Category: hydroponic agriculture

Future research projects could also benefit from a larger sample size to analyze farm-level differences and whole genome sequencing of positive isolates to ascertain any genetic relatedness between livestock species or between farms, as well as presence of antimicrobial resistance genes. In Chapter 2, the risk map determined regions at highest risk for contact between feral pigs and outdoor-raised pigs, and these areas will be important to target surveillance and outreach, in the case of future disease transmission between these two swine groups. This project can be expanded nationwide to create awareness of high-risk contact and potential disease transmission areas, to protect both public health and agriculture in the US. For example, this risk map could be used to plan surveillance programs to prevent transmission of imported diseases to the US, such as African Swine Fever that was recently detected in the Dominican Republic, or prevent the spread of reemerging diseases like pseudorabies, which was detected in feral pig populations in Mendocino County, CA in 2015. A 2015 report by the European Food Safety Authority concluded that surveillance programs will be key in preventing the introduction and spread of ASF in North America and other naïve countries. Additionally, if feral or outdoor-based domestic swine disease location data were readily accessible in the US, additional risk maps could predict the spread of specific pathogens. Disease risk maps are useful to support decision making for agencies focused on wildlife management and conservation as well as animal and public health.

Currently, USDA collects data through surveys for swine operations only in the top 16 producing pork states in the US, and California is not included, raspberry grow in pots which reduces available data for disease risk models. However, the swine NAHMS is due to be conducted again in 2021 with a larger focus on small-scale swine operators. Another avenue for future research entails using covariate rasters from specific years or decades to predict the distribution of a feral pigs. Few SDM or MaxEnt comparison projects have compared temporality of covariate rasters for model prediction accuracy. Shifts in weather patterns, as well as the dynamics of large fires, will most likely be exacerbated by climate change and will affect wildlife movements and locations in the future, which may indicate the importance of choosing temporally-specific variables for model building. Additionally, if climate change and wildfires accelerate in California, current static rasters may become inaccurate in predicting future suitable habitat. California’s annual precipitation levels fluctuate between drought and excessive precipitation associated with El Niño and La Niña events. Climatologists predict volatility of rainfall patterns and temperature for California, which may affect suitable habitat for wild mammals such as feral pigs, and emphasizes the need for dynamic climate rasters. Both the MaxEnt model and risk map in Chapter 2 are limited because they are static maps that used fixed layers as their foundation; consequently, they do not incorporate dynamic events. Future species distribution models could incorporate temporal environmental patterns into models, due to dynamic changes over time, especially in regions like California where climate change and large wildfires can affect the distribution of certain species.

For instance, Snow et al used temporally dynamic prediction models and examined three decades from 1982 to 2012 to report evidence of feral pig expansion due to climate change. Also, feral pigs in Canada build “pigloos” to be able to survive the harsh Canadian winters, expanding their habitat northward. A 2015 study on climate change affecting wild boars in Europe also reported that milder winters allow for expanded abundance of these mammals.. Additionally, feral pigs may migrate seasonally due to food and water availability in California, therefore future projects could incorporate migration patterns and develop risk maps for specific time periods as Lee et al conducted with waterfowl species, although these data are not available yet for most predictors in California. Building real time dynamic risk maps that incorporate remote sensing data, such as satellite information, could be the next step in predicting high-risk disease transmission areas, as built previously for avian influenza by the California Waterfowl tracker. However, tracking birds may be easier than collaring feral pigs. Chapter 3 combined aspects of Chapters 1 and 2, by determining the prevalence of STEC in feral pigs that reside near domestic swine raised outdoors and predicted possible areas of contact between these two swine populations. Both multilevel logistic models in Chapters 1 and 3 identified outdoor-raised livestock with access to wild areas, such as wetlands or forests, as a significant risk factor for the presence of STEC in samples. One possible pathway for shared pathogens in wild areas may be wildlife contaminating food or water, which are then consumed by livestock. Additionally, Chapter 3 results could be improved upon by using WGS to analyze relatedness between feral pig and domestic pig samples. Although the pathway for pathogen spillover can be bi-directional and temporality may be unclear, identifying clusters of shared indicator pathogens is an important next step in analyzing disease risks from feral pigs in California and nationwide. Chapters 2 and 3 analyzed feral pig populations and their risk to farms that raised domestic swine outdoors.

In Chapter 3, 45.45% of survey respondents observed feral pig presence on their farm, with 36.36% stating that feral pigs had direct contact with their domestic pigs in pastures, pens or barns. These results match the risk map from Chapter 2, which overlapped predicted suitable habitat for feral pigs and OPO locations and showed that 49.18% of the 305 OPO identified in California overlapped with suitable feral pig habitat, indicating that spillover of an emerging or transboundary disease is possible, given the correct drivers. We know human or livestock encroaching into tropical forests are drivers for zoonotic diseases such as COVID-19 or Nipah Virus, usually with an intermediate host such as bats. Although emerging zoonotic diseases in many cases originate at the interface of wildlife-livestock-humans, the US is not considered a hot spot for zoonotic disease outbreaks according to Daszak and the EcoHealth Alliance , yet zoonotic pathogen outbreaks can still occur. Possible drivers of disease spillover in the US between feral and domestic pigs raised outdoor include density of animals, shared natural areas between domestic and wildlife and increasing contact between these two growing swine populations. As the number of DSSF farms continues to grow, continued evaluation of risk factors and agricultural management practices that are unique to these small operations will identify additional risk mitigation strategies and develop extension outreach materials to keep food safe from farm to fork and protect California’s agricultural economy. Additionally, as the two parallel trends nationwide of expanding feral pig populations and outdoor-based domestic swine continues, disease surveillance of feral pigs located near outdoor-raised domestic swine is key in preventing transmission of emerging or reemerging pathogens in the future.Encouraged by consumer preference for local foods and willingness to pay more than double the price for local products , both large and small-scale farming is increasingly turning to direct markets through you-pick operations, farm stands, farmers’ markets, 30 planter pot and Community Supported Agriculture . Currently, nearly 7% of U.S. farms are involved with direct marketing with an 8% increase in sales since 2007 . The federal government began tracking the number of farmers markets in 1994 and CSAs in 2007. The number of farmers’ markets has more than doubled in the past decade, rising to 8284 in 2014 from 3706 in 2004 . Local food is also increasingly promoted through food hubs and sales to restaurants and grocery stores . Numerous practitioners of planning, land-use management, policy and economic development encourage local food programming . ‘Buy Local’ campaigns have been codified in every state with branding and are buoyed through formal and informal economic development support in comprehensive planning documents. With its growing popularity, the local food movement is expected to change both consumers and farmers. The movement often emphasizes ‘weak social ties’ created through food as bringing together novel constituents for political persuasion which combines purchasing power with the ‘soft power’ of a social movement. Where markets should emphasize the highest financial returns, economic sociologists have noted their non-economic logic , terming them ‘embedded’ in both geographies and social value systems . Hinrichs states that part of what direct marketing producers sell is “social connection. Local embeddedness itself then becomes some of the value added in the farmers’ market experience” .

Embeddedness describes the noneconomic logic of how markets yoke together two separate geographies through shared economies and social values . This research asks: what is the extent and orientation of embeddedness in the local food system? First, a literature review demonstrates the current understanding in the field and the need for new methodologies to help test theories of embeddedness within local food systems. Namely, the local food movement is expected to transmit values through proximate economic and social networks. But which communities are connected, and across which local marketing strategies? In response to this question, I pilot a method for mapping the local food system socially and spatially. Document review and program director interviews help to verify and explain the findings as well as their consequences for food systems planning and economic development.Local food activists have reconceptualized food supply chains as a means of spatially distributing social values by leveraging economic capital. The values encompassed by the food system are exemplified by the over 300 different labelling schemes which promote fair labor, sustainable land-use, and animal welfare practices to name a few . Yet, only a few global corporations control distribution, connecting consumers to producers . This bottleneck in supply chains reveals an important lever for altering geographies and financing shared value systems. Renting et al. asserts that shortening the supply chain by decreasing the number of intermediaries involved in production, distribution, processing and purchasing should clarify the values and geographies involved. In sum, geographically explicit, personal relationships between producers and consumers are expected to raise awareness about social, economic, and environmental effects of food consumption by tightening feedback loops which concentrate economic and social capital toward values-based goals . Hinrichs cautions that even the shortest supply chains, such as direct marketing from farms to consumers, can have varied power structures. Namely, farmers often travel to cities for farmers’ markets, while consumers travel to farms in which they own a share of the commodities produced in the CSA model. Hinrichs asserts that while both supply chain typologies emphasize direct, local consumer relationships with farmers, the resulting geo-social embeddedness of the network and the values it promotes will fundamentally differ. In addition, local values-based supply chains are not limited to direct-marketing. Nearly 50,000 farms in 2012 sold some or all of their products directly to retail outlets such as restaurants, grocery stores, schools, hospitals, or other businesses that in turn sold to consumers . Intermediaries between farms and consumers can also play important roles in food system-based social change. For example, chefs, like Alice Waters of Chez Panisse in California, are often seen as the forefront of the local food movement where they change consumer demand for certain types of local food. In the process, their search for ingredients resulted in direct contracts with farmers to grow specific products using agroecological methods . Similarly, farm-to-school programming is conceptualized as a means of encouraging healthy eating, transferring farming education to the next generation, and preserving local farming land-uses . Sonnino finds that school food reform in the UK gave small producers access to new income streams while offering students food that is more nutritious. Similar rationales underpin the motivations behind promoting regional food hubs . Planning practitioners have also noted that public procurement anti-hunger efforts that champion local food have had a successful track record of protecting farmland, spurring rural economic development and increasing urban food security in Canada and Belo Horizonte, Brazil . Most importantly, the geo-social embeddedness of food systems may not be driven solely by food purchases. In addition to supplying food, farms serve numerous socio-ecologic functions for urban users and nearby communities . In 2012, over 33,000 farms listed income from agritourism and recreational services such as farm tours, hayrides, school visits, and other activities . A review of the mission and vision statements from 130 nationally accredited farmland preservation agencies notes that ecosystem, social and cultural services are among the top reasons for preserving farmland, ranking far above food supply .

All these findings highlight the need for further investigation to identify high-risk areas for disease spread in the event of a future disease outbreak in California or nationwide. My overall thesis goal entailed evaluating the risk of STEC on DSSF and the risk of disease transmission in the spatial overlap of feral pigs and domestic swine raised outdoors in California. In Chapter 1, we collected fecal samples from cattle, goats, sheep and pigs raised on DSSF to estimate the prevalence of STEC on these unique operations. I used a multilevel logistic regression model to assess the association between risk factors and STEC presence in fecal samples. In Chapter 2, I used a species distribution modeling method Maximum Entropy to predict suitable habitat for feral pigs in California. Then I overlapped this MaxEnt model with the location of over 300 OPO in California to create a risk map that identified areas most at risk for disease transmission between these two growing swine populations. The increasing potential for contact between domestic swine raised outdoors and feral pigs provides an opportunity for the widespread transmission of diseases throughout California, as each pig could serve as a link in the transfer of pathogens between wildlife, livestock and humans. Additionally, the transmission of diseases to domestic pigs raised outside could negatively impact the sustainability of California’s agriculture economy. In Chapter 3, I designed a study to evaluate the prevalence of STEC in six counties at highest risk for contact between feral pigs and OPO, large plastic garden pots based on the risk map built in Chapter 2. I collected fecal samples from both feral pigs and domestic pigs raised outdoors and used a multilevel logistic regression model to assess risk factors associated with the presence of STEC in samples.

The results of these last two chapters fill critical information gaps regarding the epidemiology of STEC harbored in outdoor-raised pigs on DSSF located near suitable feral pig habitat. The increasing number of diversified small-scale farms that raise outdoor-based livestock in the United States , reflects growing consumer demand for sustainably-produced local foods, including animal products such as meat and eggs. California is the top producer of agricultural products in the US and also leads the country in organic food sales, which includes products from DSSF However, there is a lack of science-based information characterizing the risk factors associated with the prevalence of food borne pathogens, such as Shiga toxin-producing Escherichia coli , in livestock raised on DSSF. Diversified farms are most often small-scale and raise a combination of livestock and numerous types of produce or multiple livestock species, with the intent of selling specialized animal products directly to consumers. Some diversified farms integrate livestock and crop production by using their animals to graze crop residues or cover crops before planting a field to fresh produce. Grazing improves soil fertility and provides farm owners with another source of revenue through fiber or meat products. Many consumers perceive produce grown on small-scale farms and/or meat raised on pasture as more “natural” or safer than food grown on large-scale conventional farms or meat animals raised in confinement systems. However, livestock are asymptomatic reservoirs for food borne pathogens and without adequate mitigation strategies, these pathogenic microorganisms may enter into the food supply. Livestock are intermittent shedders of enteric pathogens and shedding may increase under certain conditions, such as during periods of stress , due to certain management practices or seasonally.

Foodborne pathogens survive in the soil for extended periods of time and can spread to humans directly through contact with livestock or indirectly via contaminated food or water.For instance, cattle grazing uphill from a produce field was likely the causative factor for the 2019 E. coli O157:H7 romaine lettuce outbreak. STEC remains one of the top enteric pathogens associated with food borne outbreaks in the US. The top seven STEC O-serogroups that cause the most severe illness in humans have been traced to consumption of produce consumed raw, such as spinach, tomatoes and melons. Fresh produce consumed raw , which has been contaminated by livestock or wildlife feces containing STEC, can become a vehicle for these pathogens to enter the food supply. The aim of this study is to a) describe the unique characteristics of DSSF, b) estimate the prevalence of STEC in livestock raised on DSSF and c) evaluate the association between risk factors and the presence of STEC in livestock raised on DSSF located in California. Farms were visited twice between May 2015 and June 2016, once during each of the following periods: summer/autumn or winter/spring, which reflect California growing seasons and the seasonality of STEC shedding. Livestock species sampled in this study included dairy and beef cattle, dairy and meat goats, pigs and sheep. Sample sizes were calculated using Epitools based on the total number of animals on each farm, with an assumed STEC prevalence of 5% and 10% precision error, stratified by each livestock species. Individual fresh fecal samples were collected from the ground. Samplers wore gloves and placed approximately 50- 100 grams of feces into each sterile whirl-pak bag . Bags were immediately placed into plastic coolers containing ice packs, transported to the laboratory at the end of the sampling day and most samples processed within 24 hrs.

STEC was isolated from fecal samples as described previously with modifications. In brief, 10 grams of fecal material was placed in 90 ml Tryptic Soy Broth and homogenized before and after. Samples were then incubated for 2 hrs at 25°C with 100 rpm agitation, followed by 8 hrs at 42°C with 100 rpm agitation, and held overnight at 6°C, using a Multitron programmable shaking incubator . For detecting E. coli O157:H7, immunomagnetic separation using Dynal anti-E. coli O157 beads was performed on TSB enrichment broths with the automated Dynal Bead Retriever per the manufacturer’s instructions. After incubation and washing, 50 µL of the resuspended beads were plated onto Rainbow agar O157 with novobiocin and tellurite . Fifty µL of the resuspended beads were also plated onto MacConkey II Agar using sorbitol supplemented with potassium tellurite and Cefixime ; plates were streaked for isolation and incubated for 24 hrs at 37°C. Suspect E. coli O157:H7 isolates were confirmed using traditional PCR for the rfbE gene. To detect non-O157 STEC, 1 mL of pre-enrichment broth was incubated in mEHEC selective media for 12 hrs at 42°C followed by plating and incubating on Chromagar STEC . Up to 8 presumptive STEC positive colonies were confirmed for the presence of stx1 and/or stx2 genes by real-time PCR. Confirmed STEC isolates were then characterized for virulence genes using conventional PCR. After PCR testing, one colony from each positive sample was submitted to the Pennsylvania State University E. coli Reference Center to confirm O-serogroups.A 41-question questionnaire, raspberry plant pot consisting of mostly closed-ended questions, was administered to farm owners at the end of the study period. The questionnaire included sections regarding farm demographics, animal health, farm management practices, and water sources . Variables analyzed for model building included risk factors from the farmer questionnaire, sample day factors and variables that were created using known information about each farm, for instance, whether a farm raised multiple types of livestock or if they integrated livestock within produce fields before planting. Variables from the questionnaire included whether farmers allowed different livestock species to share the same barn and if livestock had contact with wild areas . Weather data from the nearest California Irrigation Management Information System weather station within a similar microclimate, provided environmental factors for possible model inclusion . Also, USDA plant hardiness zones, which are based on the average annual minimum winter temperature, were included as a proxy for the many microclimates in California . Only three zones were necessary to categorize our participating farms: zone 7b , 9a and 9b .Descriptive statistics, were calculated for all data. STEC was estimated for the overall study and per livestock species . Generalized linear mixed models were used to assess the association between STEC presence in fecal samples and risk factors. The binary outcome of interest was whether each fecal sample was STEC positive or negative. Univariate analysis assessed the distribution of variables. During bivariate analysis, variables with low variability, small cell sizes , or large standard errors were either modified, collapsed if appropriate, or discarded from model building. Correlation of numeric variables was measured with Spearman’s rank correlation coefficient; those variables that were correlated 0.80 or more were highlighted during the model-building phase to evaluate for multicollinearity issues. To identify possible confounders, each variable was evaluated using a directed acyclic graph and then added to the model to determine whether the variable affected the odds ratios of the other variables by more than 10%. The glmer function was used from the lme4 package in R to build models, with farm added as a random effect to account for possible farm-level clustering effects when analyzing individual samples.

Variance inflation factors identified possible multicollinearity and variables in the model that had a VIF over 5 were assessed for removal. Top models were compared, and a final model chosen based on the lowest Akaike Information Criterion , smallest deviance, relative to the other models. Intraclass correlation . Diagnostic plots from the DHARMa package in R were used to assess the final model and included fitted vs binned residuals, a Q-Q plot and the Kolmogorov-Smirnov test statistic. Odds ratios and 95% confidence intervals were calculated for variables in the final model. All data analysis was performed using R Statistical Software version 1.4.1036 ©.Of the 16 participating farms, two were not included in model building, as their questionnaires were not completed, leaving a total of 502 fecal samples for model building. The mean, median and range of selected numeric variables assessed for inclusion during model building are shown in Table 1.4. Farms in this study ranged from two to 500 acres and had been farming two to 30 years. Stocking rate was calculated by dividing the total number of livestock, excluding poultry, by the total number of farm acres . Selected categorial variables, stratified by whether they were STEC positive or negative are presented in Table 1.5.P -values were reported for chi-square test or Fisher’s Exact test if cell sizes were less than five. For instance, 28.99% of positive samples came from farms that mixed livestock species within a barn, whilst only 15.70% of negative samples came from farms with shared barns . Moreover, 72.46% of positive samples were from farms that allow livestock to have contact with bordering wild areas .This is one of the first studies to describe the unique characteristics of diversified small scale farms in California, while ascertaining significant associations between risk factors and the prevalence of STEC. This study detected STEC on more than half of the enrolled farms and in all the livestock species sampled. Moreover, O-serogroups isolated in this study included ones that cause serve illness in humans, including O157:H7, O26, O103 and O111. Significant risk factors associated with the presence of STEC included the daily maximum temperature, whether multiple livestock species shared a barn, the livestock source of the collected fecal sample, and whether livestock had contact with wild areas.Overall STEC prevalence measured for the 16 farms in this study was 13.62%. Six of the 16 farms had 0% STEC prevalence; however, due to the intermittent shedding of STEC which may be affected by many factors, this result does not necessarily indicate that they are free from STEC. Although STEC prevalence in livestock raised on large farms has been measured frequently in past studies, evaluation of STEC prevalence and associated risk factors estimated on DSSF is less common. However, a study conducted by USDA-APHIS collected fecal samples from dairy cows in 21 states and stratified E. coli O157:H7 prevalence between large dairies and small dairies and reported that small ranches had 29.4% E. coli O157:H7 and large dairies had 53.9% prevalence. Although this USDA-APHIS study indicated that small farms have less E. coli O157:H7 than large farms, the 29.4% prevalence they detected on small dairies is still larger than the 18.18% we identified in dairy cattle. Risk factors for the transmission of food borne pathogens on large farms may be different, especially if they only raise one crop or livestock type, instead of a diversity of species. One of our studies published in 2018 measured a 4.17% STEC prevalence in sheep raised on a mixed crop-livestock organic farm in California, which was lower than the 13.4% prevalence in sheep identified in this current study.

Reliable markers could aid in the decision of when to harvest the grapes. “Optimum” maturity is a judgement call and will ultimately depend on the winemaker’s or grower’s specific goals or preferences. A combination of empirical factors can be utilized including °Brix, total acidity, berry tasting in the mouth for aroma and tannins, seed color, etc. °Brix or total soluble solids by itself may not be the best marker for berry ripening as it appears to be uncoupled from berry maturity by temperature. Phenylpropanoid metabolism, including anthocyanin metabolism, is also highly sensitive to both abiotic and biotic stresses and may not be a good indicator of full maturity. Thus, color may not be a good indicator either. Specific developmental signals from the seed or embryo, such as those involved with auxin and ABA signaling, may provide more reliable markers for berry ripening in diverse environments, but will not be useful in seedless grapes. Aromatic compounds may also be reliable markers but they will need to be generic, developmental markers that are not influenced by the environment. This study revealed many genes that are not reliable markers because they were expressed differently in different environments. One candidate marker that is noteworthy is ATG18G . Its transcript abundance increased and was relatively linear with increasing °Brix and these trends were offset at the two locations relative to their level of putative fruit maturity . ATG18G is required for the autophagy process and maybe important during the fruit ripening phase.

It was found to be a hub gene in a gene subnetwork associated with fruit ripening and chloroplast degradation. Further testing will be required to know if it is essential for fruit ripening and whether its transcript abundanceis influenced by abiotic and biotic stresses in grape berry skins.The ultimate function of a fruit is to produce fully mature seeds in order to reproduce another generation of plants. The ripe berry exhibits multiple traits that signal to other organisms when the fruit is ready for consumption and seed dispersal. In this study, we show that there were large differences in transcript abundance in grape skins in two different locations with different environments, plastic seedling pots confirming our original hypothesis. We also identified a set of DEGs with common profiles in the two locations. The observations made in this study provide lists of such genes and generated a large number of hypotheses to be tested in the future. WGCNA was particularly powerful and enhanced our analyses. The transcript abundance during the late stages of berry ripening was very dynamic and may respond to many of the environmental and developmental factors identified in this study. Functional analysis of the genes and GO enrichment analysis were very useful tools to elucidate these factors. Some of the factors identified were temperature, moisture, light and biotic stress. The results of this study indicated that berries still have a “sense of place” during the late stages of berry ripening. Future studies are required to follow up on these observations. It appears that fruit ripening is very malleable. Manipulation of the canopy may offer a powerful lever to adjust gene expression and berry composition, since these parameters are strongly affected by light and temperature.

The ability of a genotype to produce different phenotypes as a function of environmental cues is known as phenotypic plasticity . Phenotypic plasticity is considered one of the main processes by which plants, as sessile organisms, can face and adapt to the spatio-temporal variation of environmental factors . Grapevine berries are characterized by high phenotypic plasticity and a genotype can present variability within berries, among berriesin a cluster, and among vines . Berry phenotypic traits, such as the content of sugars, acids, phenolic, anthocyanins, and flavor compounds, are the result of cultivar and environmental influences , and often strong G × E interactions . Although grapevine plasticity in response to environmental conditions and viticulture practices may provide advantages related to the adaptation of a cultivar to specific growing conditions, it may also cause irregular ripening and large inter-seasonal fluctuations , which are undesirable characteristics for wine making . Due to its complex nature, the study of phenotypic plasticity is challenging and the mechanisms by which the genes affecting plastic responses operate are poorly characterized . In fact it is often difficult to assess the performance of different phenotypes in different environments . It has been suggested that genetic and epigenetic regulation of gene expression might be at the basis of phenotypic plasticity through the activation of alternative gene pathways or multiple genes . Epigenetics has been proposed as crucial in shaping plant phenotypic plasticity, putatively explaining the rapid and reversible alterations in gene expression in response to environmental changes. This fine-tuning of gene expression can be achieved through DNA methylation, histone modifications and chromatin remodeling . Small non-coding RNAs are ubiquitous and adjustable repressors of gene expression across a broad group of eukaryotic species and are directly involved in controlling, in a sequence specific manner, multiple epigenetic phenomena such as RNA-directed DNA methylation and chromatin remodeling and might play a role in genotype by environment interactions.

In plants, small ncRNAs are typically 20–24 nt long RNA molecules and participate in a wide series of biological processes controlling gene expression via transcriptional and post-transcriptional regulation . Moreover, small RNAs have been recently shown to play an important role in plants environmental plasticity . Fruit maturation, the process that starts with fruit-set and ends with fruit ripening , has been largely investigated in fleshy fruits such as tomato and grapevine. These studies highlighted, among others, the vast transcriptomic reprogramming underlying the berry ripening process , the extensive plasticity of berry maturation in the context of a changing environment , and the epigenetic regulatory network which contributes to adjust gene expression to internal and external stimuli . In particular, small RNAs, and especially microRNAs , are involved, among others, in those biological processes governing fruit ripening . In this work, we assessed the role of small ncRNAs in the plasticity of grapevine berries development, by employing next-generation sequencing. We focused on two cultivars of Vitis vinifera, Cabernet Sauvignon, and Sangiovese, collecting berries at four different developmental stages in three Italian vineyards, diversely located. First, we described the general landscape of small RNAs originated from hotspots present along the genome, examining their accumulation according to cultivars, environments and developmental stages. Subsequently, we analyzed miRNAs, identifying known and novel miRNA candidates and their distribution profiles in the various samples. Based on the in silico prediction of their targets, we suggest the potential involvement of this class of small RNAs in GxE interactions. The results obtained provide insights into the complex molecular machinery that connects the genotype and the environment.RNA extraction was performed as described in Kullan et al. . Briefly, total RNA was extracted from 200 mg of ground berries pericarp tissue using 1 ml of Plant RNA Isolation Reagent following manufacturer’s recommendations. The small RNA fraction was isolated from the total RNA using the mirPremier R microRNA Isolation kit and dissolved in DEPC water. All the steps suggested in the technical bulletin for small RNA isolation of plant tissues were followed except the “Filter Lysate” step, which was omitted. The quality and quantity of small RNAs were evaluated by a NanoDrop 1000 spectrometer , and their integrity assessed by an Agilent 2100 Bioanalyzer using a small RNA chip according to the manufacturer’s instructions. Small RNA libraries were prepared using the TruSeq Small RNA Sample Preparation Kit , following all manufacturers’ instructions. Forty-eight bar-coded small RNA libraries were constructed starting from 50 ng of small RNAs. The quality of each library was assessed using an Agilent DNA 1000 chip for the Agilent 2100 Bioanalyzer. Libraries were grouped in pools with six libraries each . The pools of libraries were sequenced on an Illumina Hiseq 2000 at IGA Technology Services . The sequencing data were submitted to GEO–NCBI under the accession number GSE85611. A miRNA was considered as “expressed” only when the values of both biological replicates were greater than or equal to the threshold set at 10 TP4M. We defined a miRNA as “vineyard-, cultivar-, or stage-specific” when it was expressed only in a given vineyard, cultivar or one specific developmental stage. Differentially expressed miRNAs were identified using the CLCbio Genomics Workbench using multiple comparison analysis. We loaded the total raw redundant reads from our 48 libraries in the CLCbio package and trimmed the adaptors, considering only reads between 18 and 34 nt.

We annotated miRNAs against the user defined database, comprehending our set of 122 MIRNA loci and their corresponding mature sequences. For each library, container size for raspberries the total counts of read perfectly mapping to the miRNA precursors was considered as the input of the expression analysis. Given the main focus of our work, we aimed at identifying miRNAs differentially expressed between the two cultivars in the same environment and developmental stage , or between the three vineyards in the same cultivar and in the same developmental stage . For this reason, we considered each developmental stage and we performed the Empirical Analysis of digital gene expression , an implementation of the “Exact Test” present in the EdgeR Bioconductor package, as implemented in CLCbio software and estimating tagwise dispersion with pairwise comparisons and setting the significance threshold to FDR-adjusted p ≤ 0.05. The normalized reads of all miRNAs identified in this study and also the cluster abundances obtained from the static clustering analysis were submitted to another adhoc normalization [log10 or log10 ] for correlation analysis. This normalization was chosen because of the enormous range of abundance values that produced a logunimodal distribution and may cause significant biases in the correlation analysis when performed using TP4M or HNA values. A unity was then added to the abundance value due to the presence of zero entries. After this addition, a value of zero still corresponds to zero of the log10 function, thus making consistent the comparisons among profiles. The dendrogram was generated using the function hclust and the Pearson correlation was calculated using the function cor in R, based on the log10 or log10 values for miRNAs and sRNA-generating loci respectively. Pearson’s correlation coefficients were converted into distance coefficients to define the height of the dendrogram. Heat maps were produced using MeV based on TP4M values of miRNAs abundance. The Venn diagrams were produced using the function vennDiagram in R, based on the miRNA list for each cultivar, environment and developmental stage.Small RNA libraries were constructed and sequenced for 48 samples of grapevine berries . We obtained a total of 752,020,195 raw redundant reads . After adaptors trimming, 415,910,891 raw clean reads were recovered, ranging from 18 to 34 nt in length . Eliminating the reads mapping to rRNA, tRNA, snRNA, and snoRNA sequences, 199,952,950 reads represented by 20,318,708 distinct sequences, i.e., non-redundant sequences found in the 48 libraries , were perfectly mapped to the V. vinifera PN40024 reference genome . The libraries were analyzed to assess the size distributions of mapped reads. Distinct peaks at 21- and 24-nt were observed in all the libraries. Consistent with previous reports in grapevine and other plant species , the 21- nt peak was the highest, comprising a higher proportion of redundant reads, whereas the 24-nt peak was less abundant. A few exceptions regarding the highest peak in the small RNA size profile were observed: Ric_SG_ps had the highest peak at 24- nt whereas Mont_CS_ps and Mont_SG_bc did not show a clear difference between the 21- and the 24-nt peak. Using the Pearson coefficients we observed a strong association between the replicates as indicated by the high coefficients . To facilitate access and utilization of these data, we have incorporated the small RNAs into a website . This website provides a summary of the library information, including samples metadata, mapped reads, and GEO accession numbers. It also includes pages for data analysis, such as quick summary of the abundances of annotated microRNAs from grapevine or other species. Small RNA-related tools are available, for example target prediction for user-specified small RNA sequences and matching criteria. Finally, and perhaps most importantly, a customized browser allows users to examine specific loci for the position, abundance, length, and genomic context of matched small RNAs; with this information, coupled with the target prediction output, users can develop and assess hypotheses about whether there is evidence for small RNA-mediated regulation of grapevine loci of interest.

Using k-means clustering, VviERF6L1 fell within Cluster 8 with 369 transcripts, including five additional VviERF6 paralogs. The top GO categories associated with Cluster 8 were genes associated with terpenoid metabolism and pigment biosynthesis . Other interesting flavor associated categories included fatty acid and alcohol metabolism . Representative transcripts from Cluster 8 that were correlated with the transcript abundance profile of VviERF6L1 can be seen in Figure 4. These are ACC oxidase, which is involved in ethylene biosynthesis; a lipoxygenase, part of a fatty acid degradation pathway giving rise to flavor alcohols such as hexenol; α-expansin 1, a cell wall loosening enzyme involved in fruit softening, and two terpene synthases, which produce important terpenes that contribute to Cabernet Sauvignon flavor and aroma. The high similarity of these transcript profiles indicates that ethylene biosynthesis and signaling may be involved in the production of grape aroma. Supporting this argument, two recent studies have shown that a tomato ERF TF , falling in the same ERF IX subfamily, has a strong effect on ethylene signaling and fruit ripening. The transcript abundance of AtERF6 in Arabidopsis is strongly increased by ethylene, which is triggered by the MKK9/MPK3/MPK6 pathway. The transcript abundance of VviMKK9 in the Cabernet Sauvignon berries was higher in the skin than the pulp, but there were no significant differences for VviMPK3 or VviMPK6 . This is not too surprising since AtMKK9 activates AtMPK3 and AtMPK6 by phosphorylation. In addition, planting blueberries in a pot the transcript abundance of AtERF6 in Arabidopsis increases with ROS, SA, cold, pathogens, and water deficit.

Additional circumstantial evidence for ethylene signaling in the late stages of berry ripening was that the transcript abundance of many VviERF TFs was significantly affected by berry ripening and/or tissue . The transcript abundance of 129 members from the berries was determined to be above background noise levels on the microarray . The expression profiles of the 92 significantly affected AP2/ERF super-family members were separated into six distinct clusters by hierarchical clustering and indicated that this super-family had a complex response during berry ripening . The 12 members of Cluster 1 responded similarly in both the skin and pulp, gradually decreasing with increasing °Brix with a large decrease in transcript abundance at the 36.7 °Brix level. Cluster 2 with 14 members, including 8 members of the VviERF6 clade, had much higher transcript abundance in the skin with a sharp peak at 23.2 °Brix. Cluster 3 had similar profiles in both the skin and pulp with a peak abundance at 25° Brix. Cluster 4 with 7 members was a near mirror image of cluster 2, with a sharp valley for transcript abundance in the skin between 23 and 25 °Brix. Cluster 5 had 36 members with a steady increase in transcript abundance in the pulp but no substantial increase in the skin until 36.7 °Brix. Finally, in Cluster 6, there were 13 members with a higher transcript abundance in skins compared to pulp. Their transcript abundance increased with increasing °Brix level, but decreased in the skin. The transcript abundance of important components of the ethylene signaling pathway characterized in Arabidopsis and presumed to be functional in grape were also affected by °Brix level and tissue . Three different ethylene receptors, VviETR1, VviETR2, and VviEIN4 decreased with °Brix level in the skin, however there was very little or no change in the pulp. Likewise, VviCTR1, another negative regulator of ethylene signaling that interacts with the ethylene receptors, decreased between 22.6 and 23.2 °Brix in both the skin and the pulp. The transcript abundance of the positive regulator, VviEIN2, peaked at 25 °Brix in both the skin and the pulp.

AtEIN2 is negatively regulated by AtCTR1 and when it is released from repression, turns on AtEIN3 and the ethylene signaling pathway downstream. The transcript abundance of VviEIN3 increased with °Brix level, peaking at 25 °Brix in the skin, and was much higher than in the pulp. Although more subtle, its profile was very similar to VviERF6L1. Derepression of the negative regulators and the increase in positive regulators indicated that ethylene signaling was stimulated during this late stage of berry ripening.The transcript abundance of many of the genes involved in the isoprenoid biosynthesis pathway peaked between 23 and 25 °Brix level, particularly in the skin; this stimulation of transcript abundance continued in both the carotenoid and terpenoid biosynthesis pathways . DXP synthase is a key regulatory step in isoprenoid biosynthesis and its profile was similar to VviERF6L1; its transcript abundance was correlated with the transcript abundance of several terpene synthases in the terpenoid biosynthesis pathway . About 50% of the putative 69 functional terpene synthases in the Pinot Noir reference genome have been functionally characterized. Another 20 genes may be functional but need further functional validation or checking for sequencing and assembly errors. On the NimbleGen Grape Whole-Genome array there are 110 probe sets representing transcripts of functional, partial and psuedo terpene synthases in Pinot Noir . It is uncertain how many may be functional in Cabernet Sauvignon. There were 34 probe sets that significantly changed with °Brix or the °Brix and Tissue interaction effect; 20 of these are considered functional genes in Pinot Noir. Terpene synthases are separated into 4 subfamilies in the Pinot Noir reference genome; they use a variety of substrates and produce a variety of terpenes. Many of these enzymes produce more than one terpene. The top 8 transcripts that peaked in the skin at the 23.2 to 25 °Brix stages were also much higher in the skin relative to pulp .

Five of the eight probesets match four functionally-classified genes in Pinot Noir ; these terpene synthases clustered very closely with VviTPS54, a functionally annotated – Linalool/- Nerolidol synthase. VviTPS58, a -geranyl linalool synthase, was also in the cluster. The other two probesets match partial terpene synthase sequences in the Pinot Noir reference genome. The transcript abundance of genes involved with carotenoid metabolism also changed at different °Brix levels and with tissue type . CCDs are carotenoid cleavage dioxgenases and are involved in norisoprenoid biosynthesis. The transcript abundance of VviCCD1 changed significantly with °Brix level and was higher in skin than pulp, except at 36.7 °Brix. Likewise, the transcript abundance of VviCCD4a and VviCCD4b changed signficantly with °Brix level, but was higher in the pulp than the skin. The transcript abundance of VviCCD4c significantly increased with °Brix level, but there were no significant differences between tissues. VviCCD1 and VviCCD4 produce β- and α-ionone , geranylacetone , and 6-methyl-5-hepten-2-one in grapes. There were no significant effects on the transcript abundance of VviCCD7. The transcript abundance of VviCCD8 significantly increased with°Brix level and was higher in pulp than skin. Phytoene synthase, raspberries in pots which was also increased in the skin compared to the pulp , and VviCCD1, have been associated with β-ionone and β-damascenone biosynthesis. Other important grape flavors are derived from the fatty acid metabolism pathway and lead to the production of aromatic alcohols and esters. The transcript abundance of many genes associated with fatty acid biosynthesis and catabolism changed with °Brix level . In particular the transcript abundance of a number of genes were correlated with the transcript abundance of VviERF6L1 including VviACCase, Acetyl-CoA carboxylase; KAS III ; VviOAT, ; VviFAD8; ; VviLOX2 and VviHPL . The transcript abundance of alcohol dehydrogenases was affected by tissue and °Brix level . Some ADHs are associated with the production of hexenol and benzyl alcohol.Methoxypyrazines give herbaceous/bell pepper aromas. They are synthesized early in berry development and gradually diminish to very low levels at maturity. Nevertheless, humans can detect very low concentrations of these aroma compounds. Four enzymes, VviOMT1, VviOMT2, VviOMT3 and VviOMT4 , synthesize methoxypyrazines. The transcript abundance of VviOMT1 was higher in the pulp than the skin . In addition, the transcript abundance of VviOMT1 decreased significantly with °Brix level in the pulp. There were no significant differences in the trancript abundance in the skin or pulp for VviOMT2, VviOMT3 or VviOMT4 . There was a high correlation of the transcript abundance of VviOMT1 in the pulp with 2-isobutyl-3-methoxypyrazine concentrations in the berries . The transcript abundance of VviOMT2, VviOMT3, or VviOMT4 in either skin or pulp was not correlated with IBMP concentrations . This is consistent with the suggestion that the pulp is the main contributor of IBMP in the berry. Our data indicated that VviOMT1 in the pulp may contribute to the IBMP concentration in these berries.Orthologs of RIN and SPL tomato transcription factors, which are known to be very important fruit ripening trancription factors, were at much higher transcript levels in the skin and decline with °Brix level . The transcript abundance of the VviNOR ortholog in grape was higher in the pulp and increased slightly to peak at 25 °Brix. In addition, the transcript abundance of VviRAP2.3, an inhibitor of ripening in tomato , decreased in the skin with a valley at 23.2 °Brix; it belongs to Cluster 4 of the AP2/ERF super-family . Of particular interest was VviWRKY53 [UniProt: F6I6B1], which had a very similar transcript profile as VviERF6L1 .

AtWRKY53 is a TF that promotes leaf senescence and is induced by hydrogen peroxide. This is the first report we know of implicating WRKY53 in fruit ripening . AtERF4 induces AtWRKY53 and leaf senescence, so the interactions between WRKY and ERF TFs are complex. WRKY TFs bind to the WBOX elements in promoters and VviERF6L1 has a number of WBOX elements in its promoter . In addition, AtMEKK1 regulates AtWRKY53 and the transcript abundance of VviMEKK1 peaked at 23.2 °Brix in the skin as well. Interestingly, the transcript abundance of both VviERF4 and VviERF8, whose orthologs in Arabidopsis promote leaf senescence, were at their highest level of transcript abundance at the lowest °Brix levels examined in this study .This study focused on the very late stages of the mature Cabernet Sauvignon berry when fruit flavors are known to develop. Cabernet Sauvignon is an important red wine cultivar, originating from the Bordeaux region of France. It is now grown in many countries. Wines made from Cabernet Sauvignon are dark red with flavors of dark fruit and berries. They also can contain herbaceous characters such as green bell pepper flavor that are particulary prevalent in under ripe grapes. Grape flavor is complex consisting not only of many different fruit descriptors, but descriptors that are frequently made up of a complex mixture of aromatic compounds. For example, black currant flavor, in part, can be attributed to 1,8-cineole, 3-methyl-1-butanol, ethyl hexanoate, 2- methoxy-3-isopropylpyrazine, linalool, 4-terpineol, and β- damascenone and major components of raspberry flavor can be attributed to α- and β-ionone, α- and β- phellandrene, linalool, β-damascenone, geraniol, nerol and raspberry ketone. Some common volatile compounds found in the aroma profiles of these dark fruits and berries include benzaldehyde, 1-hexanol, 2-heptanol, hexyl acetate, β-ionone, β-damascenone, linalool, and α-pinene. In a study of Cabernet Sauvignon grapes and wines in Australia, Cabernet Sauvignon berry aromas were associated with trans-geraniol and 2-pentyl furan and Cabernet Sauvignon flavor was associated with 3-hexenol, 2-heptanol, heptadienol and octanal. In another comprehensive study of 350 volatiles of Cabernet Sauvignon wines from all over Australia, the factors influencing sensory attributes were found to be complex; in part, norisoprenoids and δ − and γ-lactones were associated with sweet and fruity characteristics and red berry and dried fruit aromas were correlated with ethyl and acetate esters. In Cabernet Sauvignon wines from the USA, sensory attributes were complex also and significantly affected by alcohol level of the wine. Linalool and hexyl acetate were postitively associated with berry aroma and IBMP was positively correlated with green bell pepper aroma. In France, β-damascenone was found to contribute to Cabernet Sauvignon wine aroma. Thus, flavor development in berries and wines is very complex, being affected by a large number of factors including genetics, chemistry, time and environment. In this paper we begin to examine the changes in transcript abundance that may contribute to flavor development. We show that the transcript abundance of many genes involved in fatty acid, carotenoid, isoprenoid and terpenoid metabolism was increased in the skin and peaked at the °Brix levels known to have the highest fruit flavors . Many of these are involved in the production of dark fruit flavors such as linalool synthases, carotenoid dioxygenases and lipoxygenases. These genes serve as good candidates for berry development and flavor markers during ripening.

This was widely assumed to be the case at the time of the system’s discovery. There is now substantial evidence that this system instead forms a valley coherent state stabilized by its spin order, which would require a new mechanism for generating the Berry curvature necessary to produce a Chern magnet. In general I think it is fair to say that the details of the microscopic mechanism responsible for producing the Chern magnet in this system are not yet well understood. In light of the differences between these two systems, there was no particular reason to expect the same phenomena in MoTe2/WSe2 as in tBLG/hBN. As will shortly be explained, current-switching of the magnetic order was indeed found in MoTe2/WSe2. The fact that we find current-switching of magnetic order in both the tBLG/hBN Chern magnet and the AB-MoTe2/WSe2 Chern magnet is interesting. It may suggest that the phenomenon is a simple consequence of the presence of a finite Chern number; i.e., that it is a consequence of a local torque exerted by the spin/valley Hall effect, which is itself a simple consequence of the spin Hall effect and finite Berry curvature. These ideas will be discussed in the following sections. In spin torque magnetic memories, electrically actuated spin currents are used to switch a magnetic bit. Typically, these require a multi-layer geometry including both a free ferromagnetic layer and a second layer providing spin injection. For example, spin may be injected by a nonmagnetic layer exhibiting a large spin Hall effect, a phenomenon known as spin-orbit torque. Here, we demonstrate a spin-orbit torque magnetic bit in a single two-dimensional system with intrinsic magnetism and strong Berry curvature. We study AB-stacked MoTe2/WSe2, plant pots with drainage which hosts a magnetic Chern insulator at a carrier density of one hole per moir´e superlattice site. We observe hysteretic switching of the resistivity as a function of applied current.

The real space pattern of domain reversals aligns with spin accumulation measured near the high Berry curvature Hubbard band edges. This suggests that intrinsic spin or valley Hall torques drive the observed current-driven magnetic switching in both MoTe2/WSe2 and other moir´e materials. The switching current density is significantly less than those reported in other platforms, suggesting moir´e heterostructures are a suitable platform for efficient control of magnetic order. To support a magnetic Chern insulator and thus exhibit a quantized anomalous Hall effect, a two dimensional electron system must host both spontaneously broken time-reversal symmetry and bands with finite Chern numbers. This makes Chern magnets ideal substrates upon which to engineer low-current magnetic switches, because the same Berry curvature responsible for the finite Chern number also produces spin or valley Hall effects that may be used to effect magnetic switching. Recently, moir´e heterostructures emerged as a versatile platform for realizing intrinsic Chern magnets. In these systems, two layers with mismatched lattices are combined, producing a long-wavelength moir´e pattern that reconstructs the single particle band structure within a reduced superlattice Brillouin zone. In certain cases, moir´e heterostructures host superlattice minibands with narrow bandwidth, placing them in a strongly interacting regime whereCoulomb repulsion may lead to one or more broken symmetries. In several such systems, the underlying bands have finite Chern numbers, setting the stage for the appearance of anomalous Hall effects when combined with time-reversal symmetry breaking. Notably, in twisted bilayer graphene low current magnetic switching has been observed, though consensus does not exist on the underlying mechanism. Current switching may be correlated precisely with magnetic structure.

To examine the metastable magnetic domain structure of the system under applied current, we use tuning-fork based gradient magnetometry where a magnetic signal is produced by modulating the tip position. Figure 6.5c shows the change in magnetization relative to the zero current state for ISD = 670 nA, well above the threshold current. The images in Figs. 6.5c-d are acquired over the scan range depicted by the dashed box in Fig. 6.4f. Above the threshold, a magnetic do- main a few µm2 in size is inverted relative to the ground state on one side of the device. Reversing the current flips the side hosting the reversed domain . We conclude that the current switching corresponds to the reversal of magnetic domains, with the inverted domains appearing on opposite edges for opposing directions of applied DC current. This is confirmed by the fact that the required switching current increases dramatically as a function of the applied magnetic field, which increases the energy cost of an inverted magnetic domain. The correspondence between magnetic dynamics and resistivity may be probed in detail using current modulation magnetometry, which examines the magnetic response, δBI to a small AC current. Figs. 6.5e and f show δBI , measured near the right and left edges of the device, respectively, for the same range of VBG, VT G, and VSD as Fig. 6.5a-b. The local δBI signal shows a single sharp dip feature on the right side of the device for ISD > 0 and on the left side for ISD < 0, but no signal for the opposite signs. These features correlate precisely with the current switching features observed in transport, as evidenced by overlaying a fit to the local δBI dip on the transport data in Fig. 6.5a-b. The δBI dips may be understood as a consequence of current-driven domain wall motion. As established above, applied current drives nucleation of minority magnetization domains.

Once these domains are nucleated, increasing the current magnitude is expected to enlarge them through domain wall motion. Where domain walls are weakly pinned, a small increase in the current δI drives a correspondingly small motion δx of the domain wall, producing a change in the local magnetic field δBI characterized by a sharp negative peak at the domain wall position . We may then use this mechanism to map out the microscopic evolution of domains with current. Fig. 6.5h shows a spatial map of δBI , measured at three different values of ISD corresponding to distinct features in the transport data. Evidently, the domain wall moves from its nucleation site on the device boundary towards the device bulk. Local measurements of δBI as a function of ISD show that this motion is itself characterized by threshold behavior, corresponding to the domain wall rapidly moving between stable pinning sites. A full correspondence of transport features and local domain dynamics is presented in the associated publication. The symmetry of the observed magnetic switching is suggestive of a spin or valley Hall effect driven mechanism. The bulk nature of the spin Hall torque mechanism means that similar phenomena should manifest not only in the growing class of intrinsic Chern magnets, but in all metals combining strong Berry curvature and broken time-reversal symmetry, including crystalline graphite multi-layers. Research into charge-to-spin current transduction has identified a set of specific issues restricting the efficiency of spin torque switching of magnetic order. Spin current is not necessarily conserved, and as a result a wide variety of spin current sinks exist within typical spin torque devices. Extensive evidence indicates that in many spin torque systems a significant fraction of the spin current is destroyed or reflected at the spin-orbit material/magnet boundary. In addition, the transition metals used as magnetic bits in traditional spin-orbit torque devices are electrically quite conductive, and can thus shunt current around the spin-orbit material, preventing it from generating spin current. These issues are entirely circumvented here through the use of a material that combines a spin Hall effect with magnetism, plastic plants pots and as a result of these effects this spin Hall torque device has better current-switching efficiency than any known spin torque device. We started this discussion with a favorable comparison of the impact of disorder on the ABMoTe2/WSe2 Chern magnet to graphene-based Chern magnets. I’m sure the reader was just as disappointed as we were to see the dramatic disorder landscape on display in Fig. 6.4E, which presents a map of the magnetization in the AB-MoTe2/WSe2 Chern magnet. This is not a refutation of our original claims; it remains true that the repeatability of the fabrication protocol of the AB-MoTe2/WSe2 Chern magnet is unambiguously much better than that of tBLG/hBN, or even tMBG. It is also easy to lose track of the scale of these images- the tBLG/hBN Chern magnet was only a few square microns, whereas this sample supports a Chern magnet that is almost a hundred square microns in area.

The presence of these ‘holes’ in the magnetization of this Chern magnet is not a result of strong twist angle disorder. We have so far discussed a variety of phenomena realized in gate-tunable exfoliated heterostructures. In all cases, these phenomena were accessible experimentally because of the presence of a moir´e superlattice, which gave us access to electronic bands that could be completely filled or depleted at will using an electrostatic gate. We will next be discussing an atomic crystal without a moir´e superlattice. This material does not have flat bands, and we will have no hope of completely filling or depleting any of the bands in the system. Instead, it has features in its band structure that lend themselves to interaction-driven phenomena, specifically flat-bottomed bands satisfying the Stoner criterion. The material we will be studying is an allotrope of three-layer graphene called ABC trilayer graphene. In addition to a variety of other interesting phases, this material supports both spin and orbital magnetism. We will discuss why this is the case, and we will study the ABC trilayer magnets using the nanoSQUID microscope. As in three dimensional crystals, many two dimensional crystals have multiple allotropes that are stable under different conditions. Trilayer graphene is such a material. We label multilayer graphene allotropes using letters that refer to the relative positions of atoms of different layers, projected onto the two dimensional plane. We have already encountered ABA trilayer graphene in the introduction, and this material has atoms in the third layer aligned to atoms in the first layer. At room temperature and pressure the ABA stacking order is preferred, but trilayer graphene has a metastable allotrope, ABC trilayer graphene, that can either be prepared or found naturally occurring. In ABC trilayer graphene atoms in the third layer are aligned neither with the first nor with the second layer. ABC trilayer graphene has band structure that differs significantly from ABA trilayer graphene, and these differences have important consequences for its properties. The band structure of ABC trilayer graphene at two different displacement fields is illustrated in Fig. 7.1. In the absence of a displacement field, the system is metallic at all electron densities. When a large displacement field is applied to the system, it becomes a band insulator when the Fermi level is tuned between the two resulting bands. This is the regime of displacement field that we will be discussing. ABC trilayer graphene has extremely weak spin-orbit coupling, so the spin degree of freedom is present and more or less completely orthogonal to electronic degrees of freedom, contributing only a twofold degeneracy to the band structure. Just like most other allotropes of graphene, ABC trilayer graphene has valley degeneracy, and this produces an overall fourfold energetic degeneracy of its band structure. This is illustrated in Fig. 7.2. As is abundantly clear from these plots, the bandspresent in ABC trilayer graphene are not flat; they have extremely large bandwidths. However, the bands do satisfy the flat-bottomed band condition, and as a result we can expect these systems to be able to spin- and valley-polarize without paying significant kinetic energy costs. A schematic of the device we will discuss is presented in Fig. 7.3A. This device allows us to perform several different experiments: we can tune the electron density and displacement field in the ABC trilayer graphene layer, we can measure in-plane electronic transport , and we can measure the out-of-plane capacitive conductivity as a function of electron density and displacement field. Data extracted from this contrast mechanism is presented in Fig. 7.3B. This dataset is restricted to the hole band; i.e., the bottom band in all of the plots we have so far encountered. Sharp features in this dataset correspond to spontaneous symmetry breaking; these features are marked with the numbers and .

There are situations in which this is a valuable tool, and we will look at some DC magnetometry data shortly, but in practice our nanoSQUID sensors often suffer from 1/f noise, spoiling our sensitivity for signals at low or zero frequency. One of the primary advantages of the technique is its sensitivity, and to make the best of the sensor’s sensitivity we must measure magnetic fields at finite frequencies. We have already discussed how we can use electrostatic gates to change the electron density and band structure of two dimensional crystals. We will discuss shortly a variety of gate-tunable phenomena with magnetic signatures that appear in these systems. It follows, of course, that we can modulate the magnetic fields emitted by these electronic phases and phenomena by modulating the voltages applied to the electrostatic gates we use to stabilize these phases. This is illustrated in Fig. 1.15C: an AC voltage is applied to the bottom gate relative to the two dimensional crystal, and the local magnetic field is sampled at the same frequency by the SQUID. Electrons carry a degree of freedom that we have not yet extensively discussed: spin. Electron spin is a fundamentally quantum mechanical property; it can be more or less understood using analogies to classical physics, but it also has some properties that don’t have simple classical analogues. Spin can be understood as a quantized unit of angular momentum that an electron can never be rid of. Although an electron is, as far as we know, a point particle, this unit of angular momentum couples to charge and produces a quantized electron magnetic moment, which we call the Bohr magneton, µB. Electron spins both couple to and emit local magnetic fields, drainage for plants in pots and they are orthogonal to the electronic wave function- changing an electron’s wave function will not under normal circumstances influence its spin, and vice versa.

Electrons are fermions; they obey the Pauli exclusion principle, which states that no two electrons can be placed into the same quantum state. The simplest consequence the existence of electron spin has is the fact that electronic wave functions can fit two electrons instead of one, because an electron can have either an ‘up’ spin or a ‘down’ spin. We say that electron spin produces an energetic degeneracy, because each electronic wave function can thus support two electrons. Electron spin is not the only degree of freedom that can produce energetic degeneracies; we will discuss a different one later. All of the above arguments apply for electron spin in condensed matter systems as well, and we can expect every electronic band to support both spin ‘up’ and spin ‘down’ electrons. These arguments say nothing about interactions between electrons, and all of the physical laws we normally expect to encounter still apply. In particular, electrons of opposite spin can occupy the same wave function, but a pair of electrons have like charges, so they repel each other. There is thus an energetic cost to putting two electrons with opposite spin into the same wave function, and this cost can be quite large. This consideration is outside the realm of the physical models we have so far discussed, because electronic bands in the simplest possible picture are independent of the extent to which they are filled. We are introducing an effect that will violate this assumption; the energies of electronic bands may now change in response to the extent to which they are filled. In particular, when an electronic wave function is completely filled with one spin species , it will remain possible to add additional electrons with opposite spins, but there will be an additional energetic cost to doing so. It is important to be precise about the fact that the displacement of the unfavorable spin species upward in energy occurs after the wave function is filled with its first spin.

As a result, which spin species gets displaced upward in energy is arbitrary, and is determined by the spin polarization of the first electron we loaded into our wave function. This is an example of a ‘spontaneously broken symmetry,’ because before the addition of that first electron, the two spin species were energetically degenerate, and after the band is completely filled with both electron species, they will again be energetically degenerate. All of the above arguments apply to localized electronic wave functions and do not say anything specific about condensed matter systems, which involve many separate atoms that each support their own wave functions. A similar but somewhat subtler argument applies to electronic wave functions on adjacent atoms in condensed matter systems. When electronic wave functions on two adjacent atoms overlap, the structure of the delocalized electronic band that will emerge from them when they hybridize depends strongly on their relative spin polarization. When electrons on adjacent atoms have the same spin, the Pauli exclusion principle will prevent them from overlapping, thus minimizing their Coulomb interaction energy. When electrons on adjacent atoms have opposite spins, the Pauli exclusion principle doesn’t apply, because the two electrons are already in different quantum states, and they can overlap. This produces a larger interaction energy for arrangements wherein electrons on adjacent atoms have antialigned spins . Like all qualitative rules there are exceptions wherein other energetic contributions are more important, but this argument applies to a wide variety of condensed matter systems. These systems are known as ‘ferromagnets.’ They have interaction-driven displacements of minority spin bands, are at least partially spin polarized, and have electron spins that are largely aligned with each other. Both of these energy scales, the ‘same-site interaction’ and the ‘exchange interaction’ respectively, can be quite large in real condensed matter systems. The presence of these effects can produce a variety of phenomena.

The displacement of a spin subband upward in energy can produce partially spin-polarized metals , fully spin-polarized metals which we call ‘half-metals’ , and spin-polarized insulators which we call ‘magnetic insulators’ . Examples of each of these kinds of systems are known in nature, and all of these phenomena represent manifestations of magnetism. In principle one must perform calculations to determine whether magnetism will occur in any specific system. In practice there exist good rules of thumb for making qualitative predictions. Same-site interactions and exchange interactions minimize energy by minimizing the number of minority spin species present in a crystal, and putting the electrons that would otherwise have occupied minority spin states into majority spin states. Of course, this process always requires that the system pay an additional energetic cost in kinetic energy, because those previously unoccupied majority spin states started out above the Fermi level. The competition between these energy scales determines whether magnetism will occur in any particular material. It follows that systems with a multitude of quantum states with very similar energies in their band structure will be more likely to form magnets; to put it more precisely, growing raspberries in pots we are looking for situations in which, near the Ferm level at least, E = C, where C is some constant. We can say that under these circumstances, the energies of electrons in the crystal are independent of their momenta. We can also say that we have encountered a large local maximum or even a singularity in the density of states. We sometimes call this the ‘flat-bottomed band condition,’ or just the ‘flat band condition’ , and it can be made quantitative in the form of the Stoner criterion. Magnetism is perhaps the simplest phenomenon that can be understood in this context, but it turns out that this argument applies very generally, and physicists expect to find a variety of interesting phenomena dependent on electron interactions whenever we encounter these situations. It is important to be specific about what we mean by a flat band here: we expect to encounter magnetism whenever an electronic band is locally flat- it is fine for the band to have very high bandwidth as long as it has a region with E ≈ C. These systems will tend to produce magnetic metals. When we encounter bands that are truly flat- i.e., they have both weak dispersion and small bandwidths- we are more likely to encounter magnetic insulators, as illustrated in Fig. 2.3. The fact that spins in ferromagnetic condensed matter systems are also aligned with each other does not affect this argument, and indeed there exist systems in both theory and experiment wherein electron spins align both with each other and with an applied magnetic field, smoothly and collectively following the direction of an applied magnetic field even as it varies. This is of course incompatible with ferromagnetic hysteresis, so we will need to mix in additional physics to explain this phenomenon. We have already discussed the fact the electron spins are orthogonal degrees of freedom from electronic wave functions, and do not couple to electric fields. This was something of an oversimplification. It is true in electrostatics problems, but in the relativistic limit- when electrons are moving at a non-negligible fraction of the speed of light- in their rest frames they experience static electric fields as large magnetic fields, as illustrated in Fig. 2.4.

Most electrons in condensed matter systems are not moving at relativistic velocities. However, in the outermost valence shells of very large atoms , electrons can end up in such high angular momentum states that their velocities become relativistic. We can thus expect electrons in bands formed from orbitals supported by heavy atoms to respond to local electric potential variations as if they provide a local magnetic field. This phenomenon is known as spin-orbit coupling, and it provides a mechanism through which the energy of an electron spin can couple to the electrostatic environment inside of an atomic lattice. Predicting the global minima in energy as a function of spin orientation is very challenging, but it is often true that a discrete set of minima exist, and of course they must obey the symmetries of the atomic lattice. For this reason in many magnetic materials there is a discrete set of magnetic ground states defined by axes along which the electron spin can point. It is very often the case that there exist two global minima in energy that are antiparrallel along an axis of high symmetry; when this is the case, we say that the system is an Ising ferromagnet. The axis along which the ground state spin orientation points is called the ‘easy axis.’ This is the origin of magnetic hysteresis in ferromagnets. According to the model of ferromagnetism we have so far developed, all of the spins in a ferromagnetic crystal are always aligned. When we apply a small magnetic field antialigned with the magnetization of our ferromagnet, nothing will occur at first. When the magnitude of the magnetic field is increased past BC , all of the spins will suddenly rotate into alignment with the applied magnetic field. The simplest way is through polycrystallinity; in magnets composed of many microscopic domains, the magnetocrystalline anisotropy axes vary locally within the crystal, producing local variations in BC . In large,highly magnetized magnets, magnetic fields generated by the crystal itself can couple to its own magnetic domains . The resulting dispersion in individual domains’ coercive fields makes magnetization hysteresis loops of macroscopic samples rather smooth, instead of instantaneous at a well defined coercive field BC . However, the qualitative properties of the model apply rather well to individual domains, which do in many systems flip all together and rather suddenly at a well-defined, albeit local, BC . For this reason careful study of the detailed structure of magnetization curves of macroscopic samples often reveals a multitude of sharp steps in magnetization, corresponding to instantaneous repolarization of tiny, monocrystalline domains. This phenomenon is known as Barkhausen noise. In summary, the model we have built is very simple, and it requires both very clean samples and a lot of information about microscopic crystalline properties to provide insights into the behaviors of real spin ferromagnets. That said, there will be many situations in which it will have some utility in understanding the phenomena we encounter. We are now ready to discuss a real magnetic system. Chromium iodide is a two dimensional magnetic insulator.

Since the OOP component of the magnetization vector is parallel to the momentum transfer Q, it is not responsive in PNR. This is consistent with the observed PMA in the VSM measurements . These results collectively suggest that the more pronounced strain at the film/substrate interface leads to a higher OOP magnetic anisotropy and hence a lower measured IP MSLD. The observed depth-dependent magnetization configuration is a result of the competition between the anisotropy energy and the Zeeman energy. Thus, under the IP configuration in the PNR experiments in Fig. 2c, with reduced IP external field, the Zeeman energy becomes insufficient to compete with the interfacial-strain-enhanced magnetic anisotropy term, giving rise to a restoration of a more OOPoriented magnetization vector in the bottom layer. This magnetically soft layer is also responsible for the near-zero field kink in OOP M in Fig. 2b, where only a small OOP external field is needed for magnetic switching. To completely flip the magnetically harder top layer in the OOP configuration, though, a much higher coercive field is required . This is indeed consistent with the observation of a larger IP magnetization preserved in the top layer under reduced IP external field in Fig. 2c. This scenario is further substantiated by the lower magnetization observed for t = 6 u.c. with stronger strain measured at 5 and 60 K under 1 T IP magnetic field . The salient structural and magnetic features pave the way for an in-depth investigation of the magneto-transport responses in Cr2Te3 thin films.The 2020–2025 Dietary Guidelines for Americans encourages the intake of a variety of plant-based foods including nuts and berries.

With the goal of increasing current knowledge on nuts and berries, as well as addressing research challenges and opportunities, the Nuts and Berries Conference: Pathways to Oxidant Defense, Vascular Function, 25 liter square pot and Gut Microbiome Changes was held on 5 to 6 May, 2022 at the University of California, Davis. Tree nuts and berries were selected as the focus of the conference for their unique composition, bioactivity, and multitude of associated health-promoting qualities. With over 50 different edible nut species and hundreds of berry varietals, the following were selected for the purpose of the conference and this review: walnuts, almonds, hazelnuts, cashews, pecans, pistachios, strawberries, blueberries, raspberries, and blackberries. Tree nuts and berries are significant commodities in the United States. The total value of tree nuts grown in California in 2021 was estimated at $8.961 billion. The total value of berries grown in California in 2021 was approximately $3.667 billion. With over two-thirds of US tree nuts and berries grown in California, the agricultural land-grant institution of the University of California, Davis was the appropriate location to convene this conference of leading researchers, registered dietitians, community partners, and industry representatives. Regular tree nut and berry consumption is associated with a decreased risk for the development of cardiovascular disease along with favorable effects on brain and gut health. Tree nuts provide protein and fiber and monounsaturated and polyunsaturated fatty acids, along with vitamins, minerals, and bio-active carotenoids, phytosterols, phenolics and flavonoids, and lignan and tannins, such as the condensed proanthocyanidins and hydrolysable ellagitannins. Berries are also a significant source of fiber and vitamin C, along with bioactive carotenoids, phenolics, including proanthocyanins and ellagitannins, and anthocyanins that provide berry color. Moreover, berries provide flavan-3-ols in quantities up to 37 mg/100 g serving , which would contribute to a recently proposed daily recommended intake level of 400 to 600 mg/d.

Although research results to date have been promising, mechanisms of action in general, and for vascular and gut health specifically, have yet to be fully defined. More data are needed that can be generalized to diverse population groups as well as for modeling of precision nutrition recommendations. This paper will review the progress and challenges of current nut and berry research and suggest future directions for the field.Many different study designs have been used to assess the effects of nuts and berries on cardiometabolic health. The strengths and limitations of various clinical nutrition study designs have been addressed elsewhere. A summary of the past 5 y of studies on nuts and berries on outcome measures of cardiovascular and gut health is presented in Tables 4, 5, 6 7, 8, 9 and Tables 10, 11, 12, 13, respectively. Eligible studies consisted of clinical human trials in children, adolescents, and adults published within the last 5 y , exploring associations between the consumption of nuts and berries and associated biomarkers of interest. Two long-term intervention trials, the PREDIMED and the COcoa Supplement and Multivitamin Outcomes Study , published in 2018 and 2022, respectively, provide examples of study designs that could be useful for future planning. The PREDIMED dietary intervention trial provides the strongest evidence to date that incorporation of nuts into a healthy Mediterranean dietary pattern in individuals ages 55 to 80 y old for 4.8 y can reduce risk of cardiovascular events by 28%. The COSMOS trial demonstrated that the daily intake of monomeric and polymeric flavanols from cocoa in older adults reduces risk for cardiovascular morbidity and mortality. Although the COSMOS study utilized a flavanol supplement compared to a whole food, it is a case study to support the need for larger trials with clinical outcomes based on the use of multi-site data of surrogate outcomes from dietary interventions that use randomized, double-blind controlled trials in crossover or parallel-arm study designs for studies of nuts or berries.

A common study design for whole foods is the replacement of the test food with a nutritionally matched, isocaloric substitute. However, matching nutritional content can be a challenge because food processing, such as blending berries and roasting nuts, causes a disruption to the nutrient matrix, potentially changing the bio-availability of key nutrients. For nuts, controls often include the complete omission of the nut of interest. For berry research, a number of considerations exist that are alternative to consuming the whole food. One is the use of freeze-dried berry powders as the test product, controlled with an isocaloric powder either lower or devoid of potential bio-actives. Attempts have been made to mask the control powders, but issues such as product color, texture, scent, and mouth feel are challenging to completely match. Although this approach is similar to a classical pharmaceutical trial design, blinding study personnel and participants is challenging, thus creating both performance and detection bias. Additionally, freeze-dried berry powders can have a different food matrix compared to the whole food, which could influence outcome measures as well as limit generalizability to the whole fruit. This can aid in participant masking, but the total amount of test product provided can be limiting, and large intakes of control gelatin capsules have resulted in adverse effects. A third option can be examining 2 or more intake levels, with or without a true control group. Finally, the use of macro- and micronutrient matched gummies with similar amounts of calories, sugars, and fiber, but devoid of other bio-actives, is a novel option for use as a comparative control. In all of these approaches, the potential bio-activity of the control itself must be considered. For example, isocaloric control powders that are lowin polyphenols may still have a considerable amount of fiber in order to obtain similar mouth feel and texture, but the fiber content may have effects on lipid metabolism and the microbiome, which could influence outcome measures. Multiple cultivars of berries exist, some of which have differences in the content of bio-active ingredients, thus limiting comparison and extrapolation of results. For nuts, walnuts contain a variety of phenolic acids, catechins, gallon pot and flavonoids, most of which have been reported to possess bio-activity. Significant differences in the concentration of 16 phenolic compounds were identified when comparing black and English walnuts. More than 50 cultivars of strawberries exist in the United States. To help reduce the potential experimental variability created with the use of different cultivars, the California Strawberry Commission has produced a freeze-dried test material that utilizes a composite of genotypes to produce a powder that is characterized for its macro- and micro-nutrients and bio-active components. The US Highbush Blueberry Council also provides a powder that is a 50/50 mixture of 2 cultivars. A limitation of this approach is that the standardized mixture may contain varieties with reduced or low bioactivity. However, the advantage of this approach is that the composite represents the “market basket” available to consumers and allows comparison of results from studies conducted among different research groups and generalizability of results to a broader berry application actually used by consumers. In addition to cultivar differences, factors such as climate and seasonal differences due to heat, sunlight, and rainfall can contribute additional variability. Given the above, the characterization of bio-actives within these foods is critical. New analytical equipment and techniques have increased the precision of food composition compared to analyses performed decades ago. Current advances in the development of nutrition databases have been reviewed elsewhere.

For example, databases such as that from the USDA Food Central could be strengthened if the date of the analyses was included, along with the protocols used and the number of samples analyzed. Linking resources from repositories detailing data, such as chemical composition and bio-activity, will help both plant scientists and health professionals to make accurate and timely recommendations and guide future research.Free-living populations have differences in background diets that can influence their responses to the intake of test foods, potentially creating significant variation in baseline measurements. This variability presents a challenge when elucidating clinically relevant effects, especially if unknown a priori, where statistical significance can be masked by combining and analyzing groups together. Inter individual variability may be mitigated by increasing sample size as well as using a crossover design, but challenges in recruitment, retention, and budget constraints exist. One way to help minimize experimental variability is through a run-in period to identify participants who may be differentially metabolizing bio-active phenolics or with the goal of minimizing or removing potentially confounding metabolites from circulation prior to the intervention. However, study designs that employ highly controlled settings, strict inclusion and exclusion criteria, extended washout periods that alter background diets, and ask participants to follow an atypical consumption pattern does not reflect “normal” life and may have limited applicability to the general population. Another useful model that also has limitations is the provision of nuts or berries in amounts and duration that are greater than normally consumed. Feeding relatively high amounts of nuts or berries for a limited period of time has been employed to demonstrate proof-of-concept and provide a basis for further exploration for changes in physiology, cognitive performance, and gut microbiome profiles. Subsequent study designs must be realistic, guided by the USDA FoodCentral database for portion size. These trial designs should also use a duration that is realistically achievable by consumers, whose food purchasing behavior can be influenced by cost, access, and seasonal availability of the food. Studies using average daily portion sizes typically require intervention periods of months, which present challenges regarding participant compliance and retention and cost of the study. In a review of 231 reports on berries and health, approximately 70% of studies used interventions of less than 3 mo or contained less than 50 participants. Meeting the challenge of conducting long-term studies using amounts of foods in a typical diet, with a representative sample of participants, requires a significant commitment of resources. The health and functional levels of participants are other factors that influence study designs and outcomes. For example, studies on cognitive performance with both nuts and berries have assessed effects among those both with and without cognitive impairments. In such studies, short-term interventions may show little or no response after the addition of nuts or berries to the diet. Although the net change may not be statistically significant, this model does not address the ability of the food to prevent decline, which would require long-term testing. Further, an individual with cognitive impairments might demonstrate favorable responses compared to baseline measures following nut or berry intake but may still not reach the level of performance of a healthy individual. In both instances, neither change from baseline, nor absolute values of performance, fully captures the beneficial cognitive response.

Several crops have been studied in this evolutionary context , but there are at least two emerging issues. The first is the speed at which domestication occurs. One view, supported primarily by archaeological evidence, is that domestication is a slow process that takes millennia . Another view, based on genetic evidence and population modeling , argues that domestication occurs much more rapidly. The gap between these two views has been bridged, in part, by a recent study of African rice. The study used population genomic data to infer that a bottleneck occurred during domestication ∼3.5 kya and also that the bottleneck was preceded by a long, ∼14,000-y decline in the effective population size of the progenitor population . The authors hypothesized that the protracted Ne decline reflects a period of low-intensity management and/or cultivation before modern domestication. While an intriguing hypothesis, it is not yet clear whether other crops also have demographic histories marked by protracted Ne declines. The second emerging issue is the “cost of domestication” , which refers to an increased genetic load within cultivars. This cost originates partly from the fact that the decreased Ne during a domestication bottleneck reduces the efficacy of genome-wide selection , which may in turn increase the frequency and number of slightly deleterious variants . The characterization of deleterious variants is important because they may be fitting targets for crop improvement . Consistent with a cost of domestication, black plastic planting pots annual crops are known to contain an increase in derived, putatively deleterious variants relative to their wild progenitors . However, it is not yet clear whether these deleterious variants increase genetic load and whether this phenomenon applies to perennial crops. The distinction between annual and perennial crops is crucial because perennial domestication is expected to differ from annual domestication in at least three aspects .

The first is clonal propagation; many perennials are propagated clonally but most annuals are not. Clonal propagation maintains genetic diversity in desirous combinations but also limits opportunities for sexual recombination . The second aspect is time. As a result, the number of sexual generations is much reduced for perennials relative to annual crops, even for perennials that were domesticated relatively early in human agricultural history. The third aspect is the severity of the domestication bottleneck. A meta-analysis has documented that perennial crops retain 95% of neutral variation from their progenitors, on average, while annuals retain an average of 60% . This observation suggests that many perennial crops have not experienced severe domestication bottlenecks; as a consequence, their domestication may not come with a cost. Here we study the domestication history of the grapevine , which is the most economically important horticultural crop in the world . Grapes have been a source of food and wine since their hypothesized domestication ∼8.0 kya from their wild progenitor, V. vinifera ssp. sylvestris . The exact location of domestication remains uncertain, but most lines of evidence point to a primary domestication event in the Near East . Domestication caused morphological shifts that include larger berry and bunch sizes, higher sugar content, altered seed morphology, and a shift from dioecy to a hermaphroditic mating system . There is interest in identifying the genes that contribute to these morphological shifts. For example, several papers have attempted to identify the gene that are responsible for the shift to hermaphroditism, which were mapped to an ∼150-kb region on chromosome 2 . Historically, genetic diversity among V. vinifera varieties has been studied with simple sequence repeats . More recently, a group genotyped 950 vinifera and 59 sylvestris accessions with a chip containing 9,000 SNPs .

Their data suggest that grape domestication led to a mild reduction of genetic diversity, indicating that grape is a reasonable perennial model for studying the accumulation of deleterious variation in the absence of a pronounced bottleneck. Still more recent studies have used whole genome sequencing to assess structural variation among grape varieties . Surprisingly, however, WGS data have not been used to investigate the population genomics of grapes. Here we perform WGS on a sample of vinifera cultivars and on putatively wild sylvestris accessions to focus on three sets of questions. First, what do the data reveal about the demographic history of cultivated grapes, specifically, the timing and severity of a domestication bottleneck? Second, what genes bear the signature of selection in vinifera, and do they provide insights into the agronomic shifts associated with domestication? Finally, do domesticated grapes have more derived, putatively deleterious variants relative to sylvestris, or have the unique features of perennial domestication permitted an escape from this potential cost?There is natural spatial variability present in vineyards due to the variations in soil characteristics and topography . Soil characteristics are too complex to be thoroughly surveyed effortlessly. With traditional destructive methods, it is difficult to obtain enough comprehensive information from the soil pits at the field scale. These soil characteristics may directly affect the water availability for grapevines, which eventually determine the physiological performance of the plants . However, there is no variable management practices currently available to accommodate the natural spatial variability. Thus, the spatial variability derived from vineyard soils will inevitably be expressed in the whole plant physiology at the cost of homogeneity of vineyard productivity and quality. We previously reported the spatial variation of midday stem water potential affecting grapevine carbon assimilation and stomatal conductance of grapevine . The resultant variations in whole plant physiology were associated to flavonoid composition and concentration at the farm gate. However, there is a lack of information about the effects on the chemical composition in the final wine, which would ultimately determine wine quality as perceived by consumers.

Georeferenced proximal sensing tools can capture the spatial and temporal variability in vineyards, making it possible to supervise and manage variations at the field scale . Previous studies showed that soil bulk electrical conductivity may be used to evaluate many soil attributes, including soil moisture content, salinity, and texture . Soil electromagnetic induction sensing has been used in precision agriculture to acquire soil bulk EC at the field scale due to its non-invasive and prompt attributes . Although research had been conducted on the relationships between soil electrical properties with plant water status, they were mostly point measurements and the results were rarely interpolated to whole fields. There were only a few studies that investigated the EMI sensing and soil-plant water relationships over a vineyard . Previous research suggested that the connection between soil water content and soil bulk EC could have relied on specific soil profiles, and needed to include soil physical and chemical properties to complete this connection . Nevertheless, there is evidence that soil bulk EC may still be useful not only to identify the variability in soil, but also in the plant response affected by vineyard soils such as yield, plant physiology, and grape berry chemistry . Plant available water is a determinant factor on grapevine physiology, black plastic pots for plants together with nitrogen availability in semi-arid regions . Wine grapes are usually grown under a moderate degree of water deficits as yields were optimized at 80% of crop evapotranspiration demand with sustained deficit irrigation . Water deficits would limit leaf stomatal conductance and carbon assimilation rate that sustain grapevines’ vegetative and reproductive growth and development . When grapevines are under water deficits, carbohydrates repartitioned into the smaller berries would enhance berry soluble solids content . Sucrose and fructose, which are the major components of total soluble solids in grape berry, can act as a signaling factor to stimulate anthocyanin accumulation . The effects on grapevine physiology and berry composition also depend on the phenological stages they occur and how severe and prolonged the water deficits are . Flavonoids are the most critical compounds dictating many qualitative traits in both grape berries and wine . The variations in environmental factors could alter the concentration and biosynthesis of flavonoids and can be extrapolated spatially within the same vineyard, including water deficits , solar radiation , and air temperature . Among flavonoid compounds, anthocyanins are responsible for the color of berry skin as well as wine . Moderate water deficits during growing season can increase anthocyanin concentration in berry skin and wine . However, water deficits can impair plant temperature regulation through evaporative cooling . They may also inhibit berry growth by limiting berry size and altering berry skin weight . Thus, in some cases it may be uncertain if water deficit promotes anthocyanins biosynthesis or reduces berry growth, or contributes to anthocyanin degradation . Applying water deficit on grapevines can contribute to greater proportion in tri-hydroxylated over dihydroxylated anthocyanins due to the up-regulation of F30 5 0 H . Another major class in flavonoids, proanthocyanidins, are polymers of flavan-3-ol monomers and they contributes mainly toward astringency or bitterness in wine . Compared to anthocyanins, water deficits showed mild effects on proanthocyanidins . However, water deficits with great severity can still alter the concentration and composition of proanthocyanidins in both berries and wine .

Selective harvest is one of the targeted management strategies to minimize the spatial variation in berry chemistry in vineyards . By differentially harvesting or segregating the fruits into batches prior to vinification, the berry composition can be artificially set at a more uniform stage with minimal variations . In our previous work, we reported the use of plant water status to determine the spatial variation of grape berry flavonoids . The goal of this study was to deduce if the spatial variability of soil bulk EC and differences in soil texture can be related to plant physiology and grape and wine composition. The specific objective of the study was to determine if the spatial variability of proximally sensed vineyard soil bulk EC would affect plant water status, and if this relation would affect leaf gas exchange, components of yield, berry composition, and flavonoids in both berries and wine. The study was conducted in a commercial vineyard in 2016 and 2017 with Cabernet Sauvignon grapevines grafted on 110R located in Healdsburg, CA, United States. In this vineyard, grapevines were planted at 1.83 m × 3.35 m . The grapevines were trained to a high quadrilateral, horizontally split trellis with two bilateral cordons. They were spur pruned with two buds per spur, and seven spurs per meter of the cordon. Irrigation was applied uniformly with a drip irrigation system, starting at fruitset to the end of veraison at 50% ETc . There were two emitters per grapevine, delivering 3.8 L·h −1 of water. Weather data was obtained from the California Irrigation Management Information System station to measure precipitation, air temperature, and reference evapotranspiration .An equidistant 33 m × 33 m grid with 35 experimental units was used for on-site measurements and berry samplings. Each experimental unit consisted of five plants. The locations of each central plant in these five plant experimental units were registered as the grid nodes with a GPS , wirelessly connected to a Trimble Pro 6T DGNSS receiver .Soil bulk EC was assessed with EM38 in 2016 when the vineyard soil was at field capacity condition. Both vertical dipole mode and horizontal dipole mode were used to assess EC at two depths, including deep soil and shallow soil . The instrument was calibrated according to manufacturer instructions. The device was placed on a PVC sled and driven through the vineyard with an allterrain vehicle along the inter-rows. A distance of approximately 0.5 m from the vehicle to the device was maintained to avoid interference with the vehicle. A stratified grid was used to collect soil samples corresponding to the two depths at which we measured soil bulk EC. Soil texture was assessed according to the soil analysis method: hydrometer analysis in the North American Proficiency Testing program. Geostatistical analysis was performed in the R language by using package “gstat” 1.1-6 . The bulk EC data were filtered by Tukey’s rule to remove outliers either below the first quartile by 1.5 inter-quartile range or above the third quartile by 1.5 inter-quartile range. To further remove the outliers, the data were filtered by the speed that the vehicle was driving, which was between 3.2 km per hour to 8.0 km per hour. Variograms were assessed by “automap” package 1.0-14 , and fitted to perform kriging.

Farmers stated that soil tests often confirmed what they already knew about their soil and did not add new information. For this reason, some farmers used results from a soil test as a guide, while other farmers found results to be redundant and therefore less useful to their farm operation. Because issues with soil fertility were sometimes linked to inherent soil characteristics within a particular field, such as poor drainage or heavily sandy soil, farmers found that soil tests were not able to provide new insight to overcome these environmental limitations. “I’m not able to correct that environmental limitation [ie, poor drainage] by adding more nitrogen,” one farmer emphasized. A different farmer echoed this sentiment, saying that “I’m not going to magically get rid of issues that soil tests show… I can only slightly move the needle, no matter what I do.” Most farmers recognized that soil tests produced inconsistent results because of differences in timing and location of sampling. As one farmer noted, “You can take the same sample a couple months apart from the same field and get very different results.” Likewise, another farmer shared that, “I still struggle with the fact that I can send in two different soil tests and get two very different results. To me that seems like the science is not there.” Farmers also emphasized that each of their “fields are all so different” with “a lot of irregularity in [their] soil.” According to several farmers, soil tests did not account for variations in soil texture and soil structure, despite their observations of the influence of both edaphic characteristics on soil test results. For example, one farmer pointed out that fields that were plowed or were previously furrow irrigated created marked differences in soil test results. Similarly, plant pot with drainage another farmer shared that if a sample for soil testing was taken from an irregular patch in a field with heavier clay, differences in soil texture across samples skewed soil test results.

If a systematic sampling approach was not considered, several farmers emphasized that results of soil tests might be “misleading.” Another source of inconsistency that farmers voiced stemmed from variation in protocols used across different labs that processed soil samples. One farmer stated that in their experience, “soil tests are not really accurate, because if I use a different lab, a different person [ie, consultant] doing the soil test, it’s all different.” For example, one farmer pointed out that they do not use soluble forms of nitrogen, and instead relied on their animal rotations and cover crops to supply nutrients as part of their fertility program; this farmer emphasized that, “I think we need to get to a place with soil testing where it would be more applicable or be more accurately useful for a farm like mine. For example, with soil testing, if the standards you’re setting, and the markers you’re setting are based on farms that are putting fertilizer on the soil, I don’t think my numbers are going match up.This farmer questioned if available soil tests were calibrated to their type of farm, given that soil tests were designed for conventional agriculture . Several additional farmers interviewed also raised similar concerns. Relatedly, farmers expressed that soil tests often did not match up with their own observations of their soil and fields. One farmer plainly stated, “I’ve had soil tests that I felt were wrong; they often do not match up with what I’ve observed and gathered.” So instead, this farmer created a work around, “I usually just rent a backhoe every year and dig up one of my fields.” Another farmer also discussed this gap in soil tests, and stated the reason for this misalignment in farmer knowledge of soil and soil test results occurred because soil tests only provided “snapshots” and that observation was “just more practical in the end” because of the historical, iterative knowledge-making farmers engage in.

To this farmer, these snapshots were a “another tool” but not as powerful as direct observation; as a result, soil test results did not inform decision-making on this farm. These sentiments were often directly related to the issue of sampling discussed above. By far, the largest limitation of soil tests that nearly all farmers discussed related to the lack of analysis and interpretation of results provided by most commonly available tests. Farmers used a variety of metaphors to get at this general point. For example, one farmer likened using soil tests as a fuel gauge. This farmer stated that “the soil test tells me my tank is half empty, but it doesn’t tell me how far you’re going to be able to go… I think what’s lacking from soil tests, if someone with experience [could] help me interpret the results.” Another farmer wished they could ask “someone who has a lot of experience with doing soil tests—what do the results mean to you? Then I would incorporate my thoughts into the results… but there is not expertise and no dialogue.” This lack of dialogue was echoed by several farmers that saw the usefulness of soil tests in the collaborative interpretation of the results. Farmers emphasized that this dialogue needed to occur not with a farm consultant, but a neutral, third party expert who could “interpret relationships.” PCA indicated strong relationships among several key management variables; the results of the PCA also provided strong differentiation among farms along the first two principal components, which together accounted for 77.4% of the variability across farms . The first principal component explained 55.1% of the variation, and the second component explained 22.3% of the variation observed across all farms. Both components had eigenvalues greater than 1.0. Additional N-based fertilizer represented the management variable most associated with PC 1—followed by tillage, and inversely ICLS. While crop diversity, cover crop frequency, and crop rotation patterns also contributed to the overall variation explained by PC 1, these management variables were weaker in comparison to N-based fertilizer additions, ICLS, and tillage. On the other hand, variables with the strongest contribution to PC 2 were crop diversity, cover crop frequency, and crop rotation patterns.

Figure 1 summarizes the spatial distribution of all farms based on PCA results with PC 1 as the x-axis and PC 2 as the y-axis. As shown in Figure 3, the results of the nearest neighbor analysis order each farm from 1 to 13, and provide a basis for visualization of the gradient in management. Therefore, this gradient in management, strongly driven by the amount of external N-based fertilizer applied on-farm, served as the basis for further visual comparison of Fields A and Fields B across all farms . As shown in Figure 2a, the difference in soil ammonium concentration between fields was low among farms on the low end of the gradient. At the middle and high end of the gradient, farms showed greater soil ammonium concentrations in Field B compared to Field A—with the exception of two farms. Farm by farm, net N mineralization rates followed trends identical to soil ammonium concentrations. Soil nitrate concentrations varied widely among farms and did not produce any consistent trends ; however, a majority of farms showed greater soil nitrate concentrations in Field B compared to Field A regardless of the management gradient. Like net N mineralization rates, net N nitrification rates followed trends analogous to nitrate concentrations farm by farm. For both mineralization and nitrification rates, a majority of farms showed greater rates in Field B compared to Field A, regardless of the gradient in management. Differences between Field A and Field B for total N, total C, and POXC followed identical trends farm by farm . Among farms on the high end of the gradient, the difference in total C between fields was consistently low . Similarly, the difference between fields in soil protein values were also consistently low at the high end of the gradient . Radar plots provided further comparison of Field A and Field B across all eight indicators for soil fertility along the gradient in management developed above . As mentioned, because the level of N-based fertilizer input was a strong driver of the management gradient, pot with drainage holes radar plots were divided to reflect low, medium, and high N-based fertilizer inputs. Shown in Figure 3L is the high overlap in soil indicators, with the exception of net N mineralization and nitrification rates, between Field A and B. However, among farms with medium N-based fertilizer input , the overlap of soil indicators between fields is minimal; Field B tended to show higher concentrations of soil ammonium and soil nitrate than Field A, while Field A tends to show higher values for total N, total C, POXC, and soil protein among these farms. Among high input farms , differences between fields were less evident in terms of soil ammonium concentration, total N, total C, POXC, and soil protein, though soil nitrate concentrations and net N mineralization and nitrification rates did show noticeable differences in values between the two fields.

The results presented above are reflective of the perspectives, observations, and experiences of a sample of organic farmers in Yolo County, California, USA, and offer an enhanced understanding of soil health and fertility from this particular node of the organic movement . Here, we focus less, as prior studies have commonly done, on a comparative analysis that quantitatively compares farmers perception of soil health to results of soil laboratory analyses ; instead, we lead the discussion with farmer knowledge of soil health and fertility, and explore emergent synergies with ongoing soil health research and soil indicator results. Establishing definitions of soil health among farmers in this study was important to gauge as a starting point to discuss soil fertility, and also for selecting fields used for soil testing. Among farmers in this case study, there was general consensus on defining soil health, with strong overlap in the particular language used by farmers. Because farmers who participated in this study were geographically located within a significant node of the organic movement in California and many of the farmers interviewed participated directly or indirectly in the growth of this movement , the similarity in responses to define soil health suggests that—on the one hand, these farmers continue to draw their understanding of soil health from the culture and guiding principles of the organic movement to this day . Indeed, maintaining healthy soils was a central component of the organic movement, as stewardship of soil represented a direct connection to the land and a form of environmental protection . At the same time, the aspects of soil health that farmers touched on here were also similar to findings by other previous studies , which suggests that—on the other hand, more recent codification of the five soil health principles by the US Department of Agriculture Natural Resources Conservation Service has led to widespread integration of a national soil health lexicon, as put forth by federal policy . This soil health lexicon, in combination with farmers’ deep cultural history with organic agriculture, likely unified definitions of soil health among farmers in this study. Interestingly, while nearly all farmers interviewed touched on the first four soil health principles in some capacity, even farmers who used integrated crop livestock systems did not explicitly mention the importance of livestock integration . This finding suggests that perhaps due to sensitivity around food safety concerns, farmers may not openly emphasize livestock integration in conversation, because although this practice may be considered beneficial to their soil, in reality, they face structural and policy limitations . Despite the emphasis on understanding nutrient cycling and nitrogen availability to crops in soil health research and fertility management , we found that for most farmers interviewed in this study, tracking nutrient levels was less important than other aspects of fertility management. Moreover, for these farmers, managing for soil fertility required a holistic approach that went beyond understanding nutrient levels. Farmers also underscored that measuring indicators for soil fertility was not particularly useful to maintaining soil fertility in practice, because assessment of soil indicators lacked integration with management practices. In most farmers’ experiences, assessing soil indicators was often associated with prescriptive rather than holistic solutions. In this sense, farmers stressed that the synergy of multiple management practices over space and time guided their approach to building and assessing soil fertility on-farm, rather than using soil nutrient levels as a guide—a key finding that is also emerging in recent literature .

To address these questions, we conducted field research at 27 farm field sites in Yolo County, California, USA, and used four commonly available indicators of soil organic matter to classify farm field sites into farm types via k-means cluster analysis. Using farm typologies identified, we examined the extent to which soil texture and/or soil management practices influenced these measured soil indicators across all working organic farms, using Linear Discriminant Analysis and Variation Partitioning Analysis . We then determined the extent to which gross N cycling rates and other soil N indicators differed across these farm types. Lastly, we developed a linear mixed model to understand the key factors most useful for predicting potential gross N cycling rates along a continuous gradient, incorporating soil indicators, on-farm management practices, and soil texture data. Our study highlights the usefulness of soil indicators towards understanding plant-soil-microbe dynamics that underpin crop N availability on working organic farms. While we found measurable differences among farms based on soil organic matter, strongly influenced by soil texture and management, these differences did not translate for N cycling indicators measured here. Though N cycling is strongly linked to soil organic matter, indicators for soil organic matter are not strong predictors of N cycling rates.All farm sites were on similar parent material according to soil survey data . All fields had soil textural class that was either loam, clay loam, or silty clay loam, based on soil texture analyses. To identify potential participants for this study, we first consulted the USDA Organic Integrity database and assembled a comprehensive list of all organic farms in Yolo County . Next, with input from the University of California Cooperative Extension Small Farms Advisor for Yolo County, we narrowed the list of potential farms by applying several criteria for this study: 1) grow fruit, vegetables, large pot with drainage and other diversified crops; 2) located within Yolo County; 3) at least 10 years of experience in organic farming; 4) at least five years of farming on the same land.

In the end, 13 organic farms and 1 local research station agreed to an initial field interview in early summer 2019 and field sampling in mid-summer 2019. During the initial field visits in June 2019, two field sites were selected in collaboration with farmers on each participating farm; these sites represented fields in which farmers planned to grow summer vegetables. Therefore, only fields with all summer vegetable row crops were selected for sampling. At this time, farmers also discussed management practices applied for each field site, including information about crop history and rotations, bed prepping if applicable, tillage, organic fertilizer input, and irrigation . Because of the uniformity of long-term management at the field station , only one treatment was selected in collaboration with the Cropping Systems Manager—a tomato field in the organic corn-tomato-cover crop system. Since the farms involved in this study generally grew a wide range of vegetable crops, we designed soil sampling to have greater inference space than a single crop, even at the expense of adding variability. Sampling was therefore designed to capture indicators of nitrogen cycling rates and nitrogen pools in the bulk soil at a single timepoint. Fields were sampled mid-season near peak vegetative growth when crop nitrogen demand is the highest. Using the planting date and anticipated harvest date for each crop, peak vegetative growth was estimated and used to determine timing of sampling. We collected bulk soil samples that we did not expect to be strongly influenced by the particular crop present. This sampling approach provided a snapshot of on-farm nitrogen cycling. Field sampling occurred over the course of four weeks in July 2019. To sample each site, a random 10m by 20m transect area was placed on the field site across three rows of the same crop, away from field edges. Within the transect area, three composite samples each based on 5sub-samples were collected approximately 30cm from a plant at a depth of 20cm using an auger .

Sub-samples were composited on site, and mixed thoroughly by hand for 5 minutes before being placed on ice and immediately transported back to the laboratory. To determine bulk density , we hammered a steel bulk density core sampler approximately 30cm from a plant at a depth 20cm below the soil surface and recorded the dry weight of this volume to calculate BD; we sampled three replicates per site and averaged these values to calculate final BD measurements for each site. Soil samples were preserved on ice until processed within several hours of field extraction. Each sample was sieved to 4mm and then either air dried, extracted with 0.5M K2SO4, or utilized to measure net and gross N mineralization and nitrification . Air dried samples were measured for gravimetric water content and BD. Gravimetric water content was determined by drying fresh soils samples at 105oC for 48 hrs. Moist soils were immediately extracted and analyzed colorimetrically for NH4 + and NO3 – concentrations using modified methods from Miranda et al. and Forster . Additional volume of extracted samples were subsequently frozen for future laboratory analyses. To determine soil textural class, air dried samples were sieved to 2mm and subsequently prepared for analysis using the “micropipette” method . Water holding capacity was determined using the funnel method, adapted from Geisseler et al. , where a jumbo cotton ball thoroughly wetted with deionized water was placed inside the base of a funnel with 100g soil on top. Deionized water was added and allowed to imbibe into the soil until no water dripped from the funnel. The soil was allowed to drain overnight . A sub-sample of this soil was then weighed and dried for 48 hours at 105oC. The difference following draining and oven drying of a sub-sample was defined as 100% WHC. Air dried samples were sieved to 2mm, ground, and then analyzed for total soil N and total organic C using an elemental analyzer at the Ohio State Soil Fertility Lab ; additional soil data including pH and soil protein were also measured at this lab. Soil protein was determined using the autoclaved citrate extractable soil protein method outlined by Hurisso et al. . Additional air-dried samples were sieved to 2mm, ground, and then analyzed for POXC using the active carbon method described by Weil et al. , but with modifications as described by Culman et al. . In brief, 2.5g of air-dried soil was placed in a 50mL centrifuge tube with 20mL of 0.02 mol/L KMnO4 solution, shaken on a reciprocal shaker for exactly 2 minutes, and then allowed to settle for 10 minutes. A 0.5-mL aliquot of supernatant was added to a second centrifuge tube containing 49.5mL of water for a 1:100 dilution and analyzed at 550 nm. The amount of POXC was determined by the loss of permanganate due to C oxidation . To measure net N mineralization and nitrification in soil samples, fresh soil sub-samples were incubated in 50mL falcon tubes using a parafilm cover, applying methods adapted from Wade et al. . Prior to incubation, squre pot each sub-sample was weighed to 7g and adjusted to 60% water holding capacity. Each sample had three parallel sets of sub-samples for each incubation period . At the end of each incubation period, soil samples were extracted with 0.5M K2SO4, placed on the shaker for 30 minutes, centrifuged for 3 minutes at 7500 rpm, and then filtered using Whatman #42 filter paper. Standard colorimetry was used to measure NH4 + and NO3 – concentrations for each sample at each time point.

Net N mineralization and nitrification were calculated as the cumulative change in inorganic N between a given sampling date and the initial inorganic N levels . In the results, we report sampling date at t = 28 days. To measure gross N mineralization and nitrification in soil samples, we applied an isotope pool dilution approach, adapted from Braun et al. . This method is based on three underlying assumptions listed by Kirkham & Bartholomew : 1) microorganisms in soil do not discriminate between 15N and 14N; 2) rates of processes measured remain constant over the incubation period; and 3) 15N assimilated during the incubation period is not remineralized. To prepare soil samples for IPD, we adjusted soils to approximately 40% WHC prior to incubation with deionized water. Next, four sets of 40g of fresh soil per sub-sample were weighed into specimen cups and covered with parafilm. Based on initial NH4 + and NO3 – concentrations determined above, a maximum of 20% of the initial NH4 + and NO3 – concentrations was added as either 15N-NH4 + or 15N-NO3 – tracer solution at 10 atom%; the tracer solution also raised each sub-sample soil water content to 60% WHC. This approach increased the production pool as little as possible while also ensuring sufficient enrichment of the NH4 + and NO3 – pools with 15N-NH4 + and 15N-NO3, respectively, to facilitate high measurement precision . Due to significant variability of initial NH4 + and NO3 – pool sizes in each soil sample, differing amounts of tracer solution were added to each sample set evenly across the soil surface. To begin the incubation, each of the four sub-samples received the tracer solution via evenly distributed circular drops from a micropipette. The specimen cups were placed in a dark incubation chamber at 20oC. After four hours , two sub-sample incubations were stopped by extraction with 0.5M K2SO4 as above for initial NH4 + and NO3 – concentrations. Filters were pre-rinsed with 0.5 M K2SO4 and deionized water and dried in a drying oven at 60°C to avoid the variable NH4 + contamination from the filter paper. Soil extracts were frozen at -20°C until further isotopic analysis. Similarly after 24 hrs , two sub-sample incubations were stopped by extraction as previously detailed, and subsequently frozen at -20°C. At a later date, filtered extracts were defrosted, homogenized, and analyzed for isotopic composition of NH4 + and NO3 – in order to calculate gross production and consumption rates for N mineralization and nitrification. We prepared extracts for isotope ratio mass spectrometry using a microdiffusion approach based on Lachouani et al. . Briefly, to determine NH4 + pools, 10mL aliquots of samples were diffused with 100mg magnesium oxide into Teflon coated acid traps for 48 hours on an orbital shaker. The traps were subsequently dried, spiked with 20μg NH4+ -N at natural abundance to achieve optimal detection, and subjected to EA-IRMS for 15N:14N analysis of NH4 + . Similarly, to determine NO3 – pools, 10mL aliquots of samples were diffused with 100mg magnesium oxide into Teflon coated acid traps for 48 hours on an orbital shaker. After 48 hours, acid traps were removed and discarded, and then each sample diffused again with 50mg Devarda’s alloy into Teflon coated acid trap for 48 hours on an orbital shaker. These traps were dried and subjected to EA-IRMS for 15N:14N analysis of NO3 + . Twelve dried samples with very low spiked with 20μg NH4+ -N at natural abundance to achieve optimal detection.In addition to the soil biogeochemical variables described above, farmers were also interviewed to determine specific soil management practices on their farms. Farmers were asked to describe the number of tillage passes they performed per field per season; the total number of crops per acre that the farm produced during one calendar year at the whole farm level; the degree to which the farm utilized integrated crop and livestock systems on the farm; crop rotational complexity for each field; and the frequency of cover crop plantings for each field. To calculatethe frequency of tillage, we tallied the total number of tillage passes per season for each field. To calculate crop abundance, the total number of crops grown per year at the whole farm level was divided by the total acreage farmed. To capture the use of ICLS, we created an index based on the number of and type of animals utilized. Specifically, the index was calculated by first adding the number of animals used in rotation on farm for each animal type and then dividing by the total number of acres for each farm. These raw values were then normalized, creating an index range from 0 to 1 . Lastly, to quantify crop rotational complexity, a rotational complexity index was calculated for each site using the formula outlined by Socolar et al. .