operation (Derpsch et al., 2014; Soto et al., 2021). Further, studies that compare conventional systems to alternative systems frequently do not compare in situ systems developed and practiced by farmers (Derpsch et al., 2014; Soto et al., 2021). However, studies that have examined regenerative farming in situ have documented positive ecosystem impacts and high profitability. For example, regenerative maize and cattle grazing systems in the Midwest have significantly better soil health metrics, improved biodiversity, and reduced pest damage, while being twice as profitable relative to conventional systems (LaCanne & Lundgren, 2018; Pecenka & Lundgren, 2019).

Additionally, research in tomato fields in California’s Central Valley showed that tomato- cotton rotations that integrated cover cropping and conservation tillage led to higher levels of soil carbon than either practice on its own (Veenstra et al., 2007). Research comparing rain fed regenerative and conventional almond (Prunus dulcis) orchards in

Spain found that regenerative management can improve soil quality and ecosystem services, particularly in systems that stack regenerative practices (Soto et al., 2021).

Regenerative management in California almond orchards may influence large swaths of the agricultural community. Almond orchards are California’s highest grossing crop at $6.09 billion, spanning 619,169 ha, while producing 80% of the world’s supply

and nearly 100% of the domestic supply of almonds (California Almond Board, 2016;

CDFA, 2019; 2020). Conventional orchards are reliant on synthetic inputs for nutrient management and pest control (Wade et al., 2019). These orchards maintain bare orchard floors with tillage and synthetic herbicides (in 2017, 664,158 ha of almond orchards were sprayed with glyphosate) (California Department of Pesticide Regulation, 2019), leaving exposed soil for most of the year. The simplification of the landscape forces almond growers to use synthetic pesticides (16 million kg of pesticide/ year) to manage pest outbreaks (California Department of Pesticide Regulation, 2019). In California’s San

Joaquin Valley, agriculture is the primary driver of biodiversity loss, with certain species having lost up to 98% of their habitat range (Stewart et al., 2019; Williams et al., 1998).

Glyphosate and other pesticides also have significant human health impacts (Beard et al.,

2014; von Ehrenstein et al., 2019). As of 2017, 2,386 ha, only 73 farms (less than 1% of

California’s almond acreage) were certified organic (USDA-AMS, 2020). Further, organic production systems occasionally conflict with regenerative philosophies, making regenerative almond systems a rarity within the industry.

Ground cover may enhance the natural resource base of almond orchards in several ways. Exposed soils erode and lose soil carbon (Abdalla et al., 2020; Kosmas et al., 1997), with orchards and vineyards experiencing the highest levels of soil losses among cultivated landscapes (Abdalla et al., 2020). The loss of carbon via soil erosion negatively affects land and water quality, reducing yields (Lal, 2007; Lal et al., 2007).

Twenty percent of the eroded SOC is mineralized and released into the atmosphere in a gaseous state (0.8–1.2 Pg C year−1), contributing to global climate change (Lal, 2007).

Bare orchard floors eliminate critical habitat and resources that support beneficial invertebrates and microbial populations (Eilers & Klein, 2009; Paredes et al., 2013;

Saunders et al., 2013; Vukicevich et al., 2016). Allowing for ground cover and reducing soil disturbances in vineyards increases microbial biomass (Ingels, 2005; Steenwerth &

Belina, 2008; Vukicevich et al., 2019; Whitelaw-Weckert et al., 2007), and these soil microbes play a significant role in forming stable and chemically diverse soil organic carbon (Kallenbach et al., 2016). Increasing vegetation diversity increases invertebrate biodiversity in croplands (Lundgren & Fausti, 2015; Lundgren et al., 2009; Root, 1973) and orchards (Chaplin-Kramer et al., 2011; Paredes et al., 2013; Vukicevich et al., 2016).

Invertebrate diversity, abundance, and biological network interactions limit pest pressure through predation and competition, but also in ways that remain poorly understood

(Barbosa et al., 2009; Lundgren & Fausti, 2015). Further, increasing plant diversity in farmlands eliminates the yield gap between organic and conventional production systems

(Ponisio et al., 2015). The impacts of increasing orchard floor vegetation coverage and reducing synthetic inputs in almond orchards involves complex biological, physical and ecological factors which require systems-level scientific studies.

This study compared established and successful regenerative and conventional almond orchards. We hypothesize that practices that enhance soil health and carbon storage will also increase biodiversity. Specifically, we hypothesize that regenerative orchards will have higher levels of soil organic matter, Total Soil Carbon (TSC), lower soil bulk densities, higher rates of water infiltration, and more robust and diverse microbial and invertebrate populations (Fenster et al., 2021; LaCanne & Lundgren, 2018;

Soto et al., 2021). We hypothesize that regenerative orchards will have reduced pest pressure relative to insecticide-treated conventional systems. Finally, we hypothesize that regenerative orchards will have lower yields while being more profitable. We believe that this improved profitability will stem from a reduction in synthetic input costs as well as a greater market value for their product (LaCanne & Lundgren, 2018). Overall, we do not believe these differences will be due to a particular management practice, but rather the interacting effects of stacked regenerative practices (Fenster et al., 2021).

Methods

Sixteen orchards were studied in 2018 and 2019 (N = 8 each year). Regenerative and conventional orchard pairs were within 15.4 km of each other (mean of 3.7 km). The

Web Soil Survey was used to select similar soil types for the conventional-regenerative orchard pairings (NRCS, 2021). To further establish similar soil conditions between treatments in pairs soil samples were sent to the Oregon State Soil Lab, where the sand- silt-clay composition was determined via the hydrometer technique (OSU, 2017). The farms in the study ranged from the Northern half of the San Joaquin Valley through the

Capay Valley to Chico, and the clay percentages ranged from from 7%- 35%. The clay

2 percentages of the soils were strongly correlated with Total Soil Carbon (TSC) (R adj =

0.52; F1, 14 = 17.32, P = 0.001). The clay percentages of the soils were considered as co- factors in all subsequent models that examined TSC. The average age of the trees in conventional orchards was 13.6 ± 2.96 y and the average age in the regenerative orchards was 17.6 ± 3.27 y, which was not statistically different (t13 = -0.91, Welch P-value =

0.38). The ages of trees in regenerative orchards ranged in years from 8-38 y, while in the conventional orchards, trees ranged from 3-25 y. All the orchards in the study were planted to at least two varieties to improve pollination (Klonsky et al., 2016), and almond varieties varied among the orchards (Table 3.1).

The treatments were defined by rankings derived from a character matrix of different practices that were considered as regenerative or conventional (Fenster et al.,

2021; LaCanne & Lundgren, 2018). Use of organic amendments (compost, manure, mulch, compost teas), no-till, grazing, maintaining ground cover through planting cover crops or fostering resident vegetation, planting hedgerows were all considered regenerative and received a score of 1 in a binary system. Spraying synthetic insecticides, herbicides, fungicides (synthetic and metal based), use of chemical fertilizers, bare soil, and tillage were all considered conventional practices, and received a 0 score. Three of the regenerative orchards used organic certified bioinsecticides, with one of these three orchards also using CIDETRAK® CMDA + NOW MESO, an organic certified dual mating disruption dispenser for Navel Orangeworm control. These orchards received a score of 1 for the no synthetic insecticides category. Orchards that score greater than 4 in our matrix were declared regenerative orchards, and those that received a score of 4 or lower were categorized as conventional orchards.

Replicate plots (n = 4) were established in each orchard. The plots were 40 × 40 m and separated by at least 15 m, resulting in 64 total observation points for the study.

Plots were established 20 m into the field to avoid field margin effects. Smaller plots were used in one Capay Valley regenerative orchard and one Capay Valley conventional

orchard (approximately 30 × 15 m, 6 m margins) due to the smaller sizes of these orchards.

Soil macro- and micro-nutrients and Haney soil health score

Soil pH, soil macro and micronutrients and Haney soil health scores were quantified in each orchard (Ward Laboratories, Kearney, NE). Four soil cores (15 cm deep, 1.9 cm diam.; N = 16), were taken from the four replicate plots during the fruiting period of the orchard. The samples were taken at random locations halfway between the tree and the drip line within the wetting zone of the sprinkler/emitter (~1.5 m from the nearest tree) (Geisseler & Horwath, 2016) within each plot, at least 5 m apart, using a transect that diagonally bisected the plot. All samples were collected in the same week, and each regenerative and conventional orchard pair occurred within 24 hours. Soil cores for each orchard were combined in a sealed plastic bag and placed in a cooler with dry ice (Franzluebbers et al., 2000; Haney et al., 2008). To determine soil organic matter

(SOM), the Loss on Ignition (LOI) technique was used. Soil pH was quantified using the slurry method with a 1:1 ratio soil: water (Weil & Brady, 2017). Soil nutrients levels and the Haney Soil Health Score were measured on samples that were dried at 50˚ C. The samples were ground to pass a 2 mm sieve and divided into three subsamples (two were 4 g each and one weighed 40 g). The 40 g soil sample was incubated for 24 h at 24o C. This sample was wetted through capillary action by adding 20 mL of deionized water to a 237 mL glass jar and then capped. After 24 h, the gas inside the jar was analyzed using an infrared gas analyzer (IRGA) (Li-Cor 840A, LI-COR Biosciences, Lincoln NE) for CO2-

C. The two 4 g samples were extracted with 40 mL of deionized water and 40 mL of

3 3 3 H A, respectively. H A extracts NH4, NO3 and P from soil. The extractant, H A, is made up of organic root exudates, lithium citrate, and two synthetic chelators (DTPA, EDTA)

(Haney et al., 2006). The water and H3A extracts were analyzed on a flow injection analyzer (Lachat 8000, Hach Company, Loveland CO) for NO3-N, NH4-N, and PO4-P.

The water extract was analyzed on a Teledyne-Tekmar Torch C:N analyzer for water- extractable organic C and total N (WEOC and WEON). WEOC and WEON are the fraction of soil organic carbon and nitrogen present that is mobile and bioavailable to the microbial community (Grebliunas et al., 2016; Zhang et al., 2011; Zsolnay, 1996). The

H3A extract was also analyzed on a Thermo Scientific ICP-OES instrument for P, K, Mg,

Ca, Na, Zn, Fe, Mn, Cu, S and Al (Haney et al., 2018).

The Haney Soil Health Score provides a general estimate of the overall health of a soil system. The score combines five independent measurements of soil biological and chemical properties, consisting of NH4-N, NO3-N, WEOC, WEON, and 1 day CO2-C.

The calculation examines the balance of soil C and N and their relationship to microbial activity. This soil health calculation number can vary from 0 to > 50 (Haney et al., 2018).

The Haney Soil Health Score is calculated as 1-d CO2-Carbon/10 plus Water Extractable

Organic Carbon (WEOC)/50 plus Water Extractable Organic Nitrogen (WEON)/10.

Soil classification, bulk density, and gravimetric moisture percentage

Soil classification and surface bulk density (BD) samples were collected in three plots per farm, following the protocol outlined by the NRCS (2017). These samples were

collected during the fruiting period in 2018 and during the bloom period in 2019. The BD cores were collected next to the soil cores destined for TSC/TSN analysis. A metal cylinder (7.62 cm wide and 12.7 cm tall) was hammered to a depth of 8 cm. Wet weights of each soil core were recorded. The samples were then allowed to air dry for at least 40 wk at ~28˚ C. Soil samples were then microwaved to constant weight (Usmen & Kheng,

1983). We compared the gravimetric soil moisture percentages for all the orchards as well as just the 2019 orchards, which were sampled in early March and therefore could not be affected by variations in irrigation regimes. This weight was recorded to the 0.01 g and used to calculate BD and the soil’s gravimetric moisture percentage. BD is calculated by dividing the mass of the dry sample by the volume of the cylinder. The soil gravimetric moisture percentage was calculated by subtracting the dry weight from the wet weight and then dividing by the dry weight. These soil samples (> 200 g) were then analyzed for their sand, silt, and clay percentages using the hydrometer technique (OSU,

2017). Prior to starting the particle size separation steps, the sample was dried and particles greater than 2 mm were removed, the sample was weighed, then organic matter and any other potential cementing-agents were removed. Sodium hexametaphosphate was added to the suspension and placed on a shaker overnight to overcome flocculation during settling. Suspension was measured using a hydrometer in 1 L of water at multiple time points to determine the specific gravity of the suspension (OSU, 2017). We determined that there were no treatment level biases in soil textures, pH, and clay percentages. The volume of soil in each core was estimated, and the samples were transferred to paper bags.

Water infiltration rates

Water infiltration rates were measured twice per orchard in 2019 (four regenerative and four conventional) during the bloom and fruit development stages.

Samples were taken from the middle of the tree row at randomly selected locations. We followed the NRCS protocol, where 444 mL of water was poured into a sheet-metal ring

(15.2 cm diam, 13.5 cm tall), which was hammered 6.5 cm into the soil (Doran, 1999).

The time until all the water saturated into the soil was recorded to the nearest second.

During the fruiting period in the Chico conventional orchard, the infiltration time assigned was 8,077 s with 1 cm of water remaining in the ring. This process simulates an instantaneous 5 cm of rainfall (Doran, 1999). The two samples were averaged for each farm.

Total soil carbon and nitrogen (0-60cm)

Soil samples were collected in each plot to determine TSC and total soil nitrogen

(TSN). Random samples were taken halfway between the tree and the drip line within the wetting zone of the sprinkler/emitter . The probe (2.54 cm × 91.44 cm Plated Replaceable

Tip Probe w/ 61 cm Window and Hammer Head Handle, AMS, American Falls, ID) was inserted 60 cm deep and the resulting soil samples (2.28 cm diameter) were partitioned into 0-5, 5-10, 10-15, 15-30, 30-45, and 45-60 cm depths. Each section of the core was placed into a plastic bag that was stored on ice until it could be transferred to a paper bag in the laboratory. Samples were weighed to the nearest 0.1 g, and then were air dried for at least 12 wk before they were prepared for elemental analysis of TSC and TSN. The air-

dried weight of the soil was recorded to 0.01 g. All visible pieces of rock and organic matter were removed from the samples using tweezers, and the samples were ground using a sterilized mortar and pestle. Samples were then passed through a sieve with

0.180 mm openings. The samples were stored in manilla envelopes in a desiccator. Soil

(12-15 mg) was removed from the manilla envelope and placed into tin capsules (5 × 9 mm, Costech, Valencia, CA) for elemental analysis. For each soil depth, three subsamples underwent elemental analysis (ECS 8020, NC Technologies, Milan, Italy). To calibrate the analysis, each group of samples on a multi-sample plate consisted of five bypass samples that removed any gas or residue from the machine (12-15 mg of soil), two blanks, and four standards, 0.5-2.0 mg acetanilide (Costech, Valencia, CA), followed by the soil samples (12-15 mg). A standard was placed between every 10 soil samples to ensure accurate results. To control for the relative compaction and other circumstances associated with each vertical depth, the mass (Mg) of TSC per ha was assessed using the

Equivalent Soil Mass (ESM) method, in which a cubic spline of Mg of TSC per depth layer was calculated (Wendt & Hauser, 2013). This resulted in the assessment of carbon as Mg of TSC/ha at the following ESM layers, and the average calculated depth for each reference mass is presented in parentheses: 500 Mg (6.1 cm), 1,000 Mg (11.0 cm), 1,500

Mg (15.8 cm), 3,000 Mg (30.4 cm), 4,500 Mg (45.2 cm), and 6,000 Mg (59.2 cm).

Microbial community

Phospholipid fatty acid profiles were used to characterize the microbial communities. Soil cores (10 cm depth, 1.9 cm diam), were taken from four replicates per

plot during the fruiting period. The samples were taken at random locations halfway between the tree and the drip line within the wetting zone of the sprinkler/emitter within each replicate, at least 5 m apart, using a transect that diagonally bisected the plot. All samples were collected within the same week; samples on each paired regenerative and conventional orchard occurred within 24 hours. The 16 soil cores for each orchard were combined in a sealed plastic bag and placed on dry ice (Drenovsky et al., 2010). Soil samples were stored at -80˚ C until they could be freeze-dried and ground to 2 mm particle sizes. The microbial biomass and community composition were recorded as Total microbial biomass, Undifferentiated microbial biomass, Total bacteria, Gram-positive bacteria, Actinobacteria, Gram-negative bacteria, Rhizobia bacteria, Total fungi,

Arbuscular mycorrhizal fungi, Saprophytic fungi, and Protozoa. PLFA testing was performed by Ward Laboratories in Kearney, NE.

Plant community

Percent ground cover and composition in each of the replicates/plots was recorded during each of the three sampling periods. The percent ground cover was categorized as

0-25%, 25-50%, 50-75%, and 75-100%. Percent ground cover was assessed using visual assessments in each invertebrate quadrat (Hanley, 1978). The community composition and whether the ground cover was resident vegetation or planted was determined using information from farmer surveys and direct field observations.

Invertebrate community

The epigeal invertebrate communities were sampled using a 15 cm tall 0.25 m2 sheet metal quadrat (LaCanne & Lundgren, 2018; Lundgren et al., 2006). The quadrats were placed at two random locations in the inter-row areas of each plot, and invertebrates collected were combined. Sampling of the invertebrate communities occurred during the bloom, fruit development, and harvest periods. The invertebrate communities were collected from the soil surface and top 2 cm of the soil with mouth-operated aspirators over 15 min, and were preserved in 70% ethanol. The biomass of the invertebrates per 0.5 m2 were weighed to the nearest 0.0001 g. Invertebrates were identified to the morphospecies level. Voucher specimens are all housed in the Mark F. Longfellow

Biological Collection at Blue Dasher Farm, Estelline, SD, USA.

Pest Density/Damage

To estimate pest incidence, we assessed the pest damage on 500 almonds per farm in 2018 and 600 almonds per farm in 2019 (< 20 from any one tree) (Bentley et al., 2001;

Doll, 2009). In 2018, 125 almonds were randomly collected from the orchard floor diagonally across each plot, and almonds from each farm were pooled across plots. The samples were collected identically in 2019, but 150 almonds were collected per plot and were analyzed per plot. Almonds were stored in a -20˚ C freezer until they were inspected. The almonds were each categorized as having no pest damage, navel orange worm damage (Amyelois transitella [Walker]; Lepidoptera: Pyralidae), ant damage

(Formicidae), oriental fruit moth damage (Grapholita molesta [Busck]; Lepidoptera:

Tortricidae), peach twig borer damage (Anarsia lineatella Zeller; Lepidoptera:

Gelechiidae), leaf footed plant bug or stinkbug damage (Hemiptera: Coreidae,

Pentatomidae), and unknown pest damage (Bentley et al., 2001; Doll, 2009; Symmes,

2018). Almonds were regarded as having no serious pest damage if at worst they had slight discolorations and/or indentations, indicative of minor LFPB or stinkbug damage, while still making an edible USDA grade (USDA, 1998).

Nutrient density

We quantified the nutrient composition of 500 almonds per orchard in 2018 and

600 almonds per orchard in 2019 (< 20 from any one tree). In 2018, 125 almonds were randomly collected from the orchard floor diagonally across each plot, and almonds from each farm were pooled across plots. The samples were collected identically in 2019, except the 150 almonds per plot were not pooled across plots. Almonds were analyzed for the following nutrients at mg/g basis: Total Carbon, Total Nitrogen, Phosphorus,

Potassium, Calcium, Magnesium, Sodium, Sulfur, Zinc, Iron, Manganese, Copper,

Boron, Aluminum, Molybdenum (Regen Ag Labs, Pleasanton, NE). To quantify the overall nutrient profile of the almonds we utilized the Shannon index (H)’, Simpson index (DS), and Peilou’s community evenness index (J). Briefly, almonds (30 g; stored at

-20 C) were ground to pass through a 1 mm sieve. Total Carbon and Nitrogen was determined via dry combustion elemental analysis using a LECO 928C/N analyzer

(LECO, St. Joseph, MI). For the other nutrients, the ground almonds were digested with

nitric and hydrochloric acid to burn off organic matter. Hydrogen peroxide was then added to dissolve any fats and oils within the sample. The samples were brought to a high boiling point and then cooled under the hood. Once cooled, the samples were brought to volume, mixed, and filtered. Finally, the digested samples were placed into test tubes and the nutrient levels were determined via inductively coupled plasma mass spectrometry.

Economic analyses

A producer survey was used to determine management practices, costs, and revenues that contributed to the direct net profitability of each operation. Under production operating costs, the study included costs associated with winter sanitation, sampling for tree nutrient status and soil salinity, pH, and nutrient levels, irrigation and frost protection, fertilizers, insecticides, herbicides, fungicides, disease treatment sprays, trapping vertebrate pests, cover crop seed/bag, tillage, mowing, flamers, grazers, and harvest. Within the harvest category the study factored the hourly labor to conduct the harvest and/or the price paid to external contractors, kernel kg/ha, returns, and additional revenue streams such as almond hulls and co-products, as well as returns on harvesting the grazing livestock (Klonsky et al., 2016). No farm in the study reported additional revenue from grazers or additional revenue streams such as almond hulls, but two regenerative orchards reported revenues from selling value added products, such as almond butter. Under labor and operating costs, the study included people hours worked at a rate of $18.90/h (Klonsky et al., 2016; Yaghmour et al., 2016). For the yield analysis, one conventional and one regenerative orchard from the Capay Valley were not included.

For the profitability analysis, in addition to the two orchards we excluded from the yield analysis, we omit an additional regenerative orchard, as its 2019 revenue numbers have not yet come in (N = 13).

Data analysis

Unless otherwise noted, data are reported as mean values followed by +/-SE and α

= 0.05. Analyses were performed in R (R development Core Team, https://www.r- project.org/, version 4.0). The study used the lme4 package to create the Linear Mixed

Models (Bates et al., 2015). To calculate the biodiversity indices we used the vegan:

Community Ecology package (Oksanen et al., 2019). For non-parametric statistical calculations and comparing models the study used the RVAideMemoire: Testing and

Plotting Procedures for Biostatistics and the Modern Applied Statistics with S. Fourth

Edition packages (Brian et al., 2002; Hervé, 2020). Finally, for linear regression analysis and performing the Bonferroni Outlier Test, the analyses used the Companion to Applied

Regression (car) (Fox & Weisberg, 2019). The R base package was used for the multivariate analyses and micompr (Fachada, 2018) was used to ensure the assumptions were met for those analyses (Supplemental materials). Rstatix (Kassambra, 2020) was used to determine collinearity. Th R base package and ggplot2 (Wickham et al., 2020) were used for the Principal Component Analysis (PCA).

We tested whether regenerative or conventional orchards differed in their TSC,

TSN, bulk density, water infiltration rates, macro and micro nutrients, microbial communities, invertebrate biomass, invertebrate and Arthropoda abundance, the

Shannon-Wiener Index (H’) and Simpson (DS) diversity indices, species richness, species evenness, pest damage, the particular nutrient levels of the almonds as well as nutrient distribution in the almonds as determined by H’, DS, and Pielou’s Evenness (J), yield, revenue, costs, and profitability. As is standard practice, the vegan package log transformed the count of the invertebrate morphospecies to derive the H’ index. As appropriate, Linear Mixed Models, One-way ANOVA and Welch Two Sample t-tests were used to determine whether the treatments differed. If the data were normally distributed and we only had one sample period per farm, we ran the Welch Two Sample t-test (degrees of freedom were rounded down to the nearest integer) When data violated normality assumptions, but the residuals were normally distributed, and we had one sample period per farm we used One way ANOVA. If the residuals of the data were normally distributed and there were ≥ 2 sample periods per farm a Linear Mixed Model was used. In certain cases where the data and the residuals were not normally distributed, the non-parametric Mood’s Median Test was used. In cases where LMMs were used to examine treatment differences (i.e., nutrient quantity of the almond), treatment was a fixed factor, and farm was a random factor. MANOVA was used to confirm the patterns in soil and biological response variables that were revealed by our univariate assessments, and investigate whether or not colinearity among the response variables was a driver of these relationships( A2).

To analyze the TSC and TSN soils data we built two LMM’s. To investigate how

TSC varies across layers in regenerative and conventional orchards, we ran a LMM model where we designated treatment and clay percentage as fixed factors, with farm

being the random factor (ESM Layer TSC/TSN × Treatment(F) + Clay%(F) + Farm(R),

Number of observations = 49, groups: Farm = 16). The second LMM designated the years regenerative, years conventional and clay percentage as fixed factors, with farm being the random factor (ESM Layer TSC/TSN × Matrix Score(F) + Clay%(F) +

Farm(R), Number of observations = 49, groups: Farm = 16). These two models were run for each ESM depth layer. As we only took three surface bulk density/soil texture samples per farm, one TSC/TSN soil sample per farm was randomly excluded from the

LMMs. When examining TSC between the 3000-6000 Mg ESM layers, we log transformed the Mg TSC/ha data because the distribution of the residuals did not fit a normal distribution (Shapiro-Wilks normality test). Due to the current interest in utilizing regenerative management to sequester carbon we isolated the effect of each regenerative practices by analyzing the effect of all the regenerative management practices and soil clay percentage on TSC via multiple regression.

To analyze the relationship between invertebrate biodiversity and pest damage we built three separate LMM’s with H’, DS, and species richness as the fixed factors and farm as the random factor (Diversity Index x Treatment (F) + Farm (R); Number of Obs

= 40. Groups: Farm =1 6). To analyze differences in nutrient levels and the distribution of nutrients within the almonds we used a LMM with treatment as the fixed factor and the farm as the random factor (Nutrient/Nutrient Distribution x Treatment (F) + Farm (R);

Number of obs = 40, groups: Farm = 16). The Shapiro-Wilks test was used to quantify the distribution of the linear model residuals.

Principle Component Analysis (PCA) was used to assess the interactions among the soil quality and biological metrics, as well as the importance of management practices in determining those metrics. Further, this analysis was used to determine whether our designation of regenerative and conventional orchards accurately distinguished the response variables we observed. To evaluate the association between management practices with the first two components of the ordination, we conducted Spearman rank correlations. The False Discovery Rate correction (FDR) was used to adjust for multiple comparisons. Similarly, Spearman rank correlations were used to assess the degree of association among the soil quality and biological metrics. Since the PCA used a wide range of metrics we centered and scaled the model.

Results

Soil nutrients and health

Soil macronutrients and micronutrients were calculated on a per farm basis (N =

16) (Table 3.2). Regenerative orchards contained higher levels of Water Extractable

Organic Matter (WEOM), which is comprised of WEON and WEOC. Regenerative orchards also had higher levels of total Phosphorus, inorganic Phosphorus, available

Phosphorus, Calcium, and Sulfur. Conventional orchards had significantly more aluminum than regenerative orchards. The soil quality index, Haney Soil Scores, were higher in regenerative orchards (Figure 3.1).

Soil bulk density, water infiltration, and gravimetric moisture percentage

The soils in regenerative orchards had lower bulk densities and more effectively infiltrated water. The mean surface bulk density in regenerative orchards was 1.16 ± 0.02

3 3 2 g/cm , and in conventional orchards it was 1.33 ± 0.05 g/ cm (N = 16; R adj = 0.45, F2, 13

= 7.02, treatment P = 0.004, clay P = 0.21). In conventional orchards, the mean water infiltration rate was 3855 ± 1213 s, and in regenerative orchards it was 609 ± 302 s (N =

2 8; R adj = 0.45, F1, 6 = 6.74, P = 0.04) (Figure 3.2). Gravimetric soil moisture percentage was higher in regenerative orchards when comparing all the orchards and when only the

2019 orchards were compared (orchards in which we tested for water infiltration). The mean soil gravimetric moisture percentage of all the regenerative orchards was 25 ± 2%

2 and for all the conventional orchards it was 18 ± 2% (N = 16; R adj = 0.22, F1, 14 = 5.21 P

= 0.04).

Total soil carbon and nitrogen and soil organic matter

Regenerative orchards had significantly higher levels of TSC, TSN, and SOM through all the ESM strata (0-6000 Mg ESM layers) (Figure 3.3; Table 3.3). Through the

0-6000 Mg ESM layer (Total TSC Mg /ha) regenerative orchards had 54.52 ± 4.76 Mg

TSC/ha, while conventional orchards contained 41.37 ± 7.27 Mg TSC/ha. Through the

6000 Mg ESM layer, regenerative orchards had significantly higher TSC, and the amount of TSC increased as soil clay increased (Table 3.3). Through the 6000 Mg ESM layer, regenerative orchards contained 5.96 ± 0.45 Mg TSN/ha, while conventional orchards contained 5.00 ± 0.53 Mg TSN/ha (Table 3.3). The TSC:TSN ratio in regenerative

orchards through the 6000 Mg ESM layer was 9.42 ± 0.41 and in conventional orchards it

2 2 was 8.01 ± 0.66 (N = 49; treatment: χ 1 = 3.46, P = 0.06; clay: χ 1 = 7.13, P = 0.01).

Regenerative orchards had significantly more SOM% than conventional orchards, 3.88 ±

0.39 and 2.39 ± 0.47%, respectively (N = 16, P = 0.03, t-13 = 2.45).

Total Soil Carbon and TSN varied by treatment among the different soil depths.

In the top 1500 Mg ESM of soil, regenerative orchards had significantly more TSC and

TSN, regardless of how much soil clay was present (Table 3.3). Clay became a significant factor in determining TSC at the 1500 Mg ESM layer, but with TSN, clay was not a significant factor until the 3000 Mg ESM layer (Table 3.3). The greatest percentage of TSC was found at the 0-500 Mg ESM layer. In regenerative systems the 0-500 Mg

ESM layer accounted for 32% of the systems’ TSC and 30% of its TSN. In conventional systems it accounted for 19% of the TSC and 17% of the TSN. The 0-1500 Mg ESM layer accounted for 54% of the TSC and 50% of the TSN in regenerative systems, while in conventional systems this layer contained 39% of the TSC and 35% of the TSN. The

0-3000 Mg ESM layer accounted for 72% of the TSC and 69% of the TSN in regenerative systems and 63% of the TSC and 59% of the TSN in conventional systems.

Between the 3000-6000 Mg ESM layers there was no difference in TSC (N = 49;

2 2 treatment: χ 1 = 0.01; P = 0.94; clay: χ 1 = 37.09; P < 0.001) and TSN (N = 49; treatment:

2 2 χ 1 = 0.93, P = 0.33; clay: χ 1 = 52.07, P < 0.001) based on treatment.

The duration that these orchards had been in their respective systems had significant effects on soil carbon. The year by management type interaction term of the linear mixed model was significant. Through the 3000 Mg ESM layer, number of years

2 under regenerative management correlated with increasing TSC (N = 49; χ 1 = 5.86, P =

2 0.02), while conventional management correlated with decreasing TSC (χ 1 = 4.85, P =

2 0.03; clay: χ 1 = 31.88, P < 0.001). Similarly, when we examined 0-6,000 Mg ESM layer in relation to time, regenerative management correlated with the buildup of TSC (N = 49;

2 2 treatment: χ 1 = 8.07; P = 0.005; clay: χ 1 = 33.00; P < 0.001), and years under conventional management and soil clay percentage correlated significantly with

2 2 reductions in TSC (N = 49; treatment: χ 1 = 10.20; P = 0.001; clay: χ 1 = 42.13; P <

0.001).

Ground cover was the parameter best correlated with TSC through the soil column. Multiple regression models revealed that keeping 75-100% of the ground covered with vegetation was the sole practice that scaled with TSC through the 6,000 Mg

2 ESM layer (N = 16; R adj = 0.80, F7, 8 = 9.68, model P = 0.002, ground coverage P =

0.004, clay P = 0.01).

Microbial community

Several metrics of soil microbial community structure were significantly different between regenerative and conventional orchards (N =16; Tables 3.4 & A2). Soils in regenerative orchards contained greater total microbial biomass (ng/g) (Figure 3.4), total bacterial biomass, Gram (+) biomass, and Actinobacteria biomass (Tables 3.4 & A2). For many of these microbial community metrics there was no statistical difference between the regenerative and conventional orchards (P > 0.05). Conventional orchards never had greater metrics of microbial community structure than regenerative orchards.

Plant community

Regenerative orchards had significantly more plant cover on the soil surface than conventional orchards. For regenerative orchards, the median ground coverage percentage category was 75-100% and for conventional orchards it was 0-25% (N = 16;

2 χ 1 = 9.14, Mood’s P = 0.002). Additionally, regenerative orchards averaged more plant species on the floors of their orchards. The mean number of plant species on the orchard floor in regenerative orchards was 7.0 ± 0.7 and in conventional orchards it was 2 ± 0.7

2 (N = 16; R adj = 0.60, F1, 14 = 23.14, P < 0.001).

Invertebrate community

We identified 275 different invertebrate morphospecies (270 arthropod morphospecies) from almond orchards, totaling 12,414 individuals. Larvae and adults were considered separate morphospecies, as adults and larvae serve distinct ecological functions (Table A3). The invertebrate species consisted of eight classes across 23 orders:

Araneae, Blattodea, Coleoptera, Dermaptera, Diptera, Entomobryomorpha,

Geophilomorpha, Hemiptera, Hymenoptera, Julida, Lepidoptera, Lithobiomorpha,

Neuroptera, Oniscidea, Opiliones, Opisthopora, Orthoptera, Psocoptera, Solifugae,

Spirobolida, Stylommatophora, Symphypleona, Thysanoptera. The orders containing the highest number of morphospecies were: Coleoptera (99), Hemiptera (39), Araneae (36),

Diptera (30), and Hymenoptera (28). The most abundant orders were Entomobryomorpha

(3,785), Opisthopora (2,136), Hymenoptera (1,527), Coleoptera (1,274), Oniscidea (986),

Araneae (827), Diptera (347), Hemiptera (305), Dermaptera (157), Lithobiomorpha

(131).

Invertebrate community structure was significantly different in the regenerative and conventional orchards. Invertebrate biomass (N = 16; t12 = -3.15, P < 0.001) and abundance (N = 16; t13 = -3.86, P = 0.002) were significantly higher in regenerative orchards. Invertebrate biomass in regenerative orchards was 5.94 ± 0.54 g per m2, while the invertebrate biomass in conventional orchards was 1.09 ± 0.39 g per m2 (Figure 3.5).

Invertebrate abundance in regenerative orchards was 173.92 ± 17.88 per m2, while invertebrate abundance in conventional orchards it was 84.71 ± 14.68 per m2. Arthropod

(excluding Annelida and Gastropoda) abundance was significantly greater in regenerative orchards than in conventional orchards (N = 16; t12 = -2.42, P = 0.03). The mean arthropod abundance in regenerative orchards was 141.16 ± 23.55 per m2 and the mean arthropod abundance in conventional orchards was 72.60 ± 15.68 per m2. Mean earthworm (Lumbricina) abundance in regenerative orchards was 24.70 ± 6.18 m-2, while

2 earthworm abundance in conventional orchards was 4.92 ± 1.60 per m (n = 16; t7 = -

3.10, P = 0.01).

Regenerative orchards were significantly richer in invertebrate and arthropod morphospecies, while having more evenly distributed arthropod communities. The mean species richness of all invertebrates in regenerative orchards was 13.28 ± 1.32 and the mean species richness in conventional orchards was 6.66 ± 0.92 (N = 16; t12 = -4.13, P =

0.001). The mean species richness of arthropods in regenerative orchards was 12.31 ±

1.32 and in conventional orchards it was 6.08 ± 0.84 (N = 16; t12 = -3.97, P = 0.002). The

mean J’ of invertebrates in regenerative orchards was 0.70 ± 0.02 and in conventional orchards it was 0.68 ± 0.04 (N = 16; t11= -0.45, P = 0.66). The mean J’ of arthropods in regenerative orchards was 0.76 ± 0.02 and in conventional orchards it was 0.67 ± 0.04 (N

= 16; t11 = -2.15, P = 0.049).

Regenerative orchards were significantly more diverse than conventional orchards, with higher levels of invertebrate and arthropod H’ and DS. The mean H’ of all invertebrates in regenerative orchards was 1.69 ± 0.12 and the mean H’ in conventional orchards was 1.10 ± 0.12 (N = 16; t13 = -3.34, P = 0.01) (Figure 3.5). Further, invertebrate DS in regenerative farms was 0.68 ± 0.04 and was 0.51 ± 0.05 in conventional farms (N = 16; t13 = -2.87, P = 0.01). These findings continue to hold true when we assessed arthropod H’ and DS. In this case, H’ of arthropod morphospecies in regenerative orchards was 1.75 ± 0.10 and H’ in conventional orchards was 1.05 ± 0.12

(N = 16; t13 = -4.40, P = 0.001). DS of arthropod morphospecies in regenerative orchards was 0.72 ± 0.03 and in conventional orchards it was 0.49 ± 0.05 (N = 16, t10 = -4.22, P =

0.001).

Pest damage

Pest damage was similar in regenerative and conventional orchards. The mean percentage of almonds without damage in regenerative orchards was 92.6 ± 2.5%, while the mean percent undamaged almonds in conventional orchards was 91.0 ± 1.3% (N = 16

2 R adj = -0.04, F1, 14 = 0.33, P = 0.57). Running the Bonferroni P outlier test indicated that one regenerative orchard was a significant outlier (P = 0.004). Without this outlier the

mean percent of undamaged almonds in regenerative orchards increases to 94.9 ± 1.1%

2 (N = 16, R adj = 0.21, F1, 14 = 4.52, P = 0.055). There were significant correlations

2 2 between invertebrate diversity (H’: N = 40; χ 1 = 7.34, P = 0.01, DS: N = 40; χ 1 = 4.23,

2 P = 0.04) and species richness (N = 40; χ 1 = 4.18, P = 0.04) and reduced pest damage.

Nutrient density

One metric of almond nutrient density was significantly different between regenerative and conventional orchards (N = 16; Table 3.5). Almonds in regenerative orchards contained higher levels of magnesium (Table 3.5). To better understand this difference, we ran a LMM with years under regenerative management and years under conventional management as fixed factors. This model reveals that magnesium is negatively correlated with years under conventional management (N = 40; years

2 2 conventional: χ 1 = 5.22, P = 0.02; years regenerative χ 1 = 0.60, P = 0.27). Further, there was no difference between regenerative and conventional orchards when we used H’, DS, or J to examine the overall nutrient profile of the almonds (P > 0.05) (Table 3.5).

Yield and profitability

There was no difference in yield between conventional and regenerative orchards

(N = 14; t11 = 1.45, Welch P = 0.17). The yields in conventional orchards ranged from

538 kg/ha (480 lb/ac) to 3,026 kg/ha (2,700 lb/ac), with the yields in regenerative orchards ranging from 705 kg/ha (629 lb/ac) to 2,466 kg/ha (2,200 lb/ac).

Regenerative orchards were approximately twice as profitable as conventional orchards (Figure 3.6). The profitability of regenerative orchards (including value added products like almond butter, etc.) was $15,055 ± 2,853 per ha ($6,093 ± 1,155 per ac).

Since none of the conventional orchards sell value-added product, we only use the profit/revenue associated from selling solely almonds in subsequent analyses. The profitability of regenerative orchards not accounting for value-added products was significantly higher in the regenerative orchards versus the conventional orchards ($5,299

± 1,090 vs $2,877 ± 733 per ac) (N = 13; t9 = -2.32, P = 0.045) (Figure 3.6). The operating costs of regenerative orchards were $3,402 ± 425 per ha and for conventional orchards they were $2,494 ± 90.32 per ha (N = 15; t6 = -0.86, P = 0.42). Gross revenue of regenerative orchards was $18,178 ± 3,033 per ha and for conventional orchards it was

$9,587 ± 1,851 per ha (N = 13; t8 = -2.42, P = 0.04).

Interactions among soil, biodiversity, and management practices

PCA confirmed our distinct treatments, and revealed that nearly all management practices were correlated with improved soil and biological properties of the orchards.

Two distinct clusters separated along the first principal component, relating to farms being designated as regenerative or conventional (Figure 3.7). All of the soil quality and biological community metrics (bulk density, TSC, TSN, TSP, microbial biomass, actinobacteria biomass, invertebrate H’, and invertebrate biomass) were significantly correlated with the first component of the ordination plot, with the first component explaining 61.48% of the variance (Figure 3.7). Additionally, the correlations showed

that, moving towards the left side of the plot along principal component 1, orchards had lower soil bulk densities, higher levels of TSC, TSN, and TSP, greater microbial and actinobacteria biomasses, and larger and more diverse invertebrate communities (Figure

3.7). Permanent ground coverage, avoiding synthetic agrichemical inputs, and using organic amendments/compost teas were significantly correlated with the first component of the ordination plots (Figure 3.7). These correlations demonstrated that these are the critical management practices driving the improved soil quality and biological community metrics associated with regenerative orchards. Of the soil quality and biological community metrics, only microbial and actinobacteria biomass were significantly correlated with the second principal component, with none of the management practices significantly correlating to the second principal component (Figure

3.7). These correlations showed that moving down the plot towards the bottom results in greater microbial and actinobacteria biomasses. However, as none of the management practices correlated to the second principal component, it seems likely that there may be other correlated factors outside of the analysis that may not be causal (Figure 3.7). We also analyzed whether gravimetric soil moisture (PC1: P = 0.08, PC2: P = 0.89) or soil clay percentage (PC1: P = 0.96, PC2: P = 0.11) correlated to either to first or second principal component and neither of these variables were significantly correlated.

Discussion

Orchards under regenerative management had better soil quality, biological, and economic metrics than orchards under conventional management. Conventional and

regenerative orchards had similar levels of pest incidence, but the two systems achieved this via different strategies. Conventional orchards had highly simplified landscapes and relied on synthetic pesticide applications. In contrast regenerative orchards maintained permanent ground cover, containing significantly more plant species. We believe that the more complex landscape found in regenerative orchards combined with their abstinence from spraying synthetic insecticides drove the significantly higher invertebrate diversity levels seen in regenerative orchards. Further, we found a significant correlation between increasing invertebrate diversity and reduced pest incidence(Chaplin-Kramer et al., 2011;

Demestihas et al., 2017; Eilers & Klein, 2009; LaCanne & Lundgren, 2018; Lundgren &

Fausti, 2015; Lundgren et al., 2006). PCA analysis demonstrated that the soil quality metrics (TSC, TSN, phosphorus, bulk density) and the biological properties (microbial biomass, Actinobacteria biomass, invertebrate biomass, and invertebrate biodiversity) of the orchards were significantly correlated, with the improved metrics of the regenerative orchards generated by the farmers combining multiple regenerative practices to create a farming system greater than the sum of its parts.

Soils

The soils in regenerative orchards contained higher levels of total phosphorus, inorganic phosphorus, available phosphorus, calcium, and sulfur, while receiving higher

Haney soil quality scores. Multiple studies have found that soils with minimal disturbance and permanent ground cover provide significant benefits with regards to bulk density, soil carbon, and the labile soil organic matter pool (Awale et al., 2017; Blanco-

Moure et al., 2016; Kosmas et al., 1997; LaCanne & Lundgren, 2018; Soto et al., 2021;

Steenwerth & Belina, 2008). With global supplies of phosphorus being depleted, and soil erosion being recognized as a key factor (Alewell et al., 2020), making phosphorus levels within the soils bioavailable to plants will be critical to maintain agricultural productivity.

Calcium and sulfur are important nutrients for balancing soil texture and pH, and are essential nutrients for plants (Duke & Reisenauer, 1986; Tuteja & Mahajan, 2007).

Although regenerative practices increased the abundance of specific nutrients, additional research is necessary to know whether regenerative systems have a more favorable balance of these nutrients for plant and animal growth (Parent et al., 2012). The Haney soil health score helps to give a picture of the overall balance of the soils chemical and biological properties that theoretically should reflect the relative abundances of micronutrients like calcium and sulfur, since these nutrients affect the health and productivity of soils. This study shows that the higher nutrient levels in regenerative orchards are due to these orchards integrating multiple regenerative practices which foster robust microbial and invertebrate communities and build topsoil, with conventional orchards are losing topsoil to erosion (Martínez-Mena et al., 2020).

Compared to conventional orchards, regenerative orchards had 32% more TSC and 19% more TSN. These results generally align with (Soto et al., 2021) that found regenerative orchards using no till and organic amendments had 31% more SOC and 16% more TSN in the upper 20 cm of soil. The greatest percentage of TSC and TSN occurred in the upper soil levels, with the highest percentage in the 0-500 Mg ESM layer and no difference among regenerative and conventional orchards in the 3000-6000 Mg ESM

layer (Table 3.4). Furthermore, the concentrations of TSC and TSN in the upper soil layers suggest that orchards’ stock TSC and TSN are sensitive to orchard floor management decisions (Demestihas et al., 2017; Montanaro et al., 2017). This notwithstanding, there was value in sampling 15 cm below the root zone of almond orchards, as we did in this study (Olson & Al-Kaisi, 2015). We only observed changes in soil carbon and nitrogen in the 0-3000 Mg EMS layer (0-30 cm) (Table 3.4), which may be because nearly all almond roots are in the 0-45 cm depth. Still, while the majority of the TSC and TSN occurs in the 0-3000 Mg ESM layer, 27% of the TSC stocks of regenerative orchards and 40% of the TSC stocks of conventional orchards occur in the

3000-6000 Mg ESM layer (Table 3.4), signifying the importance of sampling to greater depths to get a more thorough accounting of almond orchards’ carbon stocks (Olson &

Al-Kaisi, 2015; Tautges et al., 2019). While this study did not conduct a carbon life cycle analysis, the fact that regenerative orchards contained an additional 13.15 Mg/carbon/ha

(48.26 Mg/CO2 equivalents/ha), 30% more than their conventional counterparts, suggests that regenerative management in almond orchards could play a significant role in carbon sequestration.

The duration of conventional or regenerative management strongly affected soil carbon levels. The literature suggests that orchards which eliminate spatiotemporal events of bare soil while integrating organic amendments may be able to eliminate their carbon losses and build soil carbon over time (Demestihas et al., 2017; Kosmas et al., 1997;

Martínez-Mena et al., 2020; Montanaro et al., 2017; Soto et al., 2021). This study’s models support this hypothesis with the model, indicating positive correlations between

years in regenerative farming and increasing soil C. In a meta-analysis of cover cropping and soil carbon sequestration rates, Poeplau and Don (2015) found that the integration of cover crops increased soil carbon sequestration at a rate of 0.32 ± 0.08 Mg/ha/yr through a sampling depth of 22 cm. Similarly, Ryals & Silver (2013) found a one-time compost application to grazed grasslands increased soil carbon accumulation at a rate of 1

Mg/ha/yr. Our findings suggest that regenerative management in almond orchards can play a critical role in preventing carbon losses, while also sequestering carbon. However, longer term studies, particularly transitional studies, are needed to ascertain a more exact rate at which regenerative orchards are building TSC and conventional orchards are losing it. Additionally, our results show soil clay percentage to be a significant determinant, supporting the inclusion of soil textures in future studies on this topic.

Water

When compared to conventional orchards, regenerative orchards had lower soil bulk densities, higher gravimetric moisture percentage, and faster water infiltration rates.

The same patterns were observed before and after irrigation. Research indicates that greater porosity is found in soils with lower bulk densities, thereby allowing water to infiltrate more effectively through the soil (Weil & Brady, 2017). Additionally, increasing soil carbon improves the water holding capacity of soils; one estimate is that each 1% increase in soil organic matter increases a soil’s water holding capacity by

187,000 L/ha (Nichols, 2017). The higher soil moisture percentages found in the regenerative orchards suggest that regenerative orchards more effectively retained water

than their conventional counterparts. Additionally, Martinez-Mena et al. (2020) found that implementing regenerative practices in almond orchards reduced water run off by

65%. The faster water infiltration rates and higher soil moisture percentages found in regenerative orchards, combined with their significantly higher levels of soil carbon/organic matter, suggests the regenerative management in almond orchards could assist California in meeting its Sustainable Groundwater Management Act ground water recharge goals.

Plant community

Higher levels of soil carbon in the regenerative orchards can be attributed in part to ground cover protecting soil aggregates, reducing erosion rates (Abdalla et al., 2020;

Kosmas et al., 1997; Montanaro et al., 2017; Peregrina et al., 2010; Soto et al., 2021), and lowering decomposition rates by lowering soil temperatures (Almagro et al., 2016;

Blanco-Canqui & Francis, 2016). Of all the management practices analyzed in the model, permanently maintaining orchard floor ground cover was significantly correlated with

TSC at the 0-6,000 Mg ESM layer. Although maintaining ground cover is important, multi-functional almond orchards require an interwoven system of regenerative practices to be successful (Fenster et al., 2021).

Compared to conventional orchards, regenerative orchards averaged more plant species and significantly more plant biomass on their orchard floors, with a median ground coverage percentage category of 75-100%, while the median for conventional orchards was 0-25%. As the primary avenue through which energy enters the ecosystem,

plants are a critical component of the carbon cycle (Weil & Brady, 2017), and drive the biodiversification of the entire system (Bianchi et al., 2006; Saunders et al., 2013; Zak et al., 2003). Additionally, plant communities play direct and indirect roles in determining soil quality in orchards (Demestihas et al., 2017; Montanaro et al., 2017; Weil & Brady,

2017), influencing, soil carbon, water infiltration rates, soil aggregate structure, and bulk density (Gulick et al., 1994; Liu et al., 2005; Martínez-Mena et al., 2020; Soto et al.,

2021). In this study, there were several ways that farmers diversified the plant communities, including allowing resident perennial vegetation cover to persist, and deliberately planting annual cover crops and hedgerows to foster biodiversity in their operations.

Microbial community

Overall microbial biomass, bacteria biomass, Gram + biomass, and

Actinobacteria (a Gram + bacteria) biomass were all significantly higher in regenerative orchards. Furthermore, microbial biomass and Actinobacteria biomass were significantly correlated to improvements in TSC, TSN, phosphorus, bulk density, and the invertebrate community. More robust microbial communities are associated with improved nutrient cycling and higher functioning soils (Steenwerth & Belina, 2008; Vukicevich et al., 2019;

Vukicevich et al., 2016; Weil & Brady, 2017). Actinobacteria, which share behavioral traits with both fungi and bacteria, produce growth-promoting compounds and metabolites such as antibiotics which are critical in helping host plants ward off pathogens (Subramanian, 2016). The growth promoting compounds that Actinobacteria

produce help their host plants acquire nutrients under severe abiotic conditions, including droughts and times of nutrient deficiency (Subramanian, 2016). Finally, Actinobacteria assist in nutrient solubilization and mobilization, mycorrhizal symbioses, biological nitrogen fixation, all while producing geosmin- a key indicator of soil fertility

(Subramanian, 2016). AMF biomass was also substantially higher in regenerative orchards than in conventional orchards (the data was marginally significant; P = 0.052).

AMF species can assist their plant hosts with salinity tolerance (Ye et al., 2019), phosphorous uptake (Romero-Munar et al., 2019), improve osmotic adjustment (Shahvali et al., 2020), ward off insect pests (Vannette & Hunter, 2009), and improve water use and photosynthetic efficiency (Shahvali et al., 2020). These myriad benefits culminate in enhanced growth and yield of AMF-associated plants (Kumar et al., 2015). Finally, our findings align with Kallenbach et al. (2016), who shows soil microbes playing a key role in making stable and chemically diverse soil organic carbon and Xiang et al. (2017) which found larger Actinobacteria communities strongly correlated to increases in soil carbon.

Invertebrate community

Invertebrate H’ and biomass are significantly interconnected to improvements in

TSC, TSN, TSP, bulk density, and soil microbial communities (Figure 3.7). The epigeal invertebrate community directly and indirectly affects decomposition and nutrient cycling, sometimes in complex ways (Weil & Brady, 2017). In Santos et al (1984), insecticide treatments eliminated the insect and mite community, including the predatory

mites that regulated populations of bacterivorous nematodes. The result was that bacterial litter degradation was reduced by 40% in this system. Further, Cifuentes-Croqueville et al

(2020), has proposed that the invertebrate diversity should be considered a key factor in promoting soil carbon and fertility due to their critical role in driving soil fertility and environmental heterogeneity, as well as their significant stature in the soil organism community (Doube & Olaf, 1997). Additionally, Colloff et al. (2010) suggests that the collective burrowing activity soil fauna may improve soil water infiltration rates. The importance of permanent ground cover and microbial communities in maintaining/building soil carbon is clear, but the role invertebrates play is less defined.

Our results suggest that a diverse and robust invertebrate community plays a key intermediary step in the process of plant litter, etc. becoming TSC. If growers are using practices that harm the invertebrate and microbial communities in their orchards, they are likely capping their orchards’ ability to build carbon and increase fertility.

Pest management

There was no difference in the pest damage to almonds among regenerative and conventional orchards. The mechanisms in which these two managements regimes attained low pest densities are fundamentally different. Conventional orchards largely eliminate their ground cover with herbicides, and respond to pest outbreaks using multiple insecticide applications per year. Regenerative replaced synthetic pesticides with more robust ground cover on their orchard floors, and this in turn supports more diverse and abundant invertebrate populations. This agrees with this study’s LMM’s, which

found significant correlations between increased invertebrate diversity and reduced pest damage. These results support previous research that shows pest populations are inversely related to that invertebrate diversity in agricultural systems (Chaplin-Kramer et al., 2011; Demestihas et al., 2017; Eilers & Klein, 2009; LaCanne & Lundgren, 2018;

Lundgren & Fausti, 2015; Lundgren et al., 2006).

Yield and almond nutrient composition

Regenerative management did not reduce almond yields, and improved at least one nutrient in the almonds relative to conventional systems. This similarity in yields between ecologically intensive food systems and conventional systems has been seen elsewhere (Ponisio et al., 2015; Soto et al., 2021). To accomplish this, regenerative orchards replaced synthetic agrichemical inputs with more robust microbial communities, diverse ground covers, and invertebrate diversity that increased soil nutrient levels, made more water available to the trees, and reduced pest pressure. A common claim is that regenerative orchards improve the nutrient density of the farm product; this may be true in some systems, but we found that magnesium was the only nutrient that was significantly improved in this study. Examining these data under different cropping conditions, and within the context of how agrichemicals might affect the bioavailability of these nutrients for almond consumers, warrants additional attention. To our knowledge, this is the first published examination of relative nutritional contents of regenerative farm products.

Despite having similar yields, regenerative orchards were twice as profitable as conventional orchards. The greater revenue of the regenerative orchards was due to the premium paid for almonds grown in regenerative systems. All of the regenerative producers in this study were certified organic, allowing them to receive the organic premium in the wholesale market. One other paper found similar profitability of regenerative farms relative to conventional (LaCanne & Lundgren, 2018), but in that case there were substantial treatment effects on production costs in the regenerative and conventional corn fields. Our work indicates there may be greater opportunity to cut costs in regenerative orchards than in conventional orchards as regenerative systems become more widespread. Although there was no difference in operating costs on average between the two systems, these costs were consistent in the conventional orchards, whereas they varied substantially in regenerative orchards. The variability in cost that we see in regenerative orchards ranged from $1,064 to $9,327/ha, and appeared to be driven by the higher costs incurred by the smaller operators. These smaller scale regenerative producers paid significantly more labor and amended their harvesting practices. They recoup these costs by direct marketing their products and by appealing to niche markets. When we look at the costs from the large-scale, wholesale regenerative producers, their costs vary less ($1,577-$2,614/ha), and are approximately 80% the conventional orchard costs ($2,357-$2843/ha). Regenerative agricultural systems are a fairly recent evolution of our food system, and a consistent formula of practices have yet to be defined by farmers to characterize regenerative systems; this variability in systems may contribute to the variability in the expenditures observed in regenerative orchards.

The end result was that regenerative almonds are more profitable than conventional systems, and because yields were equivalent, and the potential for reducing input costs, the profitability of regenerative almonds will remain equivalent or superior to conventional almonds, even if regenerative almond producers do not receive a premium for their product.

Table 3.1. The regenerative matrix score is the sum of regenerative practices on each farm, as determined through

grower surveys. Regenerative practices are scored as 1, and conventional as 0. Farms that scored higher than 5 are considered

regenerative, and below 5 are considered conventional.

Orchard Latitude, Year Almond Hedgero Graz NSynthe Organic No No No No Orchard Floor Total Score Sites Longitud Studie Variety ws er tic Amend Synthetic Synthetic till Synthetic 75-Covered Regenerative e d Fertilize ments & Herbicide or Metal Insecticide During at rs Compos s Fungicide s Least 2 of 3 t Teas s Field Visits Chico R1 N 39.688, 2018 Sonora 1 0 1 1 1 0 1 1 1 7

W121.876 Chico C1 39.690 N, 2018 Price 0 0 0 1 0 0 1 0 1 3 121.885 W

Tables Capay Valley 38.826 N, 2018 Mission 0 1 1 0 1 1 1 1 1 7

R2 122.206 & Padre W Capay Valley 38. 707 N, 2018 Nonparei 0 0 0 0 0 0 1 0 0 1 C2 122.108 l W

Capay Valley 38.686 N, 2018 Nonparei 1 1 1 1 1 0 0 1 1 7 Chapter 3 Chapter R3 122.059 l W Capay Valley 38.700 N, 2018 Butte & 0 0 0 0 0 0 1 0 0 1 C3 122.040 Padre W East Turlock 37.5186 2018 Butte & 0 0 1 1 1 1 1 1 1 7 R4 N, Padre 120.572 W

East Turlock 37.516 N, 2018 Nonparei 0 0 0 1 0 0 1 0 0 2 C4 120.610 l W Hilmar R5 37.382 N, 2019 Nonparei 1 0 1 1 1 1 0 1 1 7 120.897 l W Hilmar C5 37.387 N, 2019 Nonparei 0 0 0 1 0 0 1 0 0 2 120.886 l W Atwater R6 37.394 N, 2019 Nonparei 0 0 1 1 1 0 1 1 1 6 120.597 l W

Atwater C6 37.392 N, 2019 Butte & 0 0 0 0 0 0 1 0 0 1 120.598 Padre W East Turlock 37.505 N, 2019 Nonparei 1 0 1 1 1 1 1 1 1 8 R7 120.594 l W East Turlock 37.512 N, 2019 Nonparei 0 0 0 1 0 0 1 0 0 2 C7 120.612 l W Chico R8 39.689 N, 2019 Nonparei 1 0 1 1 1 0 1 1 1 7 121.872 l W Chico C8 39.688 N, 2019 Nonparei 0 0 0 0 0 0 1 0 0 1 121.818 l W

Table 3.2. Soil nutrients in regenerative and conventional orchards (N = 16).

Significant treatment differences are presented in bold and shaded (α = 0.05). All data represents mean ± SEM parts per million (ppm), unless otherwise noted. Test statistics are all t-values, and P-values represent Welch’s P or Mood’s Median Test.

Test Conventional Regenerative statistic Soil Nutrient Orchards Orchards (df) P-value 1:1 Soil pH 7.3 ± 0.07 7.5 ± 0.10 -2.11 (12) 0.06 1:1 Soluble Salt 0.16 ± 0.02 0.23 ± 0.03 -1.77 (12) 0.10 Organic Matter (%) 2.39 ± 0.47% 3.88 ± 0.39% -2.45 (13) 0.03

Soil Respiration (CO2-C) 20.38 ± 4.26 30.50 ± 5.91 -1.39 (12) 0.19 Water Extracted Total N 18.58 ± 2.95 23.83 ± 2.94 -1.26 (14) 0.23 Water extractable organic nitrogen (WEON) 9.79 ± 1.2 15.32 ± 1.9 -2.46 (12) 0.03 Water extractable organic carbon (WEOC) 122.63 ± 10.34 178.88 ± 14.10 -3.09 (12) 0.01 Nitrate 6.69 ± 2.23 6.05 ± 1.57 0.23 (12) 0.82 Ammonium 3 .00 ± 1.41 3.93 ± 0.78 -0.57 (10) 0.58 Inorganic Nitrogen 9.71 ± 2.49 9.99 ± 1.61 -0.09 (11) 0.93 0.01 2 Total Phosphorus 55.0 (Median) 157.5 (Median) x 1 = 6.25 (Mood) 0.01 2 Inorganic Phosphorus 47.5 (Median) 140.5 (Median) x 1 = 6.25 (Mood) 0.13 2 Organic Phosphorus 7.65 (Median) 18.80 (Median) x 1 = 2.25 (Mood) Potassium 119.50 ± 13.25 169.25 ± 29.86 -1.52 (9) 0.16 Calcium 615.75 ± 40.93 897.38 ± 73.28 -2.96 (13) 0.01 Aluminum 175.66 ± 19.48 112.53 ± 12.03 2.76 (11) 0.02 Iron 96.83 ± 13.44 74.76 ± 9.01 1.36 (12) 0.20 Sulfur 8.08 ± 2.18 18.28 ± 3.69 -2.38 (11) 0.04 Zinc 4.54 ± 1.71 5.25 ± 1.31 -0.33 (13) 0.75 Manganese 18.34 ± 5.54 12.90 ± 2.30 0.91 (9) 0.39 Copper 3.89 ± 1.90 1.30 ± 0.19 1.35 (7) 0.22 Magnesium 195.88 ± 37.85 252.00 ± 37.11 -1.06 (13) 0.31 Sodium 30.63 ± 5.42 47.63 ± 11.89 -1.30 (9) 0.22 Microbially Active Carbon (%) 16.31 ± 2.92 16.48 ± 2.14 -0.04 (12) 0.97 Organic C: N 13.42 ± 1.19 12.21 ± 0.80 0.84 (12) 0.41 Organic N: Inorganic N 1.39 ± 0.27 1.90 ± 0.40 -1.07 (12) 0.31

Organic N Release 6.09 ± 1.17 10.26 ± 2.24 -1.65 (10) 0.13 Organic N Reserve 3.69 ± 0.77 5.05 ± 1.42 -0.84 (10) 0.42 Organic P Release 4.04 ± 1.18 12.71 ± 3.69 -2.24 (8) 0.054 Organic P Reserve 4.09 ± 1.05 13.58 ± 4.57 -2.03 (7) 0.08 Available N (NO3-N, NH4-N, organic N release) 28.43 ± 5.59 36.48 ± 5.62 -1.02 (14) 0.33 114.74 350.15 2 Available P (Median) (Median) x 1 = 6.25 0.01 Available K 143.41 ± 15.84 203.26 ± 35.75 -1.53 (9) 0.16 Nutrient Value ($USD/ha) 291.84 ± 47.86 345.50 ± 33.04 -3.06 (8) 0.02 Traditional N (NO3-N) 12.06 ± 4.02 10.91 ± 2.84 0.23 (12) 0.82 Haney Test N (Same as available N) 28.43 ± 5.59 36.48 ± 5.62 -1.02 (14) 0.33 Haney Test Score 5.47 ± 0.64 8.16 ± 0.98 -2.29 (12) 0.04

Table 3.3. The differences in carbon and nitrogen at different soil depths in regenerative and conventional almond orchards. All values presented are mean ±

SEM. Statistical significance was determined via Linear Mixed Models where treatment and clay percentage were fixed factors, while farm was the random factor (N = 49 observations across 16 farms, α = 0.05).

Total Soil Carbon (TSC; Total Soil Nitrogen (TSN; Clay % Statistics Mg/ha) Mg/ha) Coefficient Soil Regenerative Conventional Regenerative Conventional TSC TSN Depth (ESM layer1) 2 0-500 Mg 17.32 ± 1.57 7.92 ± 0.58 1.76 ± 0.19 0.85 ± 0.05 N/A N/A TSC: χ 1 = 33.7, P < 0.001 Clay %: N/A

2 TSN: χ 1 = 30.9, P < 0.001 Clay %: N/A

2 0-1,000 24.52 ± 1.51 12.14 ± 1.02 2.48 ±- 0.20 1.33 ± 0.07 N/A N/A TSC: χ 1 = 38.5, Mg P < 0.001 Clay %: N/A

2 TSN: χ 1 = 27.8, P < 0.001 Clay %: N/A

2 0-1,500 29.40 ± 1.72 16.01 ± 1.60 3.00 ± 0.22 1.76 ± 0.11 0.26 ± N/A TSC: χ 1 = 29.8, Mg 0.12 P < 0.001 2 Clay %: χ 1 = 4.7, P =0.03

2 TSN: χ 1 = 23.5, P < 0.001 Clay %: N/A

2 0-3,000 9.43 ± 2.55 25.96 ± 3.59 4.12 ± 0.27 2.98 ± 0.27 0.75 ± 0.05 ± TSC: χ 1 = 16.0, Mg 0.15 0.02 P < 0.001 2 Clay %: χ 1 = 23.1, P < 0.001

2 TSN: χ 1 = 15.0, P < 0.001 2 Clay %: χ 1 = 10.6, P < 0.001

2 0-4,500 47.44 ±3.72 29.55± 5.51 5.08 ±- 0.36 4.02 ± 0.41 1.17 ± 0.09 ± TSC: χ 1 = 8.4, Mg 0.21 0.02 P = 0.003 2 Clay %: χ 1 = 34.4, P < 0.001

2 TSN: χ 1 = P = 0.002 2 Clay %: χ 1 = 22.9, P < 0.001

2 Total (0- 54.52 ±4.76 41.37 ± 7.27 5.96 ± 0.45 5.00 ± 0.53 1.52 ± 0.12 ± TSC: χ 1 = 5.4, 6,000) 0.25 0.02 P = 0.02 2 Clay %: χ 1 = 35.5, P < 0.001

2 TSN: χ 1 = 7.2, P = 0.007 2 Clay %: χ 1 = 33.6, P < 0.001

1 To control for the relative compaction and other circumstances associated with each vertical depth, the mass (Mg) of TSC per ha was assessed using the Equivalent Soil Mass

(ESM) method, in which a cubic spline of Mg of TSC per depth layer was calculated

(Wendt & Hauser, 2013).

Table 3.4. The soil microbial community in regenerative and conventional almond orchards. Units for values presented are mean SEM ng/g, unless otherwise noted.

Significant treatment differences are denoted using shaded rows and bold text N = 16 orchards (One-way ANOVA; α = 0.05).

Microbial Community Conventional Regenerative F1, 14- Characteristic Orchards Orchards P-value value 3642.12 ± 5589.63 ± Total Microbial Biomass 388.16 808.37 0.048 4.72 Undifferentiated Microbial 1756.49 ± 2355.59 ± Biomass 195.65 307.00 0.12 2.71 Undifferentiated Microbial % 48.75 ± 3.44 43.83 ± 3.61 0.34 0.98 H’ Microbial Diversity Index 1.45± 0.04 1.41 ± 0.05 0.56 0.36 1473.3 2 ± 2624.29 ± Total Bacteria Biomass 216.07 419.00 0.03 5.96 Bacteria % 40.52 ± 3.01 46.10 ± 2.47 0.17 2.05 1065.76 ± Gram (-) Biomass 676.03 ± 91.72 210.53 0.11 2.88 16.88 20.36 0.61 2 Gram (-) % (Median) (Median) (Mood) x 1 = 0.25 1558.52 ± Gram (+) Biomass 797.29 ± 797.2 221.11 0.01 8.50 Gram (+) % 22.32 ± 2.77 27.87 ± 1.07 0.08 3.48 2 Rhizobia Biomass 0 (Median) 0 (Median) 1 (Mood) x 1 = 0 1.0 2 Rhizobia % 0 (Median) 0 (Median) (Mood) x 1 = 0 Actinobacteria Biomass 230.41 ± 53.77 425.69 ± 75.09 0.043 5.06 Actinobacteria% 6.31 ± 1.04 7.32 ± 0.65 0.42 0.68 Protozoan Biomass 26.98 ± 9.13 30.78 ± 8.42 0.76 0.09 Protozoan % 0.64 ± 0.19 0.53 ± 0.15 0.67 0.19 578.97 ± Total Fungi Biomass 385.34 ± 65.45 140.52 0.23 1.56 Fungi % 10.09 ± 0.99 9.54 ± 1.39 0.75 0.10 AMF Biomass 105.05 ± 19.92 205.49 ± 42.94 0.052 4.50 AMF % 2.80 ± 0.29 3.45 ± 0.41 0.21 0.69 Saprophytic Biomass 280.29 ± 51.03 373.49 ± 99.57 0.42 0.69 Saprophytic % 7.29 ± 0.91 6.10 ± 1.03 0.40 0.75

0.62 2 Sat: Unsat 2.05 (Median) 1.51 (Median) (Mood) x 1 = 0.25 10.50 16.75 1.0 2 Mono: Poly (Median) (Median) (Mood) x 1 = 0 Fungi: Bacteria 0.26 ± 0.03 0.20 ± 0.03 0.21 1.73 Predator: Prey 0.02 ± 0.004 0.01 ± 0.003 0.36 0.90 0.62 2 Gram (+) : Gram (-) 1.21 (Median) 1.38 (Median) (Mood) x 1 = 0.25

Table 3.5. The nutrient levels in the almonds. Units for values presented are mean ±

SEM mg/g, unless otherwise noted. Statistical significance was determined via Linear

Mixed Models where treatment was a fixed factor, while farm was the random factor (N

= 40 observations across 16 farms, α = 0.05).

Conventional Regenerative 2 Almond Nutrient Orchards Orchards P-value χ 1 Total Carbon 594. 40 ± 3.11 604.55 ± 1.64 0.24 1.40 Total Nitrogen 37.63 ± 0.75 37.44 ± 0.69 0.97 0.002 Phosphorus 4.64 ± 0.07 4.95 ± 0.06 0.06 3.46 Potassium 7.39 ± 0.13 7.78 ± 0.09 0.25 1.34 Calcium 2.94 ± 0.12 2.81 ± 0.08 0.35 0.87 Magnesium 2.86 ± 0.02 3.12 ± 0.04 < 0.001 12.59 Sulfur 1.34 ± 0.02 1.44 ± 0.02 0.052 3.78 Zinc 0.032 ± 0.001 0.031 ± 0.001 0.42 0.66 Iron 0.048 ± 0.001 0.049 ± 0.001 0.89 0.02 Manganese 0.023 ± 0.001 0.022 ± 0.001 0.93 0.007 Copper 0.012 ± 0.001 0.011 ± 0.001 0.17 1.89 Boron 0.024 ± 0.001 0.020 ± 0.001 0.09 2.91 Sodium <0.001 <0.001 0.56 0.35 Molybdenum <0.001 <0.001 0.97 0.002 Aluminum 0.010 ± 0.001 0.009 ± 0.001 0.45 0.005 Shannon H’ 1.15 ± 0.01 1.17 ± 0.01 0.65 0.20 Simpson DS 0.53 ± 0.01 0.53 ± 0.01 0.59 0.29 Pielou’s Evenness J 0.44 ± 0.004 0.45 ± 0.004 0.62 0.24

Chapter 3 Figures

Figure 3.1. Haney soil health scores in regenerative and conventional almond orchards (mean ± SEM, N = 16 orchards; Welch’s two-sample t-test; α = 0.05).

Figure 3.2. Time required for soils to infiltrate a 5 cm rain event (One-way ANOVA; mean ± SEM, N = 8 orchards, α = 0.05). Water infiltration rates were measured once per orchards during bloom and fruit development in 2019. A smaller number means that water infiltrated into the soil at a faster rate

Figure 3.3. The differences in carbon and nitrogen through the 0-6,000 Mg

Equivalent Soil Mass (ESM) layer (~0-60 cm) in regenerative and conventional almond orchards (mean ± SEM). Statistical significance was determined using Linear

Mixed Models, in which treatment and clay percentage were fixed factors, and farm was a random factor (N = 49 observations across 16 orchards, α = 0.05).

Figure 3.4. Total microbial biomass collected from the soil in regenerative and conventional almond orchards (mean ± SEM, N = 16 orchards, α = 0.05). This sampling occurred during the fruiting period.

Figure 3.5. Invertebrate biomass and diversity (Shannon H) of the epigeal invertebrate communities in regenerative and conventional orchards (mean ± SEM,

N = 16 orchards, Welch’s two-sample t-test; α = 0.05). Invertebrate communities were collected during the bloom, fruit development, and harvest periods.

Figure 3.6. Net profitability of regenerative and conventional almond production systems (Welch’s two-sample t-test; mean ± SEM, N = 13, α = 0.05). A producer survey was used to determine management practices, costs, and revenues that contributed to the direct net profitability of each operation. One conventional and two regenerative orchards were not included.

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Soil Quality/Biological Metric PC 1 P-value PC 2 P-value Bulk Density -0.74 0.002 0.21 0.69 TSC 0-500 Mg ESM Layer -0.98 < 0.001 -0.04 0.89 TSN 0-500 Mg ESM Layer -0.96 < 0.001 -0.07 0.89 Total Phosphorous -0.86 < 0.001 0.17 0.89 Microbial Biomass -0.56 0.03 -0.73 0.01 Actinobacteria Biomass -0.57 0.03 -0.79 0.003 Invertebrate H' -0.86 < 0.001 0.28 0.69 Invertebrate Biomass -0.84 < 0.001 -0.21 0.69 Management Practice Importance No Synthetic Fertilizers, Insecticides, Herbicides -0.87 < 0.001 -0.08 0.89 Permanent Orchard Ground Cover -0.86 < 0.001 -0.23 0.89 No Synthetic or Metal Fungicides -0.63 0.02 -0.16 0.89 Organic Amendments or Compost Teas -0.57 0.04 0.1 0.89 Hedgerows -0.48 0.08 -0.19 0.89 Grazers -0.16 0.54 0 1 No Tilling 0.29 0.33 0.12 0.89

Figure 3.7. Principle Component Analysis of 16 almond orchards based on 8 soil quality and biological community metrics. Blue points represent farms defined as

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regenerative and red points represent farms defined as conventional in the study.

Spearman correlation coefficients are listed for associations of principle components 1 and 2, with soil quality/biological community metrics and with management practice importance. Coefficients in bold indicate significant correlations following False

Discovery Rate Adjustment.

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Chapter 4 Conclusion Conventional agricultural systems are major contributors to declining biodiversity, global climate change, and negative human health outcomes. With agricultural lands having lost 30-75% of their organic carbon they are facing major declines in soil quality and fertility (Lal et al., 2007, Robertson et al., 2014). This loss in carbon combined with the simplification of the landscape forces farmers to rely extensively on costly synthetic inputs to meet fertility needs and handle pest outbreaks. In

California, the use of glyphosate and other pesticides has been shown to cause autism and intellectual disorders with comorbidity in children (von Ehrenstein et al., 2019), with agriculture being the primary cause of biodiversity loss in the San Joaquin Valley

(Stewart et al., 2019; Williams et al., 1998). Regenerative agriculture is an “approach to farming that uses soil conservation as the entry point to regenerate and contribute to multiple provisioning, regulating and supporting services, with the objective that this will enhance not only the environmental, but also the social and economic dimensions of sustainable food production” (Schreefel et al., 2020). The principles of regenerative agriculture are: 1) reduce or eliminate tillage, 2) never leave bare soil, 3) maximize plant diversity and productivity on a farm, and 4) integrate livestock and cropping operations and. 5) reduce or eliminate synthetic agrichemicals (Fenster et al., 2021; LaCanne &

Lundgren, 2018; Pecenka & Lundgren, 2019). These practices are interconnected. The overall system, not individual practices, determine the success of an operation. The goal of a regenerative agricultural systems is to significantly increase the ecosystems services of farms, with agriculture becoming a key part of the effort towards addressing climate change, biodiversity losses, and negative human health outcomes.

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With such a wide range of purported benefits farmers, policy makers, researchers, and consumers are intrigued by the concept of transitioning towards regenerative agricultural systems. However, up until this study there has not been an empirically validated way to define a farm as regenerative and differentiate it from its conventional counterparts. Classification systems like organic certification have been established for other movements in ecological agriculture, with core principles represented by many practices, rather than by ecological outcomes. We found that higher regenerative scores in croplands and rangelands scaled positively with improved environmental and economic services, including soil quality, biodiversity, and farm profitability. Using our matrix approach, we were able to condense these varied agricultural systems to less than

10 practices, representing the five principles of regenerative agriculture. Further, when examining the data, the farms broke into distinct clusters, allowing us to determine a threshold score for distinguishing regenerative and conventional agricultural systems (5 for cropland and 3 for rangelands).

Regenerative management in almond orchards improved soil quality and increased biodiversity while producing nutritious almonds profitably. Nearly every major metric (soil carbon and micronutrient levels, water infiltration rates, and soil health indices) were improved on regenerative orchards versus their conventional counterparts.

Soil microbiology, plant community, and invertebrates (including earthworms) were also enhanced in regenerative systems, while pest populations on the nuts were equivalent in the regenerative and conventional orchards, with increasing invertebrate biodiversity correlating to reduced pest damage. Overall almond nutrient levels and yields were

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equivalent in the two treatments. Finally, profits were nearly doubled in the regenerative systems relative to their conventional counterparts. The soil and biological properties of the orchards were interconnected and produced by a combination of management practices, rather than just by one or two practices.

Overall, the performance of regenerative orchards is a result of them developing a full regenerative system that stacks multiple regenerative practices into a single operation. Their performance is not due to any one management practice. Rather, increasing the number of regenerative practices correlates to improved performance

(Fenster et al., 2021), with permanent ground coverage, the avoidance of synthetic agrichemical inputs, and the use of organic amendments correlated with regenerative outcomes of soil health and biodiversity promotion (Figure 3.7). This analysis adds a layer of nuance to the stacking concept, suggesting that there is a core set of practices that establish a foundation for improving soil quality and biological community metrics. One would expect abstaining from tillage to be a key management practice. However, as 88% of the orchards in the study avoided tillage this appears to indicate that solely avoiding tillage will not improve critical soil quality and biological metrics. The success of regenerative orchards is the result of these agroecosystems being more robust, with additive or synergistic interconnections among components of the system (Figure 3.7).

For example, soil bulk density, TSC, TSN, TSP, microbial biomass, actinobacteria biomass, invertebrate H’, and invertebrate biomass were all correlated to one another, and regenerative management drives these interconnections (Figure 3.7 and Table A2). Our results support the notion that converting agriculture over to regenerative systems could

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be part of the solution to several imminent global problems, including climate change and carbon drawdowns, diminishing water resources, biodiversity loss, agricultural pollution, human health problems, and diminishing rural economies.

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Appendix 1

CROPLAND Regenerative Score Calculator *Please answer the following questions specifically -YES- -NO- for the study field and associated growing season* color in circle color in circle 1) Were any synthetic fertilizers applied? Value = 0 Value = 1 2) Were any herbicides (granular or sprayed) applied Value = 0 Value = 1 3) Were any fungicides (seed treatment, spray or other) applied? Value = 0 Value = 1 4) Were any insecticides (seed treatment, spray or other) applied? Value = 0 Value = 1 5) Was there any mechanical tillage of the soil besides planting activity? Value = 0 Value = 1 6) Were cover crops grown on this field within one year of the study? Value = 1 Value = 0

7) Are there any strips of perennial vegetation on field borders or Value = 1 Value = 0 throughout (unmanaged road ditches not included)?

8) Were any organic amendments applied (ex: compost, biologics, compost Value = 1 Value = 0 tea, etc.)? If other, list here: ______9) Was the field grazed by livestock? Livestock type: ______Value = 1 Value = 0

------MATRIX SCORE ------> (Sum of values adjacent to colored circles in both YES and NO columns above)

128

RANGELAND Regenerative Score Calculator *Please answer questions below by coloring in circles which correspond to the best description of study pasture and cattle herd*

1) How frequently are insecticides or anthelmintics Multiple times Once per year, not Not used and/or during during grazing (wormers) used on the herd? grazing Value = 0 Value = 1 Value = 2

2) What is the average stocking density in an area cattle 0-5 Animal Units 6-10 Animal Units >10 Animal Units are allowed to graze at any given time (measured as per Hectare per Hectare per Hectare

Animal Units per Hectare)? Value = 0 Value = 1 Value = 2

3) How long do cattle remain grazing in an area before 31 or more days Between 11 and 30 10 days or less days they are moved to new forage? Value = 0 Value = 1 Value = 2

4) What is the average rest period for a grazed area No rest periods 2-30 days 31 or more days (continuous grazing) before cattle are allowed to reenter? Value = 0 Value = 1 Value = 2

------MATRIX SCORE ------> (Sum of values below colored circles above) 1

*Researchers may need to alter the above response parameters based on specific climatic and other local conditions. The above responses were successfully used to identify regenerative ranches in the Northern Great Plains, United States

129

Appendix 2

Multivariate analysis of the biological communities and soil quality indicators.

Multivariate analyses at the farm level (N = 16) were used to further quantify the effect of regenerative management on almond orchards, while also accounting for any collinearity among the metrics. Two MANCOVA models and one MANOVA model were built. The first MANCOVA model examined the effect of management type with soil clay percentage as a covariate on Mg/TSC/ha at the 6,000 Mg ESM layer, total phosphorous, bulk density, overall microbial biomass, actinobacteria biomass, and invertebrate H’. The second MANCOVA model was structured the same, except with

Mg/TSC/ha through the 0-3000 Mg ESM layer. The MANOVA model examined the effect of management type on Mg/TSC/ha at the 0-500 Mg ESM layer, bulk density, overall microbial biomass, actinobacteria biomass, and invertebrate H. Clay was not used as a covariate since analysis indicated that soil clay percentage was not a significant factor in determining TSC in the upmost soil layers. Since the multivariate normality test

(Royston) requires more observations than dependent variables, we were limited to selecting seven outcome variables. TSN, AMF biomass, and arthropod H’ were considered but were excluded due to having a collinearity > 0.9 with other outcome variables. All the models passed the Royston test (multivariate normality) and Box's M test (homogeneity of covariance matrices). The Wilks’ Lambda multivariate statistic was used to determine significance. Univariate ANOVA analyses were used to determine the outcome variables which contributed significantly to the MANCOVA model.

130

Results

Multivariate analysis of the biological communities and soil quality indicators.

Multivariate analyses at the farm level (N = 16) further supports the notion that regenerative management positively affects important biological and soil physical- chemical indicators. The first MANCOVA model (N = 16; P = 0.047) examined the effect of management type with soil clay percentage as a covariate on Mg/TSC/ha at the

0-6,000 Mg ESM layer, total phosphorus, bulk density, overall microbial biomass, actinobacteria biomass, and invertebrate H. The second MANCOVA model (N = 16; P =

0.03) was structured the same, except with Mg/TSC/ha at the 0-3,000 Mg ESM layer.

The MANOVA model (N = 16; P = 0.02) removed clay percentage as a covariate and quantified the effect of management type on Mg/TSC/ha at the 0-500 Mg ESM layer, total phosphorus bulk density, overall microbial biomass, actinobacteria biomass, and invertebrate H’. All the models show regenerative management to be significantly correlated with improved soil quality and biological community metrics (Table A2).

Further, univariate analyses for the first two models shows all the model components to be significant contributors, except for microbial biomass (Table A2). For the third model all the model components are significant contributors except for actinobacteria biomass

(Table A2).

131

Table A2. Results from the two MANCOVA models and one MANOVA model determining the effect of farms being

regenerative on key soil quality and biological community metrics. The overall model columns show the Wilks’ Lambda

multivariate statistic associated with farms being designated regenerative and the effect of soil clay percentage. The subsequent

columns present the Univariate ANOVA test statistics used to determine the outcome variables that contributed significantly to

the MANCOVA model. The first MANCOVA model examined the effect of management type with soil clay percentage as a

covariate on Mg/TSC/ha at the 6,000 Mg ESM layer, total phosphorous, bulk density, overall microbial biomass,

actinobacteria biomass, and invertebrate H’. The second MANCOVA models was structured the same, except with Mg/TSC/ha

through the 0-3,000 Mg ESM layer. The MANOVA model examined the effect of management type on Mg/TSC/ha at the 0-

500 Mg ESM layer, bulk density, overall microbial biomass, actinobacteria biomass, and invertebrate H. Clay was not used as

a covariate since analysis indicated that soil clay percentage was not a significant factor in determining TSC in the upmost soil

layers.

Overall Model Mg/TSC/ha Bulk Density Total Phosphorus Microbial Biomass Actinobacteria Biomass Invertabrate H' Model Treatment P Clay P Treatment P Clay P Treatment P Clay P Treatment P Clay P Treatment P Clay P Treatment P Clay P Treatment P Clay P Mg/TSC/ha 0-6,000 Mg ESM layer, bulk density, total phosporus microbial biomass, actinobacteria biomass, and invertebrate H' 0.047 0.03 0.04 0.001 0.004 0.21 0.01 0.16 0.056 0.82 0.043 0.12 0.002 0.03 Mg/TSC/ha 0-3,000 Mg ESM layer, bulk density, total phosporus microbial biomass, actinobacteria biomass, and invertebrate H' 0.03 0.03 0.001 0.003 0.004 0.21 0.01 0.16 0.056 0.82 0.043 0.12 0.002 0.03 Mg/TSC/ha 0-500 Mg ESM layer, bulk density, total phosporus microbial biomass, actinobacteria biomass, and invertebrate H' 0.02 N/A <0.001 N/A 0.004 N/A 0.01 N/A 0.048 N/A 0.053 N/A 0.005 N/A

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Appendix 3

Table A3. Table of the 275 different invertebrate morphospecies (270 arthropod morphospecies) from almond orchards, totaling 12,414 individuals. Larvae and adults were considered separate morphospecies, as adults and larvae serve distinct ecological functions.

Order Family Species Conventional Regenerative Overall Araneae Agelenidae Agelenidae sp. 01 0 3 3 Araneae Amaurobiidae Amaurobiidae sp. 01 3 2 5 Araneae Corinnidae Castianeira sp. 01 0 1 1 Araneae Dictynidae Dictynidae sp. 01 0 1 1 Araneae Dictynidae Dictynidae sp. 02 0 1 1 Araneae Dysderidae Dysdera crocata 0 4 4 Araneae Philodromidae Ebo evansae 0 1 1 Araneae Linyphiidae Erigone dentosa 196 255 451 Araneae Linyphiidae Erigone sp. 01 3 34 37 Araneae Gnaphosidae Gnaphosidae sp. 01 0 2 2 Araneae Gnaphosidae Gnaphosidae sp. 02 3 3 6 Araneae Salticidae Habronattus sp. 01 0 2 2 Araneae Linyphiidae Linyphiinae sp. 01 66 159 225 Araneae Linyphiidae Ostearius melanopygium 0 4 4 Araneae Oxyopidae Oxyopes salticus 0 2 2 Araneae Oxyopidae Oxyopes sp. 01 1 1 2 Araneae Lycosidae Pardosa sp. 01 1 3 4 Araneae Lycosidae Pardosa sp. 02 0 13 13 Araneae Lycosidae Pardosa sp. 03 0 2 2 Araneae Lycosidae Pardosa sp. 04 0 10 10 Araneae Lycosidae Pardosa tuoba 0 1 1 Araneae Salticidae Pellenes sp. 01 1 0 1 Araneae Lycosidae Schizocosa mccooki 1 1 2 Araneae Tetragnathidae Tetragnatha orizaba 1 3 4 Araneae Tetragnathidae Tetragnatha sp. 01 0 3 3 Araneae Tetragnathidae Tetragnatha sp. 02 1 0 1 Araneae Theridiidae Theridion californicum 1 0 1 Araneae Theridiidae Theridion sp. 01 0 2 2 Araneae Salticidae Thiodina hespera 0 1 1 Araneae Gnaphosidae Urozelotes rusticus 0 6 6

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Araneae Gnaphosidae Urozelotes sp.01 0 1 1 Araneae Thomisidae Xysticus sp. 01 0 3 3 Araneae Thomisidae Xysticus sp. 02 2 5 7 Araneae Gnaphosidae Zelotes sp. 01 1 7 8 Araneae Gnaphosidae Zelotes sp. 02 2 5 7 Araneae Gnaphosidae Zelotes sp. 03 0 3 3 Blattodea Ectobiidae Ectobiidae sp. 01 0 11 11 Coleoptera Elateridae Aeolus sp. 01 2 3 5 Coleoptera Carabidae Agonoleptus 0 9 9 Coleoptera Staphylinidae Aleochara sp. 01 0 1 1 Coleoptera Carabidae Amara (Amara) affinis 4 40 44 Coleoptera Carabidae Amara (Amara) littoralis 81 194 275 Coleoptera Staphylinidae Anotylus sp. 01 16 3 19 Coleoptera Staphylinidae Anotylus sp. 02 9 0 9 Coleoptera Anthicidae Anthicus sp. 01 2 0 2 Coleoptera Anthicidae Anthicus sp. 02 0 3 3 Coleoptera Staphylinidae Apocellus sp. 01 9 5 14 Coleoptera Staphylinidae Apocellus sp. 02 0 2 2 Coleoptera Tenebrionidae Apsena sp. 01 0 1 1 Coleoptera Scarabaeidae Ataenius lobatus 0 1 1 Coleoptera Staphylinidae Atheta sp. 01 0 30 30 Coleoptera Staphylinidae Atheta sp. 02 8 27 35 Coleoptera Staphylinidae Atheta sp. 03 6 3 9 Coleoptera Carabidae Axinopalpus biplagiatus 0 1 1 Coleoptera Carabidae Bembidion Bembidion sp. 01 29 45 74 Coleoptera Carabidae Bembidion Bembidion sp. 02 1 1 2 Coleoptera Staphylinidae Bisnius sp. 01 0 1 1 Coleoptera Tenebrionidae Blapstinus brevicollis 0 1 1 Coleoptera Tenebrionidae Blapstinus californicus 5 89 94 Coleoptera Tenebrionidae Blapstinus fuscus 0 1 1 Coleoptera Tenebrionidae Blapstinus lecontei 0 19 19 Coleoptera Tenebrionidae Blapstinus substriatus 10 11 21 Coleoptera Staphylinidae Brachyusa sp. 01 35 14 49 Coleoptera Scarabaeidae Calamosternus granarius 0 1 1 Coleoptera Carabidae Calathus ruficollis 0 2 2 Coleoptera Carabidae Carabidae larvae sp. 01 25 40 65 Coleoptera Carabidae Carabidae larvae sp. 02 0 1 1 Coleoptera Carabidae Carabidae sp. 04 1 8 9 Coleoptera Chrysomelidae Chaetocnema sp. 01 0 22 22 Coleoptera Chrysomelidae Chrysochus cobaltinus 0 1 1

134

Coleoptera Coccinellidae Coccinella septempunctata 0 1 1 Coleoptera Coccinellidae Coccinellidae larvae sp. 01 2 4 6 Coleoptera Coccinellidae Coccinellidae larvae sp. 02 0 1 1 Coleoptera Coccinellidae Coccinellidae larvae sp. 03 1 0 1 Coleoptera Coleoptera larvae sp. 01 0 1 1 Coleoptera Coleoptera pupa sp. 02 0 2 2 Coleoptera Coleoptera pupae sp. 01 1 0 1 Coleoptera Leiodidae Colon sp. 01 1 0 1 Coleoptera Tenebrionidae Coniontis sp. 01 0 1 1 Coleoptera Elateridae Conoderus California A 0 3 3 Coleoptera Elateridae Conoderus Conoderus sp. 01 0 14 14 Coleoptera Elateridae Conoderus Conoderus sp. 02 0 7 7 Coleoptera Elateridae Conoderus Conoderus sp. 03 0 3 3 Coleoptera Curculionidae Curculionidae larvae sp. 01 0 2 2 Coleoptera Curculionidae Curculionidae larvae sp. 02 0 4 4 Coleoptera Staphylinidae Drusilla sp. 01 0 4 4 Coleoptera Carabidae Elaphropus 14 5 19 Coleoptera Elateridae Elateridae larvae sp. 01 2 9 11 Coleoptera Elateridae Elateridae larvae sp. 02 1 0 1 Coleoptera Elateridae Elateridae larvae sp. 03 1 7 8 Staphylinidae Coleoptera (Scymaeninae) Euconnus (Napochus) sp. 01 0 1 1 Coleoptera Anthicidae Formicilla munda 18 11 29 Gondwanocrypticus Coleoptera Tenebrionidae platensis 0 29 29 Coleoptera Carabidae Harpalus affinis sp. 03 1 1 2 Coleoptera Carabidae Harpalus affinis sp. 01 5 28 33 Coleoptera Carabidae Harpalus affinis sp. 02 1 5 6 Coleoptera Carabidae Harpalus pensylvanicus 2 8 10 Coleoptera Coccinellidae Hippodamia convergens 2 2 4 Coleoptera Histeridae Hister sp. 01 0 1 1 Coleoptera Staphylinidae Hoplandria sp. 01 10 3 13 Coleoptera Curculionidae Hypera sp. 01 0 3 3 Coleoptera Curculionidae Hypera sp. 02 1 2 3 Coleoptera Staphylinidae Lathrobium debile 2 4 6 Coleoptera Elateridae Limonius sp. 01 0 3 3 Coleoptera Curculionidae Listroderes costirostris 0 2 2 Coleoptera Staphylinidae Lypoglossa sp. 01 1 3 4 Coleoptera Scarabaeidae Maladera sp. 01 0 1 1 Coleoptera Latridiidae Melanophthalma sp. 01 6 21 27 Coleoptera Carabidae Microlestes curtipennis 0 4 4 Coleoptera Coccinellidae Microweisea sp. 01 0 1 1

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Coleoptera Staphylinidae Neobisnius occidentoides 0 1 1 Coleoptera Carabidae Notiophilus sp. 01 1 0 1 Coleoptera Staphylinidae Oligota sp. 01 0 2 2 Coleoptera Anthicidae Omonadus formicarius 0 1 1 Coleoptera Staphylinidae Philonthus philonthus sp. 01 0 2 2 Coleoptera Staphylinidae Philonthus philonthus sp. 02 0 3 3 Coleoptera Carabidae Pterostichus commutabilis 6 8 14 Coleoptera Scarabaeidae Scarabaeidae larvae sp. 01 0 4 4 Coleoptera Scarabaeidae Scarabaeidae larvae sp. 02 1 3 4 Coleoptera Silphidae Silphidae larvae sp. 01 0 3 3 Coleoptera Curculionidae Sitona sp. 01 0 2 2 Coleoptera Staphylinidae Staphylinidae larvae sp. 01 15 9 24 Coleoptera Staphylinidae Staphylinidae larvae sp. 02 0 6 6 Coleoptera Nitidulidae Stelidota geminata 0 2 2 Coleoptera Carabidae Stenolophus sp. 01 5 11 16 Coleoptera Carabidae Stenophorus maculatus 1 0 1 Coleoptera Staphylinidae Strigota sp. 01 2 12 14 Coleoptera Staphylinidae Tachyporus sp. 01 1 5 6 Coleoptera Staphylinidae Tasgius ater 0 1 1 Coleoptera Tenebrionidae Tenebrionidae larvae sp. 01 3 11 14 Coleoptera Tenebrionidae Tenebrionidae sp. 01 1 18 19 Coleoptera Staphylinidae Xantholinus sp. 01 3 11 14 Coleoptera Staphylinidae Xantholinus sp. 02 2 3 5 Coleoptera Staphylinidae Xantholinus sp. 03 4 11 15 Coleoptera Staphylinidae Xantholinus sp. 04 0 2 2 Coleoptera Staphylinidae Xantholinus sp. 05 0 2 2 Dermaptera Forficulidae Forficula auricularia 8 96 104 Dermaptera Forficulidae Forficulidae sp. 01 3 50 53 Diptera Phoridae Auxanommatidia sp. 01 1 0 1 Diptera Sciaridae Bradysia sp. 01 0 2 2 Diptera Cecidomyiidae Cecidomyiidae larvae sp. 01 0 1 1 Diptera Chironomidae Chironomidae larvae sp. 01 16 11 27 Diptera Chironomidae Chironomidae sp. 01 1 3 4 Diptera Chironomidae Chironomidae sp. 02 0 4 4 Diptera Phoridae Diocophora sp. 01 1 0 1 Diptera Phoridae Diocophora sp. 02 1 1 2 Diptera Dolichopodidae Dolichopodidae larvae sp. 01 2 1 3 Diptera Sphaeroceridae Leptocera erythrocera 1 0 1 Diptera Cecidomyiidae Lestremia sp. 01 0 10 10 Diptera Sphaeroceridae Nearcticorpus sp. 01 0 1 1

136

Diptera Sphaeroceridae Opacifrons sp. 01 2 48 50 Diptera Cecidomyiidae Pararete elongata 1 0 1 Diptera Ephydridae Philygria sp. 01 2 2 4 Diptera Ephydridae Philygria sp. 02 1 0 1 Diptera Hybotidae Platypalpus sp. 01 0 1 1 Diptera Sphaeroceridae Pullimosina heteroneura 0 5 5 Diptera Sphaeroceridae Rudolfina sp. 01 0 5 5 Diptera Drosophilidae Scaptomyza sp. 01 3 5 8 Diptera Scathophagidae Scathophaga furcata 1 2 3 Diptera Sciaridae Sciaridae sp. 01 11 32 43 Diptera Sciaridae Sciaridae sp. 02 1 0 1 Diptera Muscidae Shoenomyza sp. 01 2 0 2 Diptera Syrphidae Syrphidae larvae sp. 01 3 1 4 Diptera Therevidae Therevidae larvae sp. 01 2 7 9 Diptera Tipulidae Tipula (Trichotipula) sp. 01 0 7 7 Diptera Tipulidae Tipula sp. 02 0 1 1 Diptera Tipulidae Tipulidae larvae sp. 01 43 70 113 Diptera Tipulidae Tipulidae pupae sp. 01 14 18 32 Entomobryomorpha Entomobryidae Entomobryidae sp. 01 1379 1239 2618 Entomobryomorpha Isotomidae Isotomidae sp. 01 174 993 1167 Geophilomorpha Geophilmorpha sp. 01 0 1 1 Hemiptera Aphididae Acyrthosiphon pisum 3 5 8 Hemiptera Cicadellidae Agallia sp. 01 1 2 3 Hemiptera Aphididae Aphididae sp. 07 0 3 3 Hemiptera Aphididae Aphididae sp. 06 1 3 4 Hemiptera Pentatomidae Asopinae sp. 01 0 1 1 Hemiptera Blissidae Blissus sp. 01 0 13 13 Hemiptera Aphididae Bradycaudus helichrysi 0 2 2 Hemiptera Cicadellidae Cicadellidae sp. 01 1 31 32 Hemiptera Cydnidae Cydninae sp. 01 0 1 1 Hemiptera Cydnidae Dallasiellus sp. 01 0 4 4 Hemiptera Delphacidae Delphacidae sp. 01 0 3 3 Hemiptera Miridae Deraeocorinae sp. 01 3 1 4 Hemiptera Rhyparochromidae Emblethis vicarius sp. 02 5 0 5 Hemiptera Rhyparochromidae Emblethis vicarius sp. 01 4 3 7 Hemiptera Cicadellidae Empoascini sp. 01 2 0 2 Hemiptera Aphididae Eriosomatinae sp. 01 0 1 1 Hemiptera Geocoridae Geocoris atricolor 4 47 51 Hemiptera Heterogastridae Heterogaster sp. 01 0 1 1 Hemiptera Heterogastridae Heterogaster sp. 02 2 0 2

137

Hemiptera Miridae Heterotoma planicornis 1 0 1 Hemiptera Miridae Lygus sp. 01 0 2 2 Hemiptera Rhyparochromidae Megalonotus sabulicola 0 14 14 Hemiptera Miridae Miridae sp .01 0 2 2 Hemiptera Miridae Miridae sp. 02 9 0 9 Hemiptera Miridae Mirinae sp. 01 1 0 1 Hemiptera Aphididae Myzus persicae 37 47 84 Hemiptera Nabidae Nabis alternatus 0 1 1 Hemiptera Berytidae Neoneides muticus 0 1 1 Hemiptera Lygaeidae Nysius sp. 01 0 1 1 Hemiptera Cicadellidae Pagaronia sp. 01 1 2 3 Hemiptera Leptopodidae Patapius spinosus 8 0 8 Hemiptera Pentatomidae Pentatomidae sp. 01 0 2 2 Hemiptera Aphididae Rhodobium porosum 2 4 6 Hemiptera Aphididae Rhopalosiphum padi 0 13 13 Hemiptera Aphididae Sitobion avenae 1 5 6 Hemiptera Miridae Spanagonicus albofasciatus 0 1 1 Hemiptera Enicocephalidae Systelloderes sp. 01 0 1 1 Hemiptera Rhyparochromidae Trapezonotus lattini 0 1 1 Hemiptera Anthocoridae Xylocoris californicus 0 1 1 Hymenoptera Ceraphronidae Aphanogmus sp. 01 0 1 1 Hymenoptera Braconidae Aphidius sp. 01 7 14 21 Hymenoptera Apidae Apis mellifera 1 2 3 Hymenoptera Formicidae Brachymyrmex patagonicus 0 20 20 Hymenoptera Megaspilidae Dendrocerus sp. 01 1 2 3 Hymenoptera Bethylidae Epyris sp. 01 6 3 9 Hymenoptera Formicidae Formica sp. 01 20 20 40 Hymenoptera Formicidae Formica sp. 02 0 3 3 Hymenoptera Formicidae Formicidae (alate) sp.01 2 0 2 Hymenoptera Formicidae Formicidae sp. 01 1 29 30 Hymenoptera Ichneumonidae Gelis sp. 01 0 1 1 Hymenoptera Dryinidae Gonatopus sp. 01 0 1 1 Hymenoptera Formicidae Hypoponera opacior 1 4 5 Hymenoptera Formicidae Hypoponera opacior (queen) 2 3 5 Hymenoptera Formicidae Linepithema humile 25 80 105 Hymenoptera Diapriidae Monelata sp. 01 6 1 7 Hymenoptera Formicidae Monomorium ergatogyna 0 17 17 Hymenoptera Platygastridae Telenominae sp. 02 1 0 1 Hymenoptera Formicidae Pogonomyrmex californicus 3 0 3 Hymenoptera Formicidae Prenolepis imparis 18 6 24

138

Hymenoptera Braconidae Pseudopraon sp. 01 1 0 1 Hymenoptera Formicidae Solenopsis invicta 567 36 603 Hymenoptera Formicidae Solenopsis molesta 1 17 18 Hymenoptera Formicidae Strumigenys membranifera 0 2 2 Hymenoptera Formicidae Tapinoma sessile 12 13 25 Hymenoptera Platygastridae Teleominae sp. 01 0 1 1 Hymenoptera Formicidae Tetramorium caespitum 96 401 497 Hymenoptera Formicidae Tetramorium sp. 01 42 37 79 Julida Julidae Julidae sp. 01 4 48 52 Lepidoptera Lepidoptera larvae sp. 01 0 1 1 Lepidoptera Lepidoptera larvae sp. 02 1 0 1 Lepidoptera Lepidoptera larvae sp. 03 0 1 1 Lepidoptera Lepidoptera larvae sp. 04 0 2 2 Lepidoptera Lepidoptera larvae sp. 05 0 1 1 Lepidoptera Lepidoptera larvae sp. 06 0 2 2 Lepidoptera Lepidoptera larvae sp. 07 0 3 3 Lepidoptera Lepidoptera larvae sp. 08 0 1 1 Lepidoptera Lepidoptera larvae sp. 09 0 1 1 Lepidoptera Lepidoptera larvae sp. 10 1 3 4 Lepidoptera Lepidoptera larvae sp. 11 0 1 1 Lepidoptera Lepidoptera larvae sp. 12 0 1 1 Lepidoptera Lepidoptera larvae sp. 13 0 1 1 Lepidoptera Lepidoptera larvae sp. 14 0 2 2 Lithobiomorpha Henicopidae Lamyctes sp. 02 8 40 48 Lithobiomorpha Henicopidae Lamyctes sp. 01 29 54 83 Neuroptera Chloropidae Chloropidae sp. 01 0 3 3 Oniscidea Armadillidiidae Armadillidiidae sp. 01 34 952 986 Opiliones Phalangiidae Phalangium sp. 01 2 5 7

Opisthopora Enchytraeidae Enchytraeidae sp. 01 342 372 714 Opisthopora Lumbricidae Lumbricidae sp. 01 236 1186 1422 Orthoptera Gryllidae Gryllidae sp. 01 1 0 1 Psocoptera Lachesillidae Lachesilla sp. 01 1 1 2 Eremobates (palpisetulosis Solifugae Eremobatidae group) 1 0 1 Spirobolida Spirobolidae Tylobolus sp. 01 0 6 6 Stylommatophora Helicidae Cepaea hortensis 0 1 1 Stylommatophora Agriolimacidae Deroceras reticulatum 0 3 3 Stylommatophora Agriolimacidae Deroceras sp. 01 3 11 14 Symphypleona Sminthuridae Sminthuridae sp. 01 39 59 98 Thysanoptera Thysanoptera sp. 01 7 3 10

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Acari sp. 01 142 541 683 Insecta larvae sp. 01 0 1 1 Insecta pupae sp. 03 0 1 1 Insecta pupae sp. 04 0 6 6 Insecta pupae sp. 01 0 6 6 Insecta pupae sp. 02 0 10 10

FINAL_Thesis_Fenster Final Audit Report 2021-05-10

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