Tag: hydroponic

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. .

Compared to other berries, blue elderberries have similar levels of anthocyanins as raspberries, but lower levels than blueberries and blackberries 100 . The lower concentration of anthocyanins in the blue elderberry may require adjustment of levels used in supplements, food and beverages for optimal performance or health benefit, or as natural coloring agents. In addition to anthocyanins, elderberries contain other phenolic compounds, such as flavonols and phenolic acids, which also contribute to the health promoting properties of elderberry. Phenolic compounds are responsible for organoleptic properties and can help protect foods against lipid oxidation. Therefore, TPC can be useful for making approximate comparisons, for example, between varieties of the same fruit, between similar fruits or in the evaluation of a processing step . It is important to note that the TPC assay is a non-selective assay and is easily impacted by extraction conditions and interfering substances, such as ascorbic acid and reducing sugars. Although there is no evidence that the beneficial effects of polyphenol-rich foods can be attributed to the TPC of a food, it can be a useful measure for making general comparisons with other studies in the literature which reported these values but should be supported by quantitative HPLC data. Herein, the range of TPC measured in the blue elderberries was from 514 ± 41 to 791 ± 34 mg GAE per 100 g FW in 2018 and from 459 ± 50 to 695 ± 41 mg GAE per 100 g FW in 2019 . TPC in the blue elderberries was significantly higher in 2018 than in 2019 . While there were significant differences found between the farms in both years , nft vertical farming most hedgerows were not significantly different than most other hedgerows in the given year when evaluated together .

Although the farms in this study were near each other and experience similar climates, there can still be differences in growing conditions for each hedgerow, such as water availability, which has been shown to influence the levels of phenolics in blueberries 101 and strawberries 102 . Hedgerows 2 and 14 were not significantly different from other hedgerows in 2019, indicating that the blue elderberries can be harvested early in the plant’s lifetime, which allows farmers to earn an early return on the investment of establishing hedgerows. These comparisons show that blue elderberries from hedgerows are a rich source of phenolic compounds. Phenolic compounds were identified and quantified in the blue elderberry based upon retention time, absorbance spectra and authentic standards when available. Concentrations for samples from 2018 are presented in Table 4, while samples from 2019 are presented in Table 5. Two peaks with significant area were observed in the HPLC chromatograms at 6.96 min and 11.70 min that did not correlate to standards or library matching. Both compounds eluted between the retention time of gallic acid and protocatechuic acid. The first eluting compound had a maximum absorbance at 300 nm while the second compound had a maximum absorbance at 280 nm. These peaks were collected individually and further evaluated by accurate mass quadrupole time-of-flight tandem mass spectrometry . TOF acquires mass spectral data by pulsing ions entering the flight tube in an orthogonal beam, therefore full spectra are collected. The data captured is accurate enough to determine the elemental composition therefore allowing identification without standards. The two compounds were tentatively identified using high mass accuracy as 5-hydroxypyrogallol hexoside, a tetrahydroxybenzene , and protocatechuic acid dihexoside .

Accurate mass was especially helpful since commercial standards for these compounds are not available. 5- HPG hexoside was identified by its fragmentation pattern , showing a precursor ion [MH]- at m/z 303.0723 and product ion [M-hexose-H]- at m/z 141.0186 . This compound was one of the most abundant phenolic compounds in the blue elderberry. While no evidence of 5-HPG glycoside was found in the literature, the aglycone has shown to have a high radical scavenging activity compared to other simple phenols 105 .The other novel phenolic compound identified was protocatechuic acid dihexoside, also present in relatively high amount in almost all the samples. The precursor ion [M-H]- at m/z 477.1609 fragmented to give product ions corresponding to [dihexoside – H]- at m/z 323.0981 and [M-dihexose -H]- at m/z 153.0562 m/z . The loss of 324 amu has been identified as the loss of a dihexoside on other phenolic compounds and was proposed to be sophorose or gentiobiose 106. PA is a breakdown product of cyanidin-based anthocyanins and has been quantified in elderberry juice during thermal processing 107. PA has been shown to have pharmacological potential in the prevention and/or treatment of neurodegenerative diseases in humans based on in vitro and in vivo studies . Like other elderberry species, rutin was the predominant flavonol and overall had the highest concentration of any of the flavonols measured, with an average of 57.01 ± 17.42 mg per 100 g FW in 2018 and 51.89 ± 25.53 mg per 100 g FW in 2019. These values fall within the range of what has been found in European elderberry. Other flavonols identified include isoquercetin , kaempferol-3-rutinoside, and isorhamnetin-3-rutinoside, which was also a major phenolic compound in the berry. Isorhamnetin- 3-rutinoside averaged 28.30 ± 14.03 mg per 100 g FW in 2018 and 24.71 ± 14.83 mg per 100 g FW in 2019, which is higher than what has been found in other subspecies42. Overall, the blue elderberry analyzed in the present study has much higher levels of total flavonols as compared to European elderberry 6,59.

In the American elderberry, the main flavonols are rutin followed by isorhamnetin-3-rutinoside whereas in European elderberries, the main flavonols are rutin followed by isoquercetin 49,59. In blue elderberry grown in Slovenia, rutin and isoquercetin were the two predominant flavonols, though the total flavonols in found for the subspecies was similar to the levels found in this study 59 . The predominant anthocyanin present in the blue elderberry is cyanidin-3-sambubioside, like the European subspecies. The average concentration in 2018 was 32.70 ± 10.18 mg per 100 g FW and 29.66 ± 16.81 mg per 100 g FW in 2019. Cyanidin-3,5-diglucoside was the next most concentrated anthocyanin, averaging 20.11 ± 5.63 mg per 100 g FW in 2018 and 19.80 ± 6.92 mg per 100 g FW in 2019. This is unlike European elderberries, in which cyanidin-3-glucoside is typically the second most prominent anthocyanin, except for the Ljubostinja cultivar which has more cyanidin-3,5-diglucoside than cyanidin-glucoside42. Cyanidin-3-sambubioside-5-glucoside and cyanidin-3-glucoside were also quantified in the berries. Cyanidin-3,5-diglucoside and cyanidin-3-sambubioside-5-glucoside were not detected in blue elderberries grown in Slovenia, suggesting the growing location impacts the profile of phenolic compounds or perhaps the two samples going by the same name are not, in fact, related. There were no acylated anthocyanins identified in the blue elderberry, like those abundant in the American elderberry. Overall, total anthocyanin concentrations averaged 61.54 ± 16.70 mg per 100 g FW in 2018 and 58.58 ± 22.18 mg per 100 g FW in 2019. The total concentration of anthocyanins in the berries was much lower compared to the other subspecies 8,49,109. Analysis of European elderberries that measured cyanidin-based anthocyanins found an average of 863.8 ± 49.9 mg per 100 g FW 8 . European elderberries grown in different locations at different altitudes had a range of 289.74 ± 66.18 to 792.66 ± 27.97 mg per 100 g FW 6 . In studies on American elderberries, one had an average of 265 ± 74 mg per 100 g FW 49, another had average of 248 ± 83 mg per 100 g FW 18, and a third had an average of 242.7 ± 91.0 mg per 100 g FW 50 . The flavan-3-ols catechin and epicatechin were measured in the elderberry, vertical tower for strawberries with epicatechin typically present in higher concentrations. The concentrations found in the present study are similar to those found in others, even across subspecies. Blue elderberry grown in Slovenia had 4.40 ± 0.26 mg per 100 g FW of catechin and 8.49 ± 0.37 mg per 100 g FW of epicatechin. The same study found no catechin present in S. nigra ssp. nigra, but 6.37 ± 0.28 mg per 100 g FW of epicatechin. In a study of European berries growing in different locations at different altitudes, total flavanol concentrations ranged from 1.93 ± 0.22 to 9.67 ± 0.66 mg per 100 g FW 6 . The variability in phenolic and anthocyanin content observed in this study is not surprising, as multiple other studies have shown significant variability in other commercialized elderberry subspecies, even with clonally propagated cultivars. For example, Lee and Finn 49 saw an average of 45% higher anthocyanins in their second harvest of American elderberries grown inOregon as compared to their first harvest, though the total phenolics only increased an average of 20%. Johnson et al. 54 observed significant changes between subsequent years in anthocyanin and phenolic compound concentrations in juices prepared from American elderberry grown in two locations in Missouri. For example, in the Adams II sample grown in one Missouri location, the quercetin 3-rutinoside content was 298 ± 48 mg L-1 in 2012, 792 ± 143 mg L-1 in 2013, and 47 ± 13 mg L-1 in 2014 54.

In a study of 107 wild American elderberries samples grown in five regions of the eastern United States by Mudge et al. 110 high variability was found in selected flavonoid compounds with an average RSD of 55.3% across samples. Overall, there is a body of evidence demonstrating that elderberry composition can vary year to year or by growing conditions even in clonally-propagated cultivars; therefore, it may be necessary to use standardization techniques for bioactive compounds in order to maintain consistent quality in elderberry products. Blue elderberry grown in California hedgerows has similar levels of sugar, organic acids, and TPC to the European and American elderberry subspecies. Furthermore, the phenolic profile of blue elderberry is similar to European elderberry, in that chlorogenic acid, rutin, and cyanidin-3-sambubioside are the predominant hydroxycinnamic acid, flavonol, and anthocyanin, respectively. However, anthocyanin levels are significantly lower in the blue elderberry compared to European and American subspecies, yet the levels of total flavonols appears to be much higher than the other subspecies. 5-Hydroxypyrogallol hexoside and protocatechuic acid dihexoside were identified for the first time in elderberry, which could potentially serve as markers of this subspecies in products that use blue elderberry. There was considerable variation within and between hedgerows in both harvest years, but this appears to be a common attribute for the elderberry species. Blue elderberries have many ecological benefits for farms when planted in hedgerows, grow well in challenging environments, are not killed by wildfires and can therefore, serve as a sustainable source of an increasingly popular fruit.The elderberry is a deciduous, multi-stemmed shrub or small tree.2,14 It can grow several meters high and in diameter and produces hundreds of clusters of aromatic flowers in the spring, that mature into small berries in summer. The plant grows well in a variety of soils and climates, and is a native of Northern America, Europe, and parts of Asia.2,14 While there are many subspecies within Sambucus nigra, the primary subspecies widely grown and commercially cultivated include S. nigra ssp. nigra found across Europe, and the “American” subspecies S. nigra ssp. canadensis, which is native to the eastern regions of North America.56 The blue elderberry , is a drought-tolerant subspecies native to the western region of North America. The blue elderberry grows in riparian ecosystems from southern British Columbia, Canada to northwest Mexico.84 In California, there have been efforts for more than a decade to increase the levels of blue elderberry planted in hedgerows on farms because of its environmental benefits, such as improving the air, water, and soil quality, as well as providing food and shelter for pollinators.111 It is now recognized that these mature hedgerow plants can be a source of locally grown elderberries and elder flowers to increase income and sustainability for the farm. However, to date there is no data on the concentration of the aroma or phenolic compounds in the flowers from this hardy heat-tolerant subspecies. The berries, flowers and bark of the elderberry plant have a long history of use by humans as both food and traditional medicine. Seeds have been found in archeological sites that date to the late stone age and their medicinal use is documented in the writings of Theophrastus , Pedanius Dioscorides and Gaius Plinius Secundus . 

However, beyond these thresholds, flavonols started to degrade, and there was an indirect relationship between the flavonol content and the percentage of kaempferol for both cultivars, this relationship being significant only for Cabernet Sauvignon .The weather conditions during the execution of this experiment were highlighted by greater maximum daily temperatures when compared to the reference period . This was more prominent during the driest months . Moreover, global solar radiation received at the experimental site was to ca. 200 W m−2 greater than the total solar radiation recorded within the reference period . The combinatory effect of LR and LT treatments caused a 58% reduction of LAI and a 45% increase of canopy porosity . However, neither leaf area nor pruning mass showed significant differences between treatments. On the other hand, yield components were mostly affected by the shoot thinning treatments . Thus, shoot thinned vines showed lower number of clusters, yield, and Ravaz Index , and increased leaf area to fruit ratio per vine as expected. The extent of yield reductions was 55% and 47% for ST and LRST vines, respectively . Berry mass was not significantly affected by canopy management practices during the berry ripening although vines subjected to LRST tended to result in smaller berries . The most influential effects observed on berrychemistry were due to shoot thinning treatments . Therefore, shoot thinned vines had greater total soluble solids and lower titratable acidity from mid-ripening to harvest. However, no significant effect was observed on the must pH . Shoot thinned grapevines had higher anthocyanin content at veraison . However, low round pots we did not measure any changes to anthocyanin content at harvest as affected by the canopy management practices applied. Although anthocyanin content was not affected, anthocyanin composition was modified by treatments from mid-ripening to harvest .

Berry skins of ST and LRST grapevines showed a lower 3’4’5’/3’4′ ratio leading to increased proportion of cyanidins and peonidins in detriment of malvidins which was the most abundant anthocyanin found in berry skins . During the monitored period, different canopy management practices modified berry flavonol content . The berries from LRST grapevines showed the greatest berry skin flavonol content, while, at harvest, the flavonol content of LR, ST, and LRST was similar and greater when compared to the UNT content. Not only canopy management practices modified flavonol content but they also affected their composition. The LRST treatment had a higher proportion of kaempferol and quercetin from midripening to harvest and lower of proportion of myricetin after veraison . As expected, berry IBMP content decreased throughout ripening with all the canopy management practices tested in this study . However, we found the significant differences among treatments after veraison and at harvest. The LRST treatment resulted in the lowest IBMP content from mid-ripening to harvest. Correlation analysis between the monitored variables at harvest revealed a strong relationship between canopy architecture variables and berry flavonol content . Moreover, canopy porosity was strongly correlated to the kaempferol proportion in berry skins . On the other hand, a lower yield due to canopy management practices was related to decreased IBMP and increased flavonol content . Finally, a strong relationship was found between TSS and TA with the leaf to fruit ratio . Finally, a higher solar exposure estimated as the kaempferol proportion was strongly correlated with decreased anthocyanin berry contents and yield .Analysis of labor operations cost of canopy management practices indicated that the most expensive canopy management practices was the LRST where growers received a 53% lower income per hectare. Thereby, productivity data provided evidence that the cost of producing a kg of anthocyanin and removing a µg of IBMP was 10-fold greater in LRST compared to UNT per ha .Yield components were mainly affected by shoot thinning practices, decreasing the number of clusters and yield per vine leading to unbalanced vines according to the previous studies . Yield per meter of row is increased quasilinearly with the increase in shoot density per meter of row as indicated by previous studies . The lack of effect of LR on yield was corroborated by several studies when a late leaf removal was applied. Moreover, Yu et al. and Cook et al. reported that grapevines may produce more leaves than required, especially in warm climates, therefore, the increase in canopy gaps and the diminution of external leaf layers did not elicit decreases in yield as they were not severe enough reductions to the functional leaf area. The RI between 5 and 10 is considered optimum for vine balance .

Therefore, RI and leaf area to fruit ratio data reported with the grapevines subjected to shoot thinning were under cropped that led to lower yields. In our study, Cabernet Sauvignon vines were not able to modulate their vegetative biomass in response to canopy management practices applied. Previous studies showed that pruning mass values up to 1 kg/m of row were considered optimal under warm climate . In our experiment the pruning mass per meter of all treatments ranged from 0.5 to 0.7 kg/m without differences between treatments. Moreover, although the shoot counts were obviously different between treatments, we did not find differences in the pruning mass, that suggested lower lateral expansion and/or reduced shoot diameter with an increasing number of shoots as previously reported Brillante et al. . Consequently, we found that the mass of each shoot ranged from 28 and 25 g in UNT and LR, respectively, to 45 and 42 g in ST and LRST, respectively, corroborating work by Brillante et al. .Martınez-Lüscher et al. reported negligible variation of berry mass of Cabernet Sauvignon due to higher solar exposure under irrigated viticulture. Similarly, berry masses remained unaffected by a higher solar exposure of the cluster due to canopy management practices unless they were directly exposed to sunlight where berries may suffer dehydration as previously reported by Mijowska et al. . This has been attributed to the effect of the higher temperatures with subsequent increases in berry transpiration that affected cell division and elongation . Under our experimental conditions, shoot thinning treatments hastened berry ripening by enhancing the TSS to ca. 2.5°Brix and decreasing must titratable acidity by 0.6 g•L−1 at harvest. Thus, overexposure has been related with higher pH due to the elevated temperature that berries overcome and the subsequent organic acid degradation . Nevertheless, Wang et al. recently suggested that changes on the source-to-sink ratio induced by shoot thinning might have more influence on berry maturity than the change in the microclimate they reported.Cultural practices have been related to increased anthocyanin content . However, in agreement with other studies , under our experimental conditions, berry anthocyanin content did not increase due to LR, ST or LRST. Similarly, anthocyanin content was not affected by mildexposure in berries collected from the commercial vineyardeither. Increasing exposure was detrimental for anthocyanin content as the overexposed berries were subjected to higher temperatures that may have impaired their accumulation .

The anthocyanin berry content at harvest is the result between synthesis and degradation rates. It was reported anthocyanin synthesis may be up-regulated by greater exposure . Therefore, ST and LRST increased the anthocyanin content at mid-ripening because of the increasing solar exposure . Additionally, it was recently highlighted that some members of the dihydroflavonol reductase and UFGT genes required for anthocyanin biosynthesis were moderately up-regulated in LR treated berries leading to increases of anthocyanin content at mid-ripening . However, at harvest, plastic pots 30 liters no significant effect of canopy management practices on anthocyanin content was found, and this result is corroborated by Pastore et al. who reported no beneficial effect due to higher cluster exposure in warm climates. Although cultural practices may induce different cluster temperatures by increasing exposure, we did not find a clear relationship between exposure and cluster temperature when kaempferol proportion are low suggesting that results of this work were mainly explained by different exposures. Nevertheless, under elevated temperatures, a down-regulation of anthocyanin biosynthesis and enhanced rates of degradation have beenreported . Those authors suggested that high temperature induced anthocyanin degradation by enhancing the expression of VviPrx31 and consequently the peroxidase activity. Likewise, overexposed berries with kaempferol proportions greater than 10% were subjected to higher temperatures that dramatically decreased anthocyanin content. Matus et al. reported that flavonol content increased by two-fold in exposed berries compared to non-exposed. Our results corroborated this finding partially, depending on the level and duration of exposure, canopy position of the berries, and orientation of the vineyard. Therefore, when flavonol proportion was below 10% of kaempferol, flavonol content increased; but would decrease after this inflection point due to degradation. Matus et al. further indicated that this increase in flavonol may be driven by the up-regulation of MYB12 and flavonols synthase 4 due to the greater exposure suggesting that FLS4 could be a target of MYB12 in grapevine. Accordingly, Sun et al. found that increased accumulation of flavonols in light exposure berries, were accompanied by the up-regulation of several genes of the FLS gene family suggesting that they may be functionally redundant in response to light signal. During the experiment conducted in the 2019 growing season, the kaempferol proportion increased in LR and ST treatments, but largest increase was measured when ST and LR were applied concurrently. Likewise, the higher the degree of exposure degree a greater kaempferol accumulation was observed during the 2017 growing season. The increase in kaempferol in total proportion of flavonols was accompanied with a concomitant decrease of quercetin and myricetin proportions. These results are corroborated with our previous work performed on Merlot and Cabernet Sauvignon. , and by others on Cabernet Sauvignon, Nero d’Avola, Raboso Piave, and Sangiovese in Italy . We previously reported the proportion of kaempferol was a feasible tool for accounting the solar radiation received by berry due to the greater canopy porosity and this corresponded to the 1930 W·m−2 of global radiation accumulated at the research site in Experiment 3. On the other hand, the higher proportion of quercetin derivatives in detriment of myricetin derivatives found in LR vines has been related to down regulation of F3’5’H family genes . Previous work on red grapevine berries, indicated that IBMP content decreased with greater solar exposure due to the canopy management practices during berry ripening . In our work, the lowest IBMP content was measured in LRST berries. Our results indicated a negative and linear relationship between leaf to fruit ratio and IBMP content.

Conversely, the relationship between kaempferol proportion and IBMP was not significant. Therefore, our data suggested that the decrease of IBMP content was better explained by changes in the source-sink balance rather than differences in solar exposure. Likewise, Koch et al. provided evidence that solar exposure affected IBMP content to a greater extent when canopy porosity was enhanced before fruit set and not during berry ripening corroborating our results. The lower berry IBMP content was explained by a diminution of the accumulation rates rather than increased rates of degradation due to canopy management practices and restriction of applied water between fruit set and veraison in a warm climate.Vineyard-fl oor management strategies, such as weed control and cover-cropping, have wide-ranging impacts both inside the vineyard, in terms of crop management and productivity, and outside the vineyard, in terms of runoff and sediment movement into streams and rivers. The increasing importance of water-quality issues statewide, including in Monterey County where the Salinas River drains into the Monterey Bay National Marine Sanctuary, highlights the need for management strategies that limit environmental impacts. Growers are interested in alternative weed-control practices and cover crops, but they need information in order to balance benefits with the economic realities of wine-grape production. We established a 5-year experiment in a commercial vineyard in Monterey County with the intent of identifying effective practices that can be integrated into the cropping system without negatively affecting winegrape production. The vineyard floor consists of two zones: the rows, a 2- to 4-foot-wide swath underneath the vines, which are managed primarily to control weeds by herbicide applications or cultural practices ; and the middles, interspersed between the rows, which are vegetated by cover crops or resident vegetation in the dormant season, and are tilled or left untilled in spring. Growers manage weeds in rows to reduce competition for water, nutrients and light , and to prevent tall-statured weeds such as horse weed from growing or climbing into the canopy, where they interfere with harvest.

The Berry phase not only has close connections to the optical selection rules that allow optical generation and detection of the valley-polarized carriers by circularly polarized photons, but also plays a central role in novel electron dynamics and transport phenomena in TMD and graphene layers, such as the valley Hall effect. In principle the Berry phase, together with other effects from inversion symmetry breaking, can have profound consequences for the wave function and energy spectrum of the excited states in two-dimensional materials. TMD monolayers are known to host strongly bound excitons with a remarkably large exciton binding energy due to enhanced Coulomb interactions in 2D. It was recently predicted that the Berry curvature of Bloch states can add an anomalous term to the group velocity of electrons and holes and creates an energy splitting between exciton states with opposite angular momentum Fig. 1a shows a simplified exciton energy spectrum illustrating the exciton fine structure based on our ab initio GW-Bethe-Salpeter equation calculations. The 2p+ and 2p− exciton states are split in energy with opposite order for the K and K’ valleys due to the opposite chirality in the two valleys. Such novel exciton fine structure, which embodies important wave function properties arising from the Bloch band geometry, can strongly modify the intraexcitonic light-matter interactions. Experimental observation of this predicted exciton spectrum, however, has been challenging, blueberry grow pot because it requires new spectroscopic probe that can distinguish both the momentum valley and the exciton angular momentum. Here, we report the first observation of the Berry-phase effect in the exciton spectrum of MoSe2 monolayer using intraexciton optical Stark spectroscopy.

We demonstrate that the degeneracy between the 2p±-exciton states is lifted by the Berry phase effect, and enabling a valley-dependent Autler-Townes doublet from strong intraexciton light-matter coupling. We coherently drive the intraexciton transitions using circularly-polarized infrared radiation, which couples the 1s exciton to the 2p+ or 2p− states selectively through the pump photon polarization . The pump-induced changes in the 1s exciton transition are detected by circularly polarized probes, which selectively measure the K or K’-valley excitons. Independent control of pump and probe photon polarization enables us to distinguish the exciton fine structures in the K and K’-valleys. We determine an energy splitting of 14 meV between the 2p+ and 2p− exciton states within a single valley, and this energy splitting changes sign between K and K’-valleys. We determine the 1s-2p transition dipole moment to be 55±6 Debye. This leads to an optical Stark shift that is almost 40 times larger than the interband counterpart under the same pump detuning and driving optical field strength. Such strong and valley-dependent intraexciton transitions open-up new pathways for the coherent manipulation of quantum states in 2D semiconducting materials using infrared radiation. To investigate the fine structure of the excitonic p-manifold, we fabricated a high quality MoSe2 monolayer that is encapsulated in hexagonal boron nitride layers using mechanical exfoliation and stacking following Ref. 21. The sandwiched hBN-MoSe2-hBN heterostructure was then transferred to an alumina-coated silver surface . The device was kept in vacuum at 77K for all optical measurements. This Aexciton peak arises from the optical transition between the ground state and the lowest energy 1s exciton state in MoSe2 monolayer, which is well-separated from the higher-lying exciton states due to strong Coulomb interactions in TMD monolayers.

It shows clearly that the 1s exciton transition exhibits avoided-crossing behavior in both valleys, which evolves gradually from energy blue shift to splitting and then to redshift as the pump photon energy is decreased. Due to the time-reversal symmetry between K and K’-valleys in MoSe2 monolayer, this observation also indicates that the 2p+ and 2p− exciton states are non-degenerate and has an energy difference of 14 meV in a single valley. We further plot the blue- and red-shifted 1s resonance as a function of the infrared pump photon energy in Fig. 3b. We find that the energy shifts induced by the intraexciton optical Stark effect are almost 40 times larger than its interband counterpart at the same pump intensity and resonance detuning21–23. To better understand the experimental results, we performed ab initio GW-BSE calculations using the BerkeleyGW26–28 package to determine the exciton energy levels and optical selection rules of exciton and intraexciton transitions in monolayer MoSe2. In these calculations, environmental screening effects from the hexagonal boron nitride encapsulation layers are included18 from first-principles . The simulation confirms the energy level diagram of the 1s, 2p+, and 2p− excitons and the optical selection rules in K and K’- valleys in Fig. 1a. Our calculations find that the energies of the 1s and 2p− exciton states are separated by 117 meV, with 2p+ exciton states further separated by 7 meV in K-valley. The energetic order of 2p+ , and 2p− excitons states is opposite in the K’-valley, as a result of time-reversal symmetry. Although the 2p± excitons are dark in linear optics, they are optically active when coupled to the 1s exciton with circularly-polarized light . For example, our calculations show that the 1s-2p+ intraexciton transition couple exclusively to the left-handed circularly polarized light with a transition dipole moment of 42 Debye. The 1s-2p− intraexciton transition, on the other hand, coupled exclusively to the right-handed circularly polarized light.

The experimentally observed intraexciton dipole moment and valley-dependent exciton fine structure match reasonably well with the ab initio GW-BSE calculations. The combination of 2p±-exciton splitting and extremely strong intraexcitonic light-matter interaction allow us to observe valley-dependent Autler-Townes doublets at higher pump intensity in MoSe2 monolayer. Towards this goal, we fabricated a hBN-encapsulated MoSe2 heterostructure on a zinc-sulphide substrate, where the local field factor on the sample for the infrared pump light is more favorable than that for MoSe2 on alumina coated silver substrate . Fig. 4c,d show the splitting energy in the Autler-Townes doublet at resonant excitation scales linearly with the excitation field strength, as expected from Eq. 129,30. At an effective driving intensity of 50±10 MW/cm2, which corresponds to a local optical field strength of 200±20 kV/cm, the Autler-Townes splitting can reach ~24 meV in both valleys. This Autler-Townes doublet leads to a valley dependent electromagnetically induced transparency in the 1s exciton transition, where the absorption at the 1s exciton resonance is reduced by more than 10-fold compared to the undriven exciton . Our findings offer a new and effective pathway to coherently manipulate the quantum states and excitonic excitations using infrared radiation coupled to the 1s-2p+ intraexciton transition.The dashed-lines indicate the peak position of unperturbed A-exciton. The dotted lines are guides to the eyes for the peak position at different driving energies. The spectra are offset for clarity and labelled according to the excitation energy . The spectra evolve from energy redshift to splitting and then to blue shift, as the driving energy is increased. The calculation is based on the Hamiltonian shown in Eq. 1. Exciton-photon coupling leads to avoided-crossing and the observed peak splitting at resonant coupling. This resonant coupling occurs at driving photon energy of 142 meV and 128 meV in the K and K’ valleys, respectively. The MoSe2 monolayer encapsulated in hBN flakes were prepared with a polyethylene terephthalate stamp by a dry transfer method21. Monolayer MoSe2 and hBN flakes were first exfoliated onto silicon substrate with a 90 nm oxide layer. We used PET stamp to pick-up the top hBN flake, monolayer MoSe2, and bottom hBN flake in sequence with accurate alignment based on an optical microscope. The hBN/MoSe2/hBN heterostructure was then stamped on a silver substrate coated with a 85 nm alumina layer or on a zinc sulphide substrate. Polymer and samples were heated to 60oC for the pickup and 130oC for the stamping process. Finally, hydroponic bucket the PET was dissolved in dichloromethane for 12 hours at room temperature. The sample temperature was kept at 77 K in a liquidnitrogen cooled cryostat equipped with BaF2 window during optical measurements. Pump-probe spectroscopy study is based on a regenerative amplifier seed by a mode-locked oscillator . The regenerative amplifier delivers femotosecond pulses at a repetition rate of 150 kHz and a pulse duration of 250 fs, which were split into two beams. One beam was used to pump an optical parametric amplifier and the other beam was focused onto a sapphire crystal to generate supercontinuum light for probe pulses. Femtosecond mid-infrared pump pulses with tunable photon energies were generated via difference frequency mixing of the idler pulses from the optical parametric amplifier and residual of fundamental output from regenerative amplifier in a 1 mm thick silver gallium sulphide crystal.

The mid-infrared pulse duration is ~350 fs. The pump-probe time delay was controlled by a motorized delay stage. The probe light was detected by high sensitivity CCD line camera operated at 1000 Hz. The helicity of pump and probe pulses was independently controlled using Fresnel rhomb and broadband quarter-wave plates, respectively. The experiment followed a reflection configuration with a normal incidence and collinear pump-probe geometry, where the absorption spectra are extracted from the reflectance contrast as described in the supporting information.This work was primarily supported by the Center for Computational Study of Excited State Phenomena in Energy Materials, which is funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division under Contract No. DE-AC02-05CH11231, as part of the Computational Materials Sciences Program which provided the experimental measurements and GW-BSE calculations. The sample fabrication and linear optical spectroscopy was supported by the US Army Research Office under MURI award W911NF-17-1-0312. The pump-probe setup was supported by the ARO MURI award W911NF- 15-1-0447. This research used resources of the National Energy Research Scientific Computing Center , a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, and the Extreme Science and Engineering Discovery Environment , which is supported by National Science Foundation grant number ACI-1548562. S.T. acknowledges support from NSF DMR-1552220. K.W. and T.T. acknowledge support from the Elemental Strategy Initiative conducted by the MEXT, Japan and the CREST , JST. E.C.R acknowledges support from the Department of Defense through the National Defense Science & Engineering Graduate Fellowship Program. C.-K.Y. and C.S.O. acknowledges useful discussion with Prof. Ajit Srivastava. Viruses are obligate intracellular pathogens that require living host cells to replicate and spread in the infected plant. During compatible interactions, viruses overcome the plant immune system and hijack host cellular processes to establish active infections . Viruses disrupt the plant cell cycle, inhibit cell death pathways, restrict macromolecular trafficking, alter cell signaling, protein turnover, and transcriptional regulation, and suppress defense mechanisms. The interference with these processes in the host leads to a wide range of plant developmental and physiological defects . Cultivated grapevines are highly susceptible to a variety of viruses and viroids, which cause significant crop losses and shorten the productive life of vineyards. More than 65 different viral species classified in at least 15 families have been reported to infect grapevines, which represents the highest number of viruses so far detected in a single cultivated plant species . Although these viruses are generally transmitted by plant-feeding insects or soilborne nematodes, they can also be spread through infected propagation material . Grapevine red blotch is a viral disease discovered in northern California in 2008 that has become a major economic problem for the wine industry in the USA . This disease is caused by the Grapevine red blotch-associated virus , a circular ssDNA virus with resemblance to geminiviruses, which infects wine grape cultivars with significant detrimental effects on productivity . The incidence and severity of the red blotch symptoms vary depending on the grape cultivar, environmental conditions, and cultural practices . In red-skinned varieties, GRBaV infections result in the appearance of red patches on the leaf blades, veins, and petioles; in white-skinned varieties, they manifest as irregular chlorotic regions on the leaf blades. GRBaV also affects berry physiology, causing uneven ripening, higher titratable acidity, and lower sugar and anthocyanin content, among others . Consequently, must and wine produced from infected berries present altered flavor and aroma. To date, there is limited information on how GRBaV infections affect grape metabolism. Comprehensive analyses to study specific cellular processes that GRBaV exploits to promote infections in berries are still needed, in particular those that relate to changes in berry chemical composition during fruit development. Grape berry development exhibits a double sigmoid growth pattern with three distinct phases: early fruit development, lag phase, and berry ripening.

Using the Crop Sequence Boundaries dataset from USDA, I isolate farm plots that have been idle at least one year in growing years 2016-2023. The CSB produces estimates of field boundaries, crop acreage, and crop rotations using satellite data in combination with other publicly available data. This data is non-confidential, and not tied to or based off of specific producer information. The CSB provides the crop reported by the Cropland Data Layer for each area defined over an 8 year period. The field boundaries defined in this analysis are based off of cropping decisions for growing years 2016-2023 . I use the crop sequence boundary layer instead of yearly CDLs because the CSB aggregates land to field level, which reduces noise and any error that is inherent in the data used to construct the CDLs . The CSB also allows me to follow the cropping decisions for a single plot of land over multiple growing seasons. This allows me to have certainty when identifying the last crop grown on a plot of fallow land. Maps showing the crops last grown on fallow land are shown in figure 7. I use data from the California Department of Water Resources to construct the bounds of the analysis . The San Joaquin Valley is composed of the San Joaquin River and Tulare Lake hydrologic regions. I combine these two areas into a layer to use as the boundary for the SJV in the rest of the analysis. Hagerty constructs mean water requirement per acre and mean revenues per acre for 19 different crop categories using data from California 2007-2018. These data are shown in Appendix 1. I assign CDL crop code values found in the CSB data to these categories. I take the constructed mean revenue and mean water needs to use in equation , nft hydroponic system the farmer’s optimization problem.

I leave A as a parameter and derive with respect to A. I solve equation for MCW to derive a ”choke price” of water per acre at which point a farmer would no longer want to plant their crop and instead will fallow their land. Crops are ordered from lowest value of MCW to highest. A low calculated value of MCW implies that the farmer cannot afford higher costs of water, and will likely make adaptations to reduce water costs. The land growing crops that have a low tolerance for rising water costs will be the first candidates for solar transition. The other ordering condition will be proximity to existing transmission lines. Transmission lines are hugely important in determining initial costs of bringing a solar farm online, and thus, planned solar projects in SJV are concentrated around existing infrastructure . Land parcels within 100 meters of high-voltage transmission lines will be preferred to those farther away.The calculated MCW values for crop categories are shown in the table below, listed from lowest value to highest value. These values represent the highest possible water cost per acre that a farmer growing a given crop would be willing to tolerate. These values are an upper bound estimates because the only costs considered in a farmer’s production function in this analysis are water costs. These values are calculated using Hagerty derived mean revenues and water needs per acre.6 In the map below, these values are represented with graduated colors representing the threshold water cost per acre values for the last crop grown in a non-fallow year on a land parcel. Farmers owning unirrigated grassland are unwilling to pay for water because they don’t use it on this kind of land. Should these lands be transitioned to solar energy generation,they would not provide any social benefit in the form of water savings, but would provide private benefit to the farmer by substantially increasing their revenues per acre. Other crops that have lower tolerances for water price shocks are safflower and alfalfa.

Safflower is not very water intensive, but does not provide much value per acre. Alfalfa, on the other hand, provides four times the revenue per acre of safflower , but has over twice the water needs per acre. Referring back to figures 2 and 3, safflower is preferred in drier years, and alfalfa grown on plots in wet years are commonly fallowed or swapped for vineyards, which have slightly lower water intensity. These kinds of farmers are the ideal targets for policy intervention to induce solar adoption. On the other end of the spectrum, truck crops like carrots and berries have extremely high tolerance for increasing water costs, as they are hugely valuable per acre, and aren’t hugely water intensive. These farmers will not likely be enticed into using their acreage for solar energy, and thus, the lands housing these crops should not be targeted by policy for land transition. Given the importance and expense of high-voltage transmission lines, I isolate plots of land that are within 100 meters of pre-existing high-voltage transmission infrastructure. These land parcels have been fallow in one or more growing season 2016-2023, and are symbolized based on the crop coverage in its last active season in figure 8. The number of single-cropped land parcels are displayed in parentheses. Such lands previously grew almonds and pistachios, grains, and tomatoes most commonly, followed by previously non-irrigated grassland. In total, there are over 30,000 acres of land identified in the SJV that have been fallow at least once out of the last eight growing seasons and are within 100m of existing high-transmission transmission lines. If the search criteria is expanded to include any farm plots within 400m of existing high-voltage transmission lines, an additional 60,000 acres qualify for solar transition. The maps of agricultural lands eligible for solar transition in the San Joaquin Valley are displayed in figure 9.

This analysis identifies over 90,000 acres of agricultural land that would benefit from solar transition. If all of the acreage identified were to produce solar energy instead of traditional crops, very little other agricultural land would need to be removed from irrigation to help the San Joaquin Valley achieve its groundwater conservation goals as set forth by SGMA. The land identified can provide 3-4x as much energy generating capacity as already exists in the San Joaquin Valley, given average generating capacity per acre in the area . The calculated threshold cost of water shows relative sensitivity to water pricing increases, as it is based on a ratio of water usage and crop value. The ordering of these values tells us which farmers are more likely to benefit privately from a solar land transition. As scarcity increases due to increased pressure from policymakers on cutting back agricultural water use, local governments may find it beneficial to target lowest-cost land transitions first, before taking more profitable agricultural land out of production. Using the calculated values of MCW , policymakers can better anticipate which farmers are more sensitive to water price shocks. These farmers growing crops that are especially sensitive may be targeted by policy interventions that incentivize investment in solar farming. Further, using existing transmission line infrastructure allows for lower input costs to bring solar farms online. Combining these two data can be a powerful way to guide land transition in the San Joaquin Valley. Future research can relax the assumption of equal water access across farmers, and allow for more nuanced production and cost functions for growers. These additions will better reflect the reality that farmers face with water, land, and other input costs. This ordering system provides a loose guide for which farmers may react to increased water prices induced by scarcity and regulation, and adding more flexibility will improve the ability to apply results to policy. This analysis aligns well with economic literature utilizing positive mathematical programming. These economic tools used in more comprehensive agricultural production models including Howitt , M´erel and Howitt , and Howitt et al. . Agriculture is a key human activity in terms of food production, hydroponic nft system economic importance and impact on the global carbon cycle. As the human population heads toward 9 billion or beyond by 2050, there is an acute need to balance agricultural output with its impact on the environment, especially in terms of greenhouse gas production. An evolving set of tools, approaches and metrics are being employed under the term “climate smart agriculture” to help—from small and industrial scale growers to local and national policy setters—develop techniques at all levels and find solutions that strike that production-environment balance and promote various ecosystem services. California epitomizes the agriculture-climate challenge, as well as its opportunities. As the United States’ largest agricultural producing state agriculture also accounted for approximately 8% of California’s greenhouse gas emissions statewide for the period 2000–2013. At the same time, California is at the forefront of innovative approaches to CSA . Given the state’s Mediterranean climate, part of an integrated CSA strategy will likely include perennial crops, such as winegrapes, that have a high market value and store C long term in woody biomass. Economically, wine production and retail represents an important contribution to California’s economy, generating $61.5 billion in annual economic impact. In terms of land use, 230,000 ha in California are managed for wine production, with 4.2 million tons of winegrapes harvested annually with an approximate $3.2 billion farm gate value.

This high level of production has come with some environmental costs, however, with degradation of native habitats, impacts to wildlife, and over abstraction of water resources. Although many economic and environmental impacts of wine production systems are actively being quantified, and while there is increasing scientific interest in the carbon footprint of vineyard management activities, efforts to quantify C capture and storage in annual and perennial biomass remain less well-examined. Studies from Mediterranean climates have focused mostly on C cycle processes in annual agroecosystems or natural systems. Related studies have investigated sources of GHGs, on-site energy balance, water use and potential impacts of climate change on productivity and the distribution of grape production. The perennial nature and extent of vineyard agroecosystems have brought increasing interest from growers and the public sector to reduce the GHG footprint associated with wine production. The ongoing development of carbon accounting protocols within the international wine industry reflects the increased attention that industry and consumers are putting on GHG emissions and offsets. In principle, an easy-to-use, wine industry specific, GHG protocol would measure the carbon footprints of winery and vineyard operations of all sizes. However, such footprint assessment protocols remain poorly parameterized, especially those requiring time-consuming empirical methods. Data collected from the field, such as vine biomass, cover crop biomass, and soil carbon storage capacity are difficult to obtain and remain sparse, and thus limit the further development of carbon accounting in the wine sector. Simple yet accurate methods are needed to allow vineyard managers to measure C stocks in situ and thereby better parameterize carbon accounting protocols. Not only would removing this data bottleneck encourage broader participation in such activities, it would also provide a reliable means to reward climate smart agriculture.Building on research that has used empirical data to compare soil and above ground C stocks in vineyards and adjacent oak woodlands in California, this study sought to estimate the C composition of a vine, including the relative contributions of its component parts . By identifying the allometric relationships among trunk diameter, plant height, and other vine dimensions, growers could utilize a reliable mechanism for translating vine architecture and biomass into C estimates. In both natural and agricultural ecosystems, several studies have been performed using allometric equations in order to estimate above ground biomass to assess potential for C sequestration. For example, functional relationships between the ground-measured Lorey’s height and above ground biomass were derived from allometric equations in forests throughout the tropics. Similarly, functional relationships have been found in tropical agriculture for above ground, below ground, and field margin biomass and C. In the vineyard setting, however, horticultural intervention and annual pruning constrain the size and shape of vines making existing allometric relationships less meaningful, though it is likely that simple physical measurements could readily estimate above ground biomass. To date, most studies on C sequestration in vineyards have been focused on soil C as sinks and some attempts to quantify biomass C stocks have been carried out in both agricultural and natural systems.

We identify TFP by estimating a Cobb-Douglas production function with inputs measured per hectare, implicitly imposing constant returns to scale on the production technology. In such a setting, the inclusion of a measure of farm size as an explanatory variable identifies any relationship between farm size and TFP . The Mexican Family Life Survey is a longitudinal survey of Mexican households, representative of the Mexican population at the national, urban, and rural levels. The MxFLS is a rich source of data for this analysis, as controlling for unobservable farm and community level characteristics using fixed effects is potentially important for determining the farm size – productivity relationship. Further, the decade long span of the surveys allows for a careful analysis of how the size-productivity relationship has evolved in the wake of NAFTA and contemporaneous reforms affecting the Mexican agricultural sector. The three survey rounds – 2002, 2005-06, and 2009-128 – tracked a broad range of individual, family, and community characteristics for the 8,437 initial households. The second and third waves of the survey successfully re-interviewed 90% and 94% of first wave households, respectively. Individuals from the first wave formed new households at annual rates of 3.6% and 4.7% between the first and second and the second and third waves, with 83% of newly formed households being re-interviewed in the third survey wave. While not representative of the Mexican agricultural sector per se, the MxFLS is representative of both rural and non-rural Mexican households. As such, the use of the dataset to study Mexican agriculture has the important caveat that it under represents the larger, stacking pots commercial agricultural operations to the degree that they are not family farms.

A comparison with the 2007 Agricultural Census reveals that both the census and MxFLS have less than 5% of farms that are greater than 50 ha. However, it is important to note that these “large” farms are not necessarily the same as those in the census because they are family-run farms and do not include corporate-run, commercial agricultural operations. In comparison to the 2007 census, the MxFLS over-represents farms less than 2 ha and under-represents farms between 20 ha and 50 ha. This is true for each survey wave, highlighting that while the MxFLS is not representative of the Mexican agricultural sector in its entirety, it is appropriate for studying household farms in Mexico. We employ a farm level analysis using all MxFLS households engaged in agricultural production. A plot-level analysis is not feasible because agricultural input data is recorded at the household level and is therefore not plot specific. However, as we are primarily concerned with documenting the farm size – productivity relationship in Mexico and how it has changed over time, and we are less concerned with fully explaining its determinants, a farm level analysis will suffice. Households in the MxFLS move in and out of agricultural production between survey waves. An unbalanced panel is constructed through two stages of restricting the MxFLS data: first, cross-sections of households with complete farm data are identified and cleaned to eliminate outliers, and second, the unbalanced panel is formed out of all households that appear in two or more MxFLS survey waves. Table 2.1 shows all households using plots for agricultural production in a given survey wave are referred to as agricultural households, whereas all households with plot size and output data for all non-fallow plots are referred to as complete farms.

The intermediate group, farms with farm size data, includes all farms with complete farm size data but not necessarily complete production data – this less restricted dataset increases the sample size at the expense of potentially introducing some measurement error, and is an alternative treatment of the data that is pursued below. Lastly, the number of farms in the panel includes the number of households with complete farm data in two or more of the survey years. These restrictions on the data leave us with a sample of 566 farms reappearing in two or more survey years. Table 2.2 describes these farms according to the combination of survey years in which they appear. Farms are classified into one of 7 farm size groups, as shown below in Table 2.3. The distribution of farms across these bins is roughly constant over time and across treatments of the data, although the share of farms between 0 and 0.5 ha is falling over time while the share of farms between 0.5 and 1 ha is increasing. Importantly, with the exception of the share of farms between 0.5 and 1 ha in 2002, the distribution does not change in any notable way as we restrict the cross section to form the panel, an indication that use of the panel has not introduced bias along this dimension. There is a considerable range in farm sizes in the sample, ranging from less than one hundredth of a hectare to 45,000 hectares. The median farm size in the panel is 2.5, 2.1, and 3.0 hectares in 2002, 2005, and 2009, respectively, with mean farm sizes of 101, 232, and 218 hectares. Around 75 percent of farms utilize only one plot for production in any given year. The preferred measure of agricultural output is a Fisher quantity index that includes all crop and livestock production for each farm in the MxFLS panel. Crop pricesfrom the Food and Agriculture Organization of the United Nations are used to aggregate crop output. Together with a measure of the value of livestock production, an output index is constructed as detailed in Appendix B.1. The MxFLS offers data on five agricultural inputs other than land: physical capital, draft animals, purchased intermediate inputs, family labor, and non-family labor.

Physical capital is measured as the value of tractors and other machines and equipment owned and draft animals is the value of horses, donkeys, and mules owned by each household in each survey year, deflated to 2002 values. Purchased intermediate inputs are measured using reported expenditures on each of nine agricultural inputs over the course of the previous year, again deflated to 2002 values. An index of family labor is constructed using household members’ time use and employment data in the MxFLS, and is an estimate of annual hours worked on the farm by all household members. In contrast, the non-family labor index is a measure of the number of non-household individuals that worked on each farm in each year, measured in workers and not labor hours. Appendix B.2 provides a detailed discussion of the source and construction of the family labor and non-family labor indices, including a set of alternative family labor indices. Table 2.4 shows the share of panel households using the different input categories in each year, with purchased intermediate inputs shown both collectively and further disaggregated into their nine components. For all of the inputs there exist at least some, if not a majority, of households that have zeros for that input category. This is expected, as farms in the sample are expected to span a range from low technology subsistence agriculture to more modern and input intensive operations. Furthermore, nft hydroponic many inputs may be substitutes for each other, and farms can access these inputs by owning them or by purchasing them in factor markets. Tractor services, for example, may be substituted for with draft animals. Households can either own some combination of these capital stocks or purchase their services from the market. We follow Battese to estimate production functions with observations having zero inputs. Of principle importance is any relationship between inputs per hectare and farm size, as systematic relationships between input intensity and farm size potentially drive a wedge between the farm size – land productivity and farm size – total factor productivity relationships . We calculate the correlation coefficients between logged input per hectare and logged farm size for those farms with non-zero values of usage of each input. These correlations are shown in Table 2.5. Conditional on using the input, the intensity of all inputs used declines with farm size, emphasizing the importance of moving from partial measures of productivity to a comprehensive measure such as TFP.The vast majority of plots are either privately owned property or are part of an ejido – a piece of communally held land where plots are farmed by designated households. It is commonly accepted that ejidos are less productive than privately held farms, although there is little empirical evidence comparing the TFP of these farms using micro data. At least 91% of privately held plots in the MxFLS have some form of formal documentation in any given year, while just 75-84% of MxFLS ejido properties do. Privately held plots primarily have a formal deed or title to the land as documentation, whereas ejido plots primarily have a certificate of ejido status or agricultural rights.

Formal documentation of property rights is important for accessing credit and is expected to be positively correlated with TFP. How property rights are formally documented matters, however, as a certificate of ejido status is often not acceptable to private financial institutions for use as collateral whereas formal deeds are. We control for both separately in the core empirical analysis. Because ejidos may function differently than privately owned parcels, we control for ejido status. Ejido farms make up 58% of the panel, and the ejido status of farms does not change for almost all farms in the panel. Panel farms are located in 92 distinct communities and are grouped into five regions in Mexico: the North, Center, Pacific, South, and Gulf. In the first survey wave, 26% of panel farms are in Northern states where agriculture is characterized by having larger commercial farms with greater importance of the commercial production of maize. In comparison, 50% of first wave farms are in Southern and Central states where agriculture is characterized by more traditional, smallholder maize producers and the commercial production of fruits and edible vegetables . In tests of heterogeneity, we introduce regional interactions with farm size in estimations of equation , allowing the farm size – TFP relationship to vary across agricultural regions. Additional household level controls are grouped into two broad categories: variables describing agricultural practices that are mostly endogenous, and demographic variables that are largely exogenous. Household demographic variables are based on predetermined characteristics of the household head. The panel farms predominantly have male, married, and Spanish speaking heads of household, with little differences across farm sizes or ejido status. Table B.3.5 in Appendix B.3 shows that farms larger than about 5 ha appear to be less likely to have an indigenous household head and more likely to have a literate household head than do smaller farms. Literacy is just one way to measure educational attainment of the household head, and it captures a rather low bar. We measure the education of household head by creating indicator variables for the highest level of formal schooling attended, from no formal education to elementary school, secondary school, high school, or college education. With little variation across survey years, Table B.3.6 in Appendix B.3 shows educational attainment by farm size for 2002 only, showing that a majority of farms have household heads with no more than an elementary school education, while almost one quarter of the panel’s household heads have no formal education at all. The following variables describing agricultural practices of farms are potentially endogenous, and for this reason are not included in the base specifications. They are introduced to shed light on potential channels affecting TFP and the farm size – TFP relationship. Any farm that does not bring any of its crop to market is classified as a subsistence farm, identifying farms that may behave differently than those who do. There is little difference in the prevalence of subsistence farming between ejido and non-ejido farms. As shown in Table B.3.1 in Appendix B.3, subsistence farming decreases with farm size, as expected. We calculate the share of each farm’s crop that is marketed – on average, those farms in the sample that do participate in the market sell around 75% of their production. This appears relatively constant across farm size bins. Alongside subsistence farming practices, Table B.3.1 in Appendix B.3 shows the share of farms engaged in monocropping.