Unfortunately, heat processing and pasteurization can lead to the degradation of anthocyanins resulting in a loss in color or a formation of brown polymers, possibly impacting the acceptability of the final product.Elderberry juice and extracts have been evaluated for their thermal stability, which have shown that anthocyanins degrade following first-order reaction kinetics.138 The stability of anthocyanidins can be reinforced via intra- and inter-molecular interactions with protective structures and flavonoids through a phenomenon termed copigmentation. Notably, acylated anthocyanins, such as those found in American elderberry like cyanidin 3-coumaroylsambubioside, are sometimes more stable during thermal processing due to protective properties of the coumaroyl group folding over the flavylium ion. Like the European elderberry, the blue elderberry does not contain acylated anthocyanins. The thermal stability of anthocyanidins in blue elderberry juice has not yet been evaluated. However, as elderberry juice and extracts are frequently thermally processed to make products, such as jam, syrup, or gummies, it is important to understand the thermal degradation of anthocyanidins in the juice from blue elderberry. The purpose of this study was to determine the stability of the cyanogenic glycosides and main phenolic compounds in blue elderberry juice cooked at 72 °C and 95°C for two hoursto elucidate the kinetics of degradation of these important compounds.HPLC-grade methanol , and LCMS grade acetonitrile, methanol, and formic acid were purchased from Fisher Scientific . Ultrapure water was obtained from a Milli-Q water system . Prunasin was also obtained from Millipore Sigma. HPLC-grade acetonitrile , rutin , isorhamnetin 3-O-glucoside, caffeic acid, chlorogenic acid, -catechin, protocatechuic acid, ammoniumformate, and amygdalin were purchased from Sigma-Aldrich .

Cyanidin 3-Osambubioside chloride and cyanidin 3-O-glucoside were purchased from ExtraSynthese .Ripe berries were harvested in July 2019 from a farm in Winters, vertical plant tower CA at latitude and longitude coordinates of 38.634884, -122.007502. Fruit was selected from all sides of the plant at a variety of heights to obtain a representative sample of berries from each plant. Only fully ripe berries . About 5 kg were harvested from each of shrub in three hedgerows. The plant material was transported to University of California, Davis on ice in plastic gallon bags within two hours of harvest and stored at -20 °C until analysis.Elderberry juice was prepared from 300 g previously frozen berries, thawed at room temperature for one hour in a mesh bag in a metal bowl. Thawed berries were juiced with a manual fruit press . The juice obtained was then aliquoted into glass vials and sealed with a screw cap to avoid evaporation . A time-zero aliquot of juice was immediately placed on ice for analysis. An aliquot was also analyzed for Brix and pH measurements .A hot water bath was prepared using an immersion circulator , set to the desired cooking temperature . The temperature was also monitored with a thermometer in the water bath. Duplicate vials were removed at the following times: 15, 30, 45, 60, 75, 90, 105, and 120 min. An extended processing time was used to observe degradation of more heat-stable compounds as well as to make comparisons to other studies that processed juice for multiple hours at these temperatures. Once removed from the hot water bath, vials were placed immediately into an ice bath for 15 min. Then from each vial, 1 mL of juice was placed in a microcentrifuge tube and centrifuged at 4 °C, 15,000 rpm for 15 min . Next, the supernatant was diluted 1:10 with 1% formic acid in water, filtered with 0.2 µm PTFE filter, and placed in an HPLC vial for analysis. Five replicate juice samples were prepared and cooked at both temperatures.Composite juice samples were prepared by combing equal aliquots of the time points 0, 15, 30, 60, and 120 minutes of thermal processing for both temperatures for each juice prepared.

To extract CNGs for blue elderberry juice, 0.500 mL of juice was mixed with 2.00 mL of methanol in a 5 mL centrifuge tube, then sonicated at 30 °C for 30 min. After sonication, 1.00 mL of extract was transferred to a 1 mL microcentrifuge tube and centrifuged at 15,000 rpm at 4 °C for 15 min. The supernatant was collected and filtered through 0.22 µm PTFE into an HPLC vial and used for analysis. Five replicate extractions were made of raw elderberry juice and triplicate extractions were made for the other time/temperature juices.CNGs were analyzed via ultra-high performance liquid chromatography with electrospray ionization and triple quadrupole tandem mass spectrometry using an Agilent 1290 Infinity HPLC and 6460 mass spectrometer . The UHPLC was equipped with a binary pump with an integrated vacuum degasser , an autosampler with thermostat , and a thermostated column compartment . CNGs were separated using a Kinetex F5 column at 40.0 °C. The mobile phase consisted of a linear gradient of 1 mM ammonium formate in water and 1 mM ammonium formate in 650:50 MeOH:ACNe as follows: 5% B, 0–5 min; 100%B 5-5.50 min, 95% A 5.60-6.50 min. The flow rate was 0.400 mL/min, and the injection volume was 5.0 μL. The CNGs were analyzed using negative ESI mode. The drying gas temperature was 300 °C and the flow rate was 8.0 L min-1 . The sheath gas temperature and flow rate were 350 °C and 11.0 L min-1 , respectively. The nebulizer gas pressure and capillary voltage were 45 psi and 3.5 kV, respectively. The fragmentor voltage was 160 V for amygdalin and 100 V for sambunigrin. The dwell time was 100 ms for amygdalin and 200 ms for sambunigrin. The collision energy was set to 12 V for amygdalin, 0 V for sambunigrin. The multiple reaction monitoring mode was utilized to analyze amygdalin and sambunigrin. Quantification of CNGs was performed using external calibration curves using standard addition at levels of 500, 100, 50, and 5 ng L-1 . For amygdalin, the area of m/z 456.2 to m/z 323.1 was measured. For sambunigrin, the area of m/z 340.3 to m/z 294.2 was measured.Half-life values of phenolic compounds were calculated by plotting the natural log of C0/C ratio vs heating time t, where C0 is the initial concentration of a compound, C is the concentration of the same compound at time t in hours. The slope calculated from the figure is k .

Longitudinal Analysis of variance was performed with Tukey’s post-hoc test with p value at 0.05. Excel used for average and standard deviation and R Studio was used for ANOVA and post hoc analysis . CNG data was analyzed using MassHunter Quantitative Analysis to obtain peak areas for the targeted compounds. Microsoft Excel was used to create the calibration curves and determine CNG concentrations in samples .The concentrations of cyanogenic glycosides were quantified in raw and cooked blue elderberry juice for the first time. Results indicate that neoamygdalin , sambunigrin and prunasin are the primary CNGs in blue elderberry . Concentration of neoamygdalin were significantly higher than sambunigrin and prunasin . Neoamygdalin has been measured in raw bitters almonds in concentrations lower than amygdalin. In studies of American and European elderberry, sambunigrin is typically major CNG identified. Levels of total CNGs in blue elderberry are lower than American and European elderberry. European elderberry CNG levels range from 0.08 ± 0.01 to 0.77 ± 0.08 µg g-1 depending on the elevation and growing location. CNG levels in American elderberry juice range from 0.29 to 2.36 µg mL-1 . Differences between the subspecies may be due to genetic variation, impact of growing environment such as altitude, or methodology used to extract and analyze CNG content in the fruit and fruit juice, growing strawberries vertically including how berries were handled prior to juice and juicing method.The degradation of neoamygdalin > sambunigrin > prunasin was observed during cooking and the rate of degradation was faster at 95 °C as compared to 72 °C . However, degradation in juice at 72 °C was not linear, such that sambunigrin levels in juice cooked at significantly increased during the final timepoint measured . This may be attributed to neoamygdalin breaking down resulting in sambunigrin and a glucose molecule, an equivalent pathway to amygdalin degrading to prunasin and a glucose molecule. However, some of the resulting sambunigrin from that reaction would also have to be degrading since the decrease in concentration of neoamygdalindid not cause an equivalent increase in sambunigrin. An increase in sambunigrin at the end of the processing time was not observed in the juice cooked at 95 °C. In the juice processed at 95 °C, the combined concentration of neoamygdalin and sambunigrin decreased at each measured time point and did not increase at any time points like the juice processed at 72 °C. Prunasin levels did not significantly change in the elderberry juice cooked at 72 °C but prunasin did degrade significantly when processed at 95 °C . It appears that sambunigrin is more stable than prunasin in the elderberry juice; retaining about 70% of the original concentration in the juice heated to 95 °C. Neoamygdalin levels decreased significantly in the elderberry juice at both processing temperatures, with increased degradation at 95 °C as compared with the treatments at 72 °C. As previously mentioned, sambunigrin is the expected breakdown product from neoamygladin, but sambunigrin levels did not have concomitant increase due to thermal degradation of sambunigrin as well. Thermal processing has been seen to degrade CNGs in elderberry, flaxseed, and almond in previous studies. Furthermore, studies have seen enzymatic activity contributing to the breakdown of CNGs in nuts to reduce after exposure to heat. If the β-glucosidases for the CNGs present in blue elderberry are similar, they would also be inactivated during thermal processing at 72 and 95 °C, indicating thermal degradation is the main contribution to CNG levels decreasing in the present study. Because enzymatic degradation of CNGs was not measured during the thawing and juicing steps, the impact of the enzymes before the thermal processing cannot be evaluated here. The presence of neoamygdalin instead of amygdalin is unexpected. Amygdalin can convert to neoamygdalin with heat and in alkaline conditions. However, herein the raw elderberry juice hadsignificantly higher levels of neoamygdalin as compared to amygdalin. In a study of amygdalin content in almond varieties, amygdalin was found to convert to neoamygdalin during extraction , but the addition of acetic acid prevented the conversion. Blue elderberry naturally contain citric and malic acids, with an average titratable acidity of 0.60 ± 0.10 to 0.65 ± 0.07 g citric acid per 100 g FW. The average pH value of the juices in the present study was 3.76 ± 0.11. Therefore, there may not be enough acid in the matrix to prevent the conversion. In contrast, another study of amygdalin and derivatives in almonds found that heat of cooking caused neoamygdalin and amygdalin amide to convert to amygdalin, which was not observed in the present study. Further analysis of conversion of amygdalin to neoamygdalin in the blue elderberry could uncover why this epimer is dominant. The total levels of CNGs measured here are much lower than CNG concentrations found in European or American elderberry. In a study of European elderberries evaluated at various growing locations and altitudes found that sambunigrin levels range from 0.08 ± 0.01 to 0.77 ± 0.08 µg g-1 . A nearly 10-fold difference in concentrations between elderberry samples highlights the variation on CNG levels due to differences in growing conditions and environmental factors like sun exposure and temperature fluctuations. Furthermore, evidence of CNGs degrading with thermal processing has been evaluated in European elderberry products: when sambunigrin levels were measured in raw and cooked elderberry juice and other products, heating of elderberry juice reduced the level of sambunigrin, from 18.8 ± 4.3 mg kg-1 to 10.6 ± 0.7 mg kg-1 . Liqueur, tea, and spread also had significantly lower CNG concentrations as compared to the raw and cooked juice. American elderberry was evaluated for concentration of CNGs in the seeds, juice, skin, and stem of two genotypes: Ozone and Ozark. Elderberry juice was prepared by thawingpreviously frozen berries in a plastic bag and gently pressing to release juice. The juice of these elderberries contained amygdalin, dhurrin, prunasin/sambunigrin , and linamarin. Total concentrations of these four CNGs was 4.01 µg g-1 in Ozone and 3.66 µg g-1 in Ozark elderberries.

Unfortunately, that study did not include any growing information about the elder flowers or the concentration of the phenolic compounds in the extract, which would have helped other researchers replicate and expand on the results. While there have been promising studies on the impact of elderberry and elderflower extracts to combat illness and disease, more in vivo studies and clinical trials should be performed to better understand the mechanisms of the bioactivity as well as to determine which compounds are responsible for the bioactivity, particularly in the lesser-known subspecies canadensis andcerulea. This can better inform people involved with the cultivation of elderberry to select for varieties that have the compounds of interest.The market for herbal supplements has been growing in the recent decade and immune system-supporting supplements had a huge spike in sales during the COVID-19 pandemic. Elderberry products are a popular option of alternative medicine in hopes of improving and protecting health. Beverages are a popular use of the elderberry, including syrup or other tonics made by soaking the berries in water or alcohol. It can also be found as an ingredient in various kombuchas, juices, energy drinks, wine, and tea. Elderberry is typically mixed with a variety of other ingredients, including but not limited to ginger, honey, echinacea, and other spices. More recent products using elderberry include gummies typically marketed as health supplements, lozenges, tablets, and powdered berries especially as part of a drink mix. Elderberries are also frequently used in jams and jellies. Pomace, the byproduct of juicing, has been studied for its benefits when incorporated into other products just as baked goods. Beyond its potential for bio-active products to benefit consumers, tower garden elderberry can also be used as a natural food dye due to the high concentration of anthocyanins , which can be used in place of artificial red or purple food dyes, particularly in acidic foods.

Its application in edible films has recently been investigated, explored various bio-polymers that could retain the phenolic compounds of elderberry in the film so that they can remain active to protect foods. Active edible films can be an effective solution to reduce plastic packaging and food waste due to spoilage.Cosmetic and skin care applications are also an area of interest, 37 and current products on the market that include elderberry include lip color, toner, face mask, and Epsom salt.Future chapters will focus on evaluating the blue elderberry and elderflower for their composition. Herein, the data available on the other elderberry subspecies of interest are summarized to provide a basis of the expected composition as well as information to compare the subspecies for their composition. Elderberries have a high amount of water, at about 80%. The main sugars in elderberry are glucose and fructose, with some small amounts of sucrose. Sorbitol was also measured, which was very minor compared to the other three sugars and was seen in the highest concentrations in the wild elderberry. Citric acid is the main organic acid in elderberry, with malic acid the next highest acid. Small amounts of shikimic, tartaric, and fumaric acid have been measured in elderberry as well. Only data on European elderberry is available for microcon-stituents such as vitamins, minerals, fatty acids, and amino acids. Vitamins found in elderberry include various B vitamins, vitamin C, and vitamin E.The main minerals are magnesium, calcium, and potassium. Because studies of these micro-nutrients have only been performed on the European elderberry, it is important for further work to include other subspecies, including the American and blue elderberry so that better comparisons can be made.An important group of bio-active compounds found in fruit and vegetables is phenolic compounds, which consist of one or more phenolic groups .

Types of phenolic compounds include phenolic acids and flavonoids; flavonoids can be further separated into groups such as anthocyanins, flavonols, flavan-3-ols, and flavones. Phenolic compounds may have some biological activity, although bio-availability can be very low. A common, albeit imperfect, way to measure phenolic content of elderberries is using a colorimetric method like Folin-Ciocalteu which can measure a complex that forms between phenolic compounds and molybdenum-tungsten at 765 nm. Because this method measures all reducing agents in the matrix, reducing sugars and ascorbic acid will also react and increase the absorption thus inflating the total phenolic content . Standard curves are typically constructed using gallic acid, hence the units for TPC are gallic acid equivalents . TPC in European elderberries can vary greatly but reported values include 461 ± 121 49 and 683 ± 49. In American elderberry, TPC has been reported to be 390 ± 56 50, 593 ± 70. One study has included blue elderberry grown in Slovenia, which had a TPC of 416 ± 31. However, because of the imprecise nature of this assay, it is important to identify and measure the concentration of each phenolic compound present whenever possible, the results of which is explored in the following sections.Anthocyanins are water-soluble pigments in plants, and they give elderberries their blue purple hue. Total monomeric anthocyanin content is typically measured using the pH differential method, which takes advantage of the change in light absorption of anthocyanins insolutions with different pH and the unit is typically cyanidin glucoside equivalents . Analysis of the phenolic compounds via high performance liquid chromatography with UV-Visible light detection or with mass spectrometry have elucidated a variety of molecules present in the European elderberry. Anthocyanins, a type of flavonoid and popular for their red to blue pigments, are of high interest in elderberry.

Most studies have found that cyanidin -based anthocyanins are the dominant type in European and American elderberry, including cyn 3-O-sambubioside -β-Dglucopyranoside and cyn 3-O-glucoside. 1,8 Cyn 3-sambubioside-5-glucoside and cyn 3,5- diglucoside are also commonly seen in the elderberry. The American elderberry has a more unique anthocyanin profile with high presence of acylated anthocyanins compared to the European elderberry, including cyn 3-O-coumaroyl-sambubioside-5-O-glucoside , cyn 3-Ocoumoaryl-sambubioside. These acylated anthocyanins may be more stable during processing, but the authors found that cyn 3-O-coumaroyl-sambubioside was the least stable anthocyanin during storage , whereas cyn 3-O-cou-sam-5- O-glu and cyn-3-O-sam-5-O-glu were more stable. Another major type of phenolic compound in elderberry is flavonol glycosides, which include rutin , isorhamnetin 3-O-glucoside or 3-O-rutinoside, and kaempferol 3-O-rutinoside. Rutin has frequently seen to be the most concentrated flavonol in European elderberry, and often the most concentrated phenolic compound of any present. S. nigra ssp. canadensis also contains higher levels of rutin than other flavonols. Other flavonol glycosides present in elderberry include kaempferol and isorhamnetin derivates, such as kaempferol-rutinoside, isorhamnetin-rutinoside, and isorhamnetin-glucoside. Phenolic acids are also present in high amounts in elderberry, including chlorogenic acid isomers , p-coumaric acid, sinnapic acid, cinnamic acid, and ferulic acid. Flavan-3-ols found in elderberry include -catechin, -epicatechin, and procyanidins. Parts of the elderberry plant are known for having toxic compounds called cyanogenic glycosides that, if consumed, can be dangerous due to the release of cyanide. The stems and leaves have the highest concentration of CNGs, followed by unripe berries and flowers, followed by ripe berries and cooked juices. The primary CNG in elderberry is sambunigrin, stacking flower pot tower which is a diastereoisomer of the more commonly known CNG prunisin. Amygdalin is the next most common CNG, though it is not often measured. Dhurrin and linamarin have also been measured in elderberry plant material. European elderberry levels of CNGs can vary greatly depending on the growing location, such that concentrations ranged from 0.08 concentrations ranged from 0.08 ± 0.01 to 0.77 ± 0.08 µg g-1 when fruit was evaluated from various altitudes in Slovenia. 6 These concentrations are lowerthan those detected in elderberry juice, found to be 18.8 ± 4.3 mg kg-1 in raw juice and 10.6 ± 0.7 mg kg-1 in cooked elderberry juice, suggesting that thermal processing can reduce CNG levels in elderberry products. American elderberries have been evaluated for their concentrations of CNGs. These include amygdalin, sambunigrin , linamarin, and dhurrin. Specifically, the Ozone and Ozark genotypes were evaluated, giving better insight into how CNG concentrations may be impacted by plant genetics. While the total concentrations of the four CNGs in the two American elderberry genotypes were somewhat similar , the composition of which CNGs made up that total were quite different: Ozone elderberries had similar levels of amygdalin and sambunigrin while Ozark elderberries had much higher levels of amygdalin than sambunigrin .The flavor profile of elderberries is an important factor in the consumer sensory experience with elderberry products. Two of the most common compounds identified as drivers of elderberry aroma identified in multiple studies of the berries or elderberry juice are β-damascenone and dihydroedulan.

Nonanol was also identified as a key volatile compound contributing to the characteristic elderberry aroma, while ethyl-9-decenoate was found to be important for the characteristic elderberry aroma by another study. While these volatile compounds can be key to the unique aroma, they are not typically the most concentrated compounds. Studies have found the most concentrated compounds to be linalyl acetate, linalool, phenylacetaldehyde, benzaldehyde , hexanal, 2- and 3-methyl-1-butanol, nonanal and benzaldehyde. However, comparing concentration of compounds across studies can be difficult due to differences in sample preparation, extraction method, and method parameters, to name a few important factors. Neither American nor blue elderberry has been evaluated for their volatile aroma composition, which limits the understanding of how these subspecies may perform and be accepted by consumers in the same formats as European elderberry. Analytical assessments of the elderberries and products using the elderberries, in addition to sensory panels would be useful information for product developers and should be performed when cultivars or genotypes are being selected for cultivation and use in commercial products.Elder flowers are frequently used in beverages and food products, including but not limited to teas, syrups, lemonades, liqueurs, wines, jams/marmalades, and tonic water. They are also used for flavoring in yogurt, coated almonds, lozenges, and confectionary goods, to name a few. Furthermore, elderflower can now commonly be found in soaps, lotions, and candles, thus consumers, especially in the United States are becoming more familiar with elderflowers, which have been well-known in Europe for generations. Topical applications are also being explored for their benefits to skin. These recent studies support the long history of use of elderflower by the Lumbee tribe in North Carolina, who use elderflower as a treatment for skin cancer by soaking flowers in witch hazel for a week then applying that to the skin. The main compound in elderflowers, like elderberries, is water, and is found in similar concentrations .Glucose, fructose, and sucrose make up the main sugars found in elderflower. While European elderflowers have a roughly equal amount of these sugars, elderflowers of the blue elderberry have a much higher level of fructose than glucose or sucrose. However, there has only been one study to measure these compounds in elderflowers, and more studies are needed to know if this trend occurs across each of the subspecies. There is limited data on these compounds across the three subspecies of interest, such as no information on the American elderberry; thus, few comparisons can be made. Minerals and vitamins have been evaluated in European elderflowers. Minerals include calcium, magnesium, copper, zinc, and manganese. Calcium is the most concentrated mineral with an average of 2955.9 ± 272.7 µg g-1 across several wild and cultivated samples and magnesium is the next most concentrate mineral at an average of 1200.2 ± 453.6 µg g-1 . Vitamin C has only been measured in elderflower syrup, ranging from 22.47 ± 0.06 mg L-1 to 46.17 mg L-1 . Elderflowers of the European subspecies have been evaluated several times for their phenolic profile. Dominant compounds in the flavonol rutin and neochlorogenic acid. Concentrations can vary greatly, just like many of the other compounds already explored in this review. Significant differences in phenolic concentrations have been found between cultivars, such that the concentration of rutin ranged from 11.6 to 42.3 mg g-1 dry weight and neochlorogenic acid ranged from 10.1 to 20.7 mg g-1 dry weight among the 16 genotypes. The coefficient of variation was greater than 10% for all of the compounds measured, including nine phenolic acids and six flavonol glycosides. American elderflowers have also been studied for their concentration of rutin and chlorogenic acid which generally align with the European elderflower profile, except that the primary phenolic acid was chlorogenic acid instead of neochlorogenic acid.

In vineyard production systems, canopy management practices are usually employed to control the source-sink balance and improve the cluster microclimate leading to an improved grape composition and resultant wines . Additional canopy management practices may be applied during berry development. Fruit-zone leaf removal and especially, shoot thinning have been widely used in order to increase the cluster exposure to solar radiation, reduce crop load as well as decreasing the pest pressures , increasing flavonoid content and diminishing herbaceous aromas . Nevertheless, when high air temperature and excessive radiation combine, detrimental effects on berry acidity and flavonoid content have been reported in warm climate regions . Leaf removal consists of removing basal leaves around the clusters in the east or north side during grape development increasing the cluster exposure to solar radiation. It is well known that an early leaf removal increased total soluble solids, anthocyanins, and flavonols . However, some authors reported increases in titratable acidity in Sangiovese and Teran cultivars while other authors found decreases in acidity with basal leaf removal on Tempranillo . Conversely, Sivilotti et al. reported a positive effect of leaf removal applied after flowering on Merlot grapevine by improving cluster integrity by reducing incidence of Botrytis, and lower herbaceous aromas without affecting yield and cluster mass. Contrariwise, Pastore et al. reported that defoliation at veraison reduced the anthocyanin content and increased the impact of sunburn. In fact, these authors found that leaf removal induced a general delay in the transcriptional ripening program, dutch buckets for sale which was particularly apparent for structural and regulatory genes involved in the anthocyanin biosynthesis.

Clearly, vineyard location, cultivar , timing of leaf removal , method , and degree of leaf removal , the growing season , among others, are all factors influencing how leaf removal affects grapevine berry composition and integrity. On the other hand, shoot thinning has been related to increased cluster and berry mass and the number of berries per cluster, with a reduction on yield . Conversely, Wang et al. observed that shoot thinning had relatively minor impacts on yield components because of a compensatory effect due to the lower cluster number with concomitant increase in cluster mass. Contrarily, shoot thinning practices on grapevine did not show a great impact on berry primary metabolism , however, secondary metabolites were affected by them . In fact, we recently reported an increase of two-fold in the flavonol content of Merlot berries when leaf or shoot removal was applied mainly by increasing the proportion of quercetin and kaempferol derivatives in detriment of the myricetin derivatives . Berry composition is dependent on a complex balance between compounds derived from primary and secondary metabolism. Between secondary metabolites, flavonoids play an important role in the quality and the antioxidant properties of grapes and are very responsive to environmental factors such as solar exposure . Anthocyanin compounds are responsive of the berry color, and flavonols act as a UV shields, contribute to the wine antioxidant capacity, color stability, and hue through copigmentation with anthocyanins . On the other hand, the methoxypyrazines are wine key odorants contributing to their herbaceous characteristics and have been related to unripe berries and poor-quality wines when these are not part of the wine typicity . Since they can be present in grape berry and wines at high levels, they may have an important sensorial impact on wine quality . Among methoxypyrazines, the 3-isobutyl-2- methoxypyrazine is considered the most relevant to wine flavor due to its correlation with the intensity of the bell pepper character of wines and its content at harvest seems to be dependent of the solar exposure .

The differences found in the literature about the effect of manipulating the canopy architecture on the flavonoid and aromatic content due to different solar exposure of berries in warm climates opens an important field of research. Therefore, we aimed to find the optimal ranges of berry solar exposure estimated as percent of kaempferol for flavonoid synthesis up regulation and the thresholds for their degradation, and to evaluate how canopy management practices such as leaf removal, shoot thinning and a combination of both affect the grapevine yield components, berry composition, flavonoid profile, and herbaceous aromas.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 LR and ST decreased leaf area index and increased canopy porosity. 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, 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.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, hydroponic net pots 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 vineyard either. 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, 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.

Once the inner control loop was working, we implemented the heading controller and waypoint guidance algorithm. Rather than rely purely on the odometry model to determine the heading of the vehicle, the algorithm corrects the estimated heading using the attitude estimate from the AHRS filter after each odometry computation. Thus, the vehicle can orient itself even indoors enabling autonomous missions. The heading controller is a pure proportional controller, essentially always pointing the wheels in the direction of the next waypoint. This is an implementation of pursuit guidance, with multiple fixed way points. A simulated mission using five way points and a real mission with the same way points is shown in Fig. 7.16. The way points are the black diamonds in the plots. Initially, the vehicle is oriented south, that is, in the −y direction. Both the simulation and the data demonstrate that the vehicle is able to orient itself properly and head for each waypoint in turn. When the vehicle is within a certain distance of the current waypoint, in this case set to 1 meter, the algorithm loads the next waypoint. The mission ends when there are no more way points. Fig. 7.17 is the same experimental mission, but in this case the vehicle is oriented in different directions at the start. This figure demonstrates the robust nature of the attitude estimation to orient the vehicle. For this experiment the vehicle performed a simple two waypoint mission, after which we manually drove it back to the start position. The total mission length was approximately 8 meters and repeated 10 times. we define the mission error to be the difference between the expected distance from the final waypoint and the actual distance at the end of the mission.

The mean rms error and standard deviation after the missions was 0.48±0.13 meters. If the odometry errors were normally distributed then we could expect the variance to be proportional to the length of the mission. Of course, bato bucket the errors are not normally distributed due to the non-linearity of the odometry model, however, the variance nevertheless increases with longer missions. This highlights the importance of having an absolute reference to position, e.g., the GPS sensor. For missions in the absence of GPS information, the vehicle must map its location relative to the local landmarks using a localization algorithm such as SLAM. The ASV is a catamaran vessel with twin BLDC motors and rudders for propulsion and steering. Each motor is controlled independently with an ESC by an OC module. The rudders are connected to a single servo that is controlled by an additional OC module. The boat uses the Max32 development board and the OSAVC I/O rev 1.2 daughter board instead of the integrated OSAVC board. The ASV employs three of the sensors for navigation: GPS, IMU, and a rotary encoder. These all use the sensor drivers from the OSAVC code repository. The encoder is used to measure the servo angle of the rudders, the GPS provides absolute position and velocity, and the IMU provides attitude. The boat uses an AHRS algorithm similar to the one used to benchmark the OSAVC but implemented by the developer of the boat and placed in the OSAVC repository as a contributor. Like the AGV, the ASV uses the Raspberry Pi4b SBC for its guidance computer and communicates via USB using the MAVLink communication protocol. In addition to the common hardware and firmware, the developer of the ASV also implemented some custom additions. The main one to mention here is aEMO hardware switch that disables the motors in case of emergency. Additionally, the he uses a sensor not in the repository—a sonic depth gauge used to map the ocean or lake floor.

This sensor connects to the SBC directly. The block diagram of the ASV is in Fig. 8.2.To make the ordinary kriging method more computationally tractable, we introduced a method known as partitioned ordinary kriging. Fig. 8.3 shows the theoretical results of this method against the true field and two other estimates. This method reduces the overall complexity of a field by subdividing it into smaller partitions and only updating the field estimate within the partition. We deployed it to the SBC to demonstrate feasible use in the field. Vlastos introduced an optimal search method using the variance of the field estimate and implemented it on the ASV in his PhD disssertation. More information regarding the ASV and these algorithms can be found there as well. The next vehicle using the OSAVC architecture is a quadcopter. This vehicle is designed to localize itself in environments where GPS is either unavailable or intermittent. The research goal is to demonstrate a method to identify features in the landscape using the TPU and a monocular camera from a pretrained model. The vehicle has a map where these landmarks are geo-referenced to GPS. The source of the map can be taken from existing imagery or mapped and geo-referenced prior to the mission. The vehicle locates itself in the environment by comparing its current pose relative to two or more landmarks that it identfies in flight. Fig. 8.4 is an image of the vehicle during its hardware development. The UAV is designed entirely out of foamcore so that it can be fabricated on a laser cutter and glued together in a matter of hours. The airframe is equipped with a front-mounted BLDC motor and propeller powered by a three cell LiPo battery and controlled by a 25 A ESC. It has ailerons, a rudder, and elevators for control surfaces. They are all controlled by small servomotors. It is currrently equipped with only a GPS sensor, commercial flight controller , and an RC receiver. In this configuration it is not possible to develop new flight control algorithms for two reasons. The first is that without access to the IMU data, attitude estimation—a mandatory component for the experimental flight controller—is impossible.

The second is that it is not possible to import novel flight control algorithms into the commercial flight controller. Indeed, this is the main purpose of the OSAVC. These areas will be addressed in the next phase of development where the vehicle will be equipped with the OSAVC, IMU, and serial radio. It optionally may include an airspeed sensor. Unlike the other vehicles under development, this vehicle does not currently plan to use the full distributed control architecture. The hardware block diagram of the UAV in the final state is in Fig. 8.6. Yet another project underway is to use GPS data to identify AGV parameters that inform its kinematic and dynamic model. The main goal of this project is to use the GPS data to refine and calibrate parameters of the vehicle that are difficult to measure directly, in particular, the effective tire radius, the relationship between the measured servo angle and the vehicle angular velocity, and the static coefficient of friction between the tires and a given road surface. The effective tire radius is used to determine the vehicle speed accurately in the odometry model. The GPS provides an independent estimate of the vehicle velocity. The ratio of GPS velocity and rear wheel angular velocity is the effective tire radius. By itself this parameter calibrates the odometry model for velocity. Once determined, it helps identify the transfer function between the steering servo angle and the vehicle angular velocity using least-squares regression and GPS position data. We can use the calibrated odometry models to determine the lateral and longitudinal dynamics of the tires. Independent position and velocity measurements compared against the odometry estimates provide a convenient mechanism to determine wheel slip. Finally, slip detections keeps the AGV in the non-slip regime and is used to identify the road-tire interface parameters in-situ, that is, dutch bucket hydroponic during mission operation. This work will be published the 2023 ION/PLANS conference proceedings.A rich area of research using the AGV platform and combining Sections 9.3 and 9.4 is to develop an autonomous race car. In this project the mapping sensor is used to determine the inner and outer race track boundaries and the road-tire interface parameters determine the friction limits of the tires. The goal of the research would be to provide optimal guidance through a given course once its boundaries are autonomously mapped and the road-tire friction parameters determined. Unlike many academic research projects, this work has often been collaborative with students from ASL as well as interns from the Google Summer of Code program. It is our belief that individuals working together will always exceed the accomplishments of those same individuals working separately. This is one of the main reasons we chose to make this project open source–to promote collaborative development. It was exceedingly gratifying, therefore, to see so much interest in the project from all over the world as well as here at home. Perhaps the greatest potential contribution of this research is to provide a control platform to enable future autonomous vehicle research and a community to collaborate with. Our hope is that by developing the OSAVC and integrating it into a distributed control framework, vehicle developers can take advantage of the power of embedded programming. We also hope that by following the example code they will be able modify it for their own purposes, tremendously shortening the learning curve. Embedded programming in C can be a daunting prospect to the programmer unfamiliar with the process. Paradoxically, good embedded programming practices make understanding and troubleshooting real-time systems easier. This is because every aspect of the program is dictated by the programmer—there are no hidden mechanisms behind the scenes.

Therefore, while hardware abstraction does allow for relatively easy coding of complex tasks, it hides important aspects of what is happening at the hardware level. While this may not matter for many applications, for real-time control it is critical to understand the operations of the microcontroller and the hardware peripherals to ensure predictable latency and efficient code. Also, understanding the low-level hardware allows for easier troubleshooting of faulty code. In obese male C57BL/6J mice consuming a high-fat diet, daily supplementation freeze-dried mango at either 1% or 10% of the weight of the diet significantly reduced body fat compared to those consuming a non-supplemented control diet. Curiously, only the 1% mango group showed significantly decreased fasting blood glucose and postprandial blood glucose responses after tolerance tests, but no difference was noted for insulin or HOMA-IR, compared to those consuming the 10% supplementation or control diets. In overweight and obese humans, plasma insulin was significantly increased 45 min after consuming 100 kcal of mango , compared to their baseline levels, but did not increase as much as when the participants consumed an to isocaloric low-fat cookie. The same study also noted that capillary blood glucose levels were significantly elevated 30 min after mango intake compared to their baseline values, while returning to the baseline range at 60, 90, or 120 min after intake, whereas intake of the low-fat cookie showed significantly increased blood glucose at both 30 and 60 min, which is consistent with trends from our study. However, the above study measured insulin at baseline and 45 min after food intake, so the postprandial insulin levels cannot be compared directly with our study. Future research may consider assessing the association between postprandial BP, glucose, and insulin resistance at multiple time points. This study has several limitations. The Ataulfo mangos were not analyzed for nutrients or phenolic contents. Different mango cultivars vary in macro-nutrients, micro-nutrients, as well as phytonutrient content. Among commonly consumed mango cultivars, Ataufo mango pulp contains the highest concentration of β-carotene, ascorbic acid, total phenolics, gallotannins, and mangiferin, in comparison to Haden, Keitt, Kent, and Tommy Atkins. The high concentrations were used in the selection of Ataulfo. The amount of white bread as an isocaloric control was calculated based on the USDA food database, which does not identify the cultivar or cultivars that were tested. Finally, the postprandial blood glucose and insulin responses in study II were not measured at 30 min, which may have missed the possible peak levels. Future studies may take the measurements at more frequent time points, as well as insulin resistance indicators, such as HOMA-IR, to better understand the role of mango in blood glucose management. In conclusion, two weeks of daily mango intake was associated with a decrease in SBP and PP.

EEG is an electrophysiological signal that reflects unique cognitive and neurological information of the person; thus, EEG possesses the potential to be used for robust bio-metrics. EEG provides a high level of security since it is very difficult to reproduce the EEG patterns of a specific person. In addition, in the event that the person is subjected to a forced EEG reading against his/her will, the EEG biometric system may detect the person’s stress and deny access. Deep learning-based models have achieved state-of-the-art results in a wide range of clinical applications and biometric identification problems, ranging from phone authentication to bank security systems. The use of DL models for biometric identification has been increasing in recent years. Within the field of EEG bio-metrics, DL models have been leveraged to improve the accuracy of EEG biometric identification systems. Most Machine Learning projects are performed on ordinary computers or Graphics Processing Units because of the computing power they offer. However, since the purpose of this project is to develop a portable Internet of Things system, it is not feasible to use common computers. In this work, we use Raspberry Pi as the core, which is compact, portable, and Wi-Fi capable. Raspberry Pi is widely used for portable IoT applications because of its versatility. The Raspberry Pi has been used in a number of projects for a variety of purposes, including monitoring the health status of patients, a security system, and a testing system device to name a few. The Raspberry Pi has also been used for projects based on EEG signals. For example, in [12], EEG is used to control a car, and, in [13], gutter berries it is used to monitor the depth of anesthesia. The purpose of this work is to develop a first approximation to a fully functional portable system for subject identification that uses trained DL models to process the signals.

This paper is organized as follows: The data description is presented in Section 2. In Section 3, we describe the software implementation for the subject identification using EEG signals. Section 4 shows the hardware implementation of the system. Section 5 reports the results. Finally, the discussion and conclusion will be drawn in Section 6. The BED is a dataset specifically designed to test EEGbased biometric approaches that use relatively inexpensive consumer-grade devices. The dataset includes EEG responses from 21 subjects to 12 different stimuli, which were broadly divided into four different types, namely affective stimuli, cognitive stimuli, visual evoked potentials, and resting-state. Each stimulus contains data across three different chronologically disjointed sessions. Fourteen-channel EEG signals containing the channels—AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, and AF4 as shown in Figure 1—were collected at a sampling rate of 256 Hz. The BED dataset includes the raw EEG recordings with no preprocessing and the log files of the experimental procedure, in text format. The EEG recordings were segmented, structured, and annotated according to the presented stimuli, in Matlab format. The BED dataset also includes Autoregression Reflection Coefficients , Mel-Frequency Cepstral Coefficients , and Spectral features that were extracted from each EEG segment. In this work, however, only raw EEG recordings without any manually extracted features were used. Out of the 12 different stimuli, we only used EEG signals recorded during the Rest-Closed Stimulus since the action of “closing your eyes” is an easy and natural action and could be best replicated in a realworld scenario for biometric applications with ease. Having stimuli in the experimental procedure will require additional devices to present them to the individuals, which in turn will increase the complexity of the whole setup and practical use in the real world. The BED dataset was simulated using our real-time Raspberry Pi-based system as a proof of concept to test the feasibility of this work for any real independent practical use. The simulated data acquisition in our Raspberry Pi-based system is performed by an analog-to-digital converter that reads the analog output from the digital-to-analog converter that receives the input from the original saved BED dataset. More details will be discussed in Section 4.

The preprocessing step of EEG signals is a crucial step in the DL pipeline because of its impact on the EEG analysis process. Without the preprocessing step, there may be noisy data and artifacts that can mask distinct features in the EEG signals. This can cause the model to have a harder time distinguishing between relevant EEG features, resulting in poorer performance of the model. In addition, we must pay attention to the quality of the preprocessing step as it can introduce unwanted artifacts if the early stages of the pipeline are not properly addressed. For example, although ordinary average referencing improves the signal-to-noise ratio, noisy channels which depend on the reference can contaminate the results. Figure 2 shows the different steps of EEG signal preprocessing. The well-known preprocessing technique—PREP pipeline —introduces specific important functionality for referencing the data, removing line noise, and detecting bad channels in order to deal with noisy channel-reference interactions. The PREP pipeline also removes artifacts such as muscle movement, jaw clenching, and eye blinking. The pipeline consists of various steps. First, the signal is filtered using a 1 Hz high pass filter followed by line noise removal using a notch filter at 60 Hz. In addition, finally, the signal is robustly referenced with respect to an estimate of the true mean reference, thereby enabling the detection of faulty channels. These channels are then interpolated relative to the same reference. We then use a low pass filter of 50 Hz and divide the EEG data into overlapping epochs with an overlap rate of 90 percent. Finally, we standardize the EEG signals for each channel using StandardScaler. Figure 3 depicts an EEG epoch before and after preprocessing.The deep learning models used for subject identification are based on ResNet, an extension of the neural network into internal structures that add direct connections to the internal residual blocks to allow the gradient to flow directly through the lower layers; Inception, a convolutional neural network architecture that executes multiple operations with multiple filter sizes in parallel to avoid facing any compensation and allows the network to automatically extract relevant features from the time series; and EEGNet, a compact convolutional neural network that has been designed to build an EEG-specific model as it includes concepts and tools specific to EEG signals such as feature extraction and optimal spatial filtering to reduce the number of trainable parameters. The utilization of these three DL models was based on the fact that they are state-of-the-art models that have achieved good results for various other applications. For example, ResNet and InceptionTime were designed for Time Series Classification. EEGNet is a DL model that was designed for EEG-based Brain-Computer Interfaces.

As a result, strawberry gutter system we wanted to examine the use of these models for the EEG Biometrics application. Modifications for ResNet include adding additional residual blocks, to understand whether a more complex model that could extract complex features would perform better. For the Inception model, additional dropout layers and inception blocks were added. The activation function was changed from Rectified Linear Unit to Exponential Linear Unit as the model was overfitting. For the EEGNet model, we fine-tuned the length of temporal convolution in the first layer and the number of channels after trial and error. Other modifications, such as adding GlobalAveragePooling2D layer, varying the dropout rate from 40 percent to 60 percent, and rearranging the order of layers, did not significantly improve the model performance. In addition, within all our models, we added a callback function that reduces the learning rate based on the training loss. Specifically, we added the hyperparameter called patience, which is the number of epochs of non-decreasing loss values that the model runs before reducing the learning rate by half. Because we want to check the feasibility of EEG-based Biometric identification over long periods of time, these presented models were trained using the first two weeks of the three chronologically disjoint sessions in the BED dataset. The third week of data was used for testing the trained model. Once the models were trained, we had their hyperparameters, including learning rate, batch size, number of filters, kernel size, and number of epochs fine-tuned. The hyperparameter learning rate was set to 0.003 for the ResNet model and 0.009 for the Inception and EEGNet models. In addition, the number of epochs has been modified, 150 for Inception and 400 for ResNet. Figures 4–6 briefly show the overall architectures of our modified Resnet-based, Inception-based, and EEGNet-based framework used in this work. The model was evaluated using the third session of the three chronologically disjoint sessions provided in the dataset. The trained model would take one EEG segment from the testing dataset at a time, and output the prediction of which person it belongs to. Once all the EEG segments in the testing dataset have been predicted, we would use the confusion matrix to evaluate the model performance. The evaluation of the machine learning algorithms was carried out by comparing the most relevant indices for the prediction of the subjects. The accuracy of the models calculates the ratio of correct predictions over the total number of instances evaluated. Precision is used to measure the positive patterns that are correctly predicted from the total predicted patterns in a positive class. Recall is used to measure the fraction of positive patterns that are correctly classified. F1 Score is calculated to represent the harmonic mean between recall and precision values. Finally,the Precison vs. Recall curves are obtained to provide a graphical representation of the DL models’ performance. In this experimental work, data acquisition is simulated by using data from an existing database as mentioned in Section 2. Real-time EEG acquisition is in the scope of the future where we intend to implement the best deep learning model obtained from this study along with real-time signal acquisition for biometric application. Keeping in mind our goals for future work and existing time constraints, we decided to simulate analog EEG input in this work as mentioned later in this section. The system consists of a Digital-to-Analog Converter in charge of converting the stored data to analog signals mimicking real EEG acquisition scenarios. The DAC is the 12-bit MCP4725 chip with an Inter-Integrated Circuit communication bus. Since the converter is a 12-bit converter, the range of digital values that can be converted is from 0 to 4095. Therefore, before converting the EEG signals stored in the memory of the Raspberry Pi to analog signals, the data were transformed by scaling each value to the range from 0 to 4095. The design includes an electronic loop with an Analog-to-Digital Converter that transforms the analog EEG signal from the subjects—in this case, the DAC, to a digital signal for its processing by machine learning algorithms. The ADC is the 10-bit MCP3008 chip with Serial Peripheral Interfac. The analog data are converted to digital signals by 10-bit architecture, so the digital data range is from 0 to 1023. As a result, we simulated the acquisition of EEG signals by means of an electronic loop between an ADC and a DAC using the data from the BED dataset. Figure 7 illustrates the complete hardware of the system and its connections.The acquired signals are processed by the system controller, which is based on the Raspberry Pi 4 Model B 4 GB RAM, which performs the tasks of capturing the EEG signals, pre-processing them using a pipeline, and processing them through machine learning algorithms for the identification of the subject. The Raspberry Pi is a single-board computer based on Linux . The programming language used is Python as it is the most suitable for machine learning applications owing to a large number of available libraries. Task management is carried out by threads that run in parallel. The first thread consists of constant EEG data acquisition by the ADC, which is stored in a buffer for their processing, and the second thread is in charge of the pre-processing technique and the classification in real time of the samples stored in the buffer. Therefore, the result of the subject identification can be obtained in other edge devices, such as a PC or a server. Here, the result is displayed on the terminal indicating the identification of the subject.

The valley Hall effect has been understood in terms of the Berry curvature ; the symmetries in 1 ML 2H-MX2 cause a sign change in the Berry curvature as one goes from one valley to an inequivalent valley in the BZ . This allows us to understand the valley Hall effect in terms of pseudospins, and provides possibilities to control the pseudo-spins by an external field. On the other hand, the Berry curvature is expected to vanish in the bulk because the bulk TMDCs have an inversion symmetry. However, one can imagine that the valley Hall in each layer could be nonvanishing—only the sum vanishes. This may naturally introduce the concept of “hidden Berry curvature,” a nonvanishing Berry curvature localized in each layer. An analogous case can be found in the case of the hidden spin polarization proposed and measured recently . Existence of hidden Berry curvature implies that the topology could be determined by local field; the local symmetry determines the physics . While experimental verification of a hidden Berry phase in the Bloch state is highly desired, standard measurements such as quantum oscillation cannot reveal a hidden Berry phase because these measurements represent an averaged quantity, with hidden quantity invisible. However, if we use an external field or surface sensitive technique such as angle resolved photo emission, then the direct measurement of such a hidden Berry curvature may be possible. In fact, the surface sensitivity of ARPES has recently been utilized in the measurement of hidden spin polarization. Then, the question is if Berry curvature can be measured by means of ARPES. In this regard, we note a recent proposal, based on a tight-binding model calculation on a simple cubic lattice with s and p orbitals, dutch buckets that the nonAbelian Berry curvature is approximately proportional to the local orbital angular momentum in the Bloch state.

We use a similar approach and derived the relationship between OAM and the Berry curvature by using a three band, tight-binding model for in WSe2. We find that there is a linear relationship between OAM and the Berry curvature . Even though circular dichroism ARPES is not a direct measure of the OAM in the initial state in general, it has been shown that CD-ARPES bears information on the OAM. This fact can provide us a way to observe the existence of hidden Berry curvature by using CD-ARPES. In actual measurements, an important challenge lies in the fact that CD-ARPES has contributions other than the one from OAM. The most notable contribution comes from the geometrical effect, which is caused by a mirror symmetry breaking in the experimental geometry. Therefore, how we separate the Berry curvature and geometrical contributions holds the key to successful observation of the hidden Berry curvature. We exploit the unique valley configurations of TMDCs in the BZ to successfully disentangle the two contributions. Moreover, we find the hidden Berry curvature exists over a wide range in the BZ. These features are consistently explained within the first principles calculations and tight binding description. ARPES measurements were performed at the beam line 4.0.3 of the Advanced Light Source at the Lawrence Berkeley National Laboratory. Data were taken with left and right-circularly polarized 94 eV light, with the circular polarization of the light better than 80%. The energy resolution was better than 20 meV with a momentum resolution of 0.004 Å−1 . Bulk 2H-WSe2 single crystals were purchased from HQ graphene and were cleaved in situ at 100 K in a vacuum better than 5 × 10−11 Torr. All the data were taken at 100 K. Figure 1 shows the crystal structure of 2H-WSe2 for which the inversion symmetry is broken for a ML. In the bulk form of 2H-WSe2, the layers are stacked in a way that inversion symmetry is recovered.

In the actual experiment, the contribution from the top layer to the ARPES signal is more than that from the sublayer, as illustrated by the dimmed color of the sublayer. Figure 1 schematically sketches the electronic structure with the hexagonal BZ of WSe2. The low energy electronic structures of 2H-WSe2 ML was found to be described by the massive Dirac-Fermion model, with hole bands at K and K0 points. The bands are then spin split due to the coupling between the spin and OAM. In the bulk, layers are stacked in a way that K of a layer is at the same momentum position as the K0 of next layer. Consequently, spin and valley symmetries are restored due to the recovered inversion symmetry and any valley sensitive signal should vanish. On the other hand, the in-plane nature of the primary orbital character of the Bloch states around the K and K0 points and the graphenelike phase cancellation as well as the strong spin orbital coupling strongly suppress the interlayer hopping along the c axis and make them quasi-two dimensional. In that case, the valley physics as well as the spin-split nature maybe retained within each layer as illustrated in Fig. 1 by the top- and sublayer spin-split bands . In that case, one may be able to measure the hidden Berry curvature by using ARPES because it preferentially probes the top layer due to its surface sensitivity as, once again, illustrated by the dimmed color of the sublayer. Since the signal is preferentially from the top layer, the situation becomes as if ARPES data are taken from the topmost layer of WSe2, for which the inversion symmetry is broken. As mentioned earlier, it was argued that OAM is directly related to the Berry curvature, which indeed has opposite signs at the K and K0 points as OAM does. Then, the hidden Berry curvature may be measured by using CDARPES, which was shown to be sensitive to OAM. However, CD-ARPES has aforementioned geometrical contribution due to the broken mirror symmetry in the experimental geometry. In order to resolve the issue, we exploit the unique character of the electronic structures of TMDCs. The key idea is that, while the contribution from the geometrical effect is an odd function of k about the mirror plane, we can make the OAM contribution an even function. In that case, the two contributions can be easily isolated from each other.

To make the OAM contribution an even function, we use the experimental geometry illustrated in Fig. 1. The experimental mirror plane, grow bucket which is normal to the sample surface and contains the incident light wave vector, is precisely aligned to cross both K and K0 points. In such experimental condition, the Berry curvature is mirror symmetric about the experimental mirror plane and so is its contribution to the CD-ARPES. Then, the CD-ARPES is taken along the momentum perpendicular to the mirror plane , i.e., from K to K and K0 to K0 as shown in Fig. 1 by green dash-dot and brown dashed lines, respectively. We point out that we kept the same light incident angle for K-K and K0 -K0 cuts [note the color pair for the cut and light incidence in Fig. 1] to prevent any contribution other than those from Berry curvature and experimental chirality. Figures 1–1 show data along the K0 -K0 cut. The dispersion is very symmetric with the minimum binding energy at the K0 point as expected. However, the intensity varies rather peculiarly; there appears to be no symmetry in the CD intensity in Fig. 1. The K-K cut in Figs. 1–1 shows a similar behavior. While the dispersion is symmetric , the CD intensity in Fig. 1 at a glance does not seem to show a symmetric behavior. However, upon a close look of the CD data in Figs. 1 and 1, one finds that the two are remarkably similar; the two are almost exact mirror images of each other if the colors are swapped in one of the images. This is already an indication that the CD data reflect certain aspects of the electronic structure that are opposite at the K and K0 points, most likely the hidden Berry curvature of bulk 2H-WSe2.There are several aspects to be noticed from the figure. First of all, the symmetric part IS NCD for K-K and K0 -K0 cuts have almost identical behavior except their signs are reversed as seen in Fig. 2. As we will show later, this sign changes occurs precisely on the entire Γ-M line. In addition, we find that IS NCD is almost the same for the two spin-split bands . These observations on the behavior of IS NCD are consistent with what we expect from the Berry curvature; it is valley dependent but independent of the spin-split bands. On the other hand, the antisymmetric parts IA NCD from K-K and K0 -K0 cuts shown in Fig. 2 stay very similar to each other over the whole momentum range. The results indicate careful execution of our experiments and trustworthiness of our analysis. To study the behavior of IS NCD better, we expand the range from a cut to a map of IS NCD that covers the BZ. IS NCD of the upper spin-split band is obtained from the CD-ARPES data and plotted in Fig. 3. In addition, in order to understand the connection between IS NCD and Berry curvature as well as OAM, we performed tight binding analysis and first principles calculation for a ML WSe2. For the Berry curvature calculation, we consider the tight binding Hamiltonian based on the hybridization between a W d orbital and a Se p orbital . In the calculation, the parameters are adjusted until the dispersion fits the experimental one and previous TB result . Then, the Berry curvature of the upper band is calculated based on the TKNN formula and its map is plotted in Fig. 3. The momentum dependent local OAM is obtained by density functional theory calculation. The resulting Lz map is depicted in Fig. 3. The in-plane components of the Berry curvature and OAM are also calculated but are found to be negligible over the whole BZ and thus are not presented. One can immediately note that the three plots of experimentally obtained IS NCD, Berry curvature from TB analysis, and local Lz from DFT calculation show remarkably similar behavior; their signs are determined by the valley indices and change only across the Γ − M line. In addition, all of them retain significant values quite far away from the K and K0 points. Our observation shows that IS NCD can be considered as a measure of the OAM and Berry curvature. We also find that IS taken with different photon energies shows no qualitative difference . These observations support the notion that IS reflects an intrinsic property of the state, that is, OAM. For a more quantitative comparison, we plot IS NCD, Berry curvature and OAM along the high symmetry lines . Once again, IS NCD, Berry curvature and OAM show very similar behavior. As the Bloch states at the Γ and M points possess inversion symmetry, IS NCD, and Berry curvature as well as OAM are all zero. One particular aspect worth noting is their behavior near the Γ point. They are approximately zero near the Γ point but suddenly increase about a third of the way to the K or K0 point. Orbital projected band structure from TB calculation shows that this is when the orbital character of the wave function switches from out-of-plane dz2 and pz orbitals to in-plane dxy, dx2−y2 , px, and py orbitals. This behavior can be understood from the fact that the local OAM is formed by in-plane orbitals. These results strongly suggest that IS NCD is indeed representative of the Berry curvature and that the Berry curvature is closely related to the local OAM, at least for TMDCs. Characteristics of electron wave functions in the momentum space often play very important roles in macroscopic properties of solids. For example, topological nature of an insulator is determined by the characteristics of electron wave function at high symmetric points in the momentum space. The Berry curvature which is also embedded in the nature of the electron wave function in the momentum space determines the Berry phase and thus macroscopic properties such as spin and valley Hall effects.

The insecticide material and application costs are estimated to be $825.33 per hectare. However, these chemical applications may also provide incidental control of other pests. This implies that the estimate represents an upper bound of the potential chemical control costs associated with SWD. The revenue loss and management cost estimates in these prior studies can be substantially improved using current information about SWD-induced yield losses and management practices. Fruit losses due to SWD and SWD management costs have decreased over time as researchers and producers have developed and implemented better techniques for reducing crop losses. We can also more accurately estimate historic yield losses now that more is known about SWD biology, its spread, and the efficacy of different management techniques. Lastly, we can now incorporate increases in labor costs into these SWD management cost estimates. This analysis has two components. First, we utilize recent estimates of SWD-induced yield losses in the California raspberry industry to calculate industry-level revenue losses for both organic and conventional raspberry producers. Second, we revise prior estimates of SWD management costs to reflect the cost of modern organic and conventional chemical management programs and the increased labor costs resulting from the presence of SWD.Prior estimates of SWD-induced revenue losses were based on the maximum observed yield losses in different industries where SWD infestations occurred. These estimates provide information about SWD’s damage potential, hydroponic gutter but do not yield an accurate estimate of actual SWD crop damage.

Actual crop damage is useful for estimating revenue losses due to SWD and will differ by year and production style. This analysis incorporates field trial results and expert opinions to estimate SWD-induced revenue losses for the California raspberry industry. SWD infestations directly reduce raspberry yields in two ways. First, fruit infested by SWD decay more quickly. These yield losses are difficult to attribute to SWD because the initial infestation is difficult to detect, and the accelerated decay has a similar appearance to decay caused by fungal diseases, bacteria, and yeasts. Second, raspberry shippers that detect SWD infestations may reject the entire delivery from the grower. Fresh fruit are held to rigorous quality standards. The risk of rejection of an entire delivery incentivizes growers to eliminate all visible defects in harvested fruit. SWD infestations are more prevalent late in the year as the population grows until winter weather reduces the population. Further, raspberry production is fairly concentrated geographically and the leftover, overripe fruit from nearby fields’ summer harvest acts as a breeding ground for SWD. SWD infestations are also more prevalent in fruit destined for the processing market, where the price is lower than in the fresh market. Fruit intended for processing are harvested later in the season, tend to be riper because they are harvested less frequently, and receive less frequent pesticide treatments. SWD damage rates could change significantly in the future due to pesticide resistance development and the introduction of new SWD management practices, including introducing biological control agents. Recent studies in the US and Europe found that indigenous parasitoids had limited effect on SWD populations. However, in Asia, where SWD originates, several endemic parasitoids attack and develop from SWD. We begin by examining SWD-induced yield losses in California’s conventional raspberry industry. The original reports of SWD damage in the raspberry industry indicated that as much as 50% of production could be lost if SWD was left unmanaged. 

Yield losses of this magnitude occurred as raspberry producers first learned how to manage SWD, but are now uncommon due to implementation of extensive academic research and industry experience. According to private communications with conventional raspberry producers, they have managed to reduce SWD induced yield losses to less than 3% of production. In recently published reports, conventional raspberry producers that employ effective chemical management programs face virtually no yield losses due to SWD. This substantial reduction in yield losses is primarily attributable to two factors. First, conventional raspberry producers have access to cheap and effective chemical management options. Second, these producers are harvesting their crop more frequently in order to reduce the amount of time raspberries are susceptible to infestation. These observations of actual SWD-induced yield losses are consistent with field trial observations as well. Entomologists Kelly Hamby and Frank Zalom monitored traps and evaluated fruit samples for damage between October 2010 and December 2012 in both organically- and conventionally-managed raspberry sites. Analyzing the 40-fruit samples collected from these fields resulted in estimated yield loss observations for raspberry producers employing standard management practices at the time. SWD-induced yield losses for conventional producers in the study were estimated to be approximately 10% of production in 2011 and less than 1% in 2012. These estimated yield losses are consistent with those observed by De Ros et al. in Italy between 2011 and 2013. De Ros et al. estimated raspberry losses of 11.5% prior to i and 3.24% after the implementation of an integrated strategy. The yield losses observed in the UC Davis study were concentrated in the fall harvest. The summer harvest is hypothesized to experience less SWD pressure because the population grows throughout the year until cold weather arrives and lack of host fruit in the winter significantly reduces population levels. SWD biology and infestation intensity is affected by climatic conditions and the availability of host fruit, implying that different climatic conditions and influences of neighboring crops could significantly impact SWD-related yield losses. On the other hand, organic raspberry producers still face significant SWD-induced yield losses.

Private communications with raspberry producers indicate that these producers experience yield losses between 5% and 15% of production due to a lack of efficacious chemical treatments approved for organic use, and the efficacy and high cost of other labor-intensive SWD management practices. Once again, these field observations are consistent with the yield losses measured in field trials. SWD-induced yield losses for organic raspberry producers in the study were estimated to be approximately 12% of production in both 2011 and 2012. We calculate yearly estimates of industry-level revenue losses using these observed yield losses due to SWD and a procedure similar to Goodhue et al. . First, we assume an ownprice elasticity of demand for raspberries of -1.66. This elasticity value is the value estimated for fresh raspberries by Sobekova, Thomsen, and Ahrendsen . Second, we assume that actual yield losses in the California raspberry industry correspond to the yield losses observed in the field trials. Specifically, we assume that SWD-induced yield losses between 2009 and 2011 correspond to the yield losses observed in 2011, and that losses after 2011 correspond to the yield losses observed in 2012. Raspberry production and price data are obtained from the U.S. Census of Agriculture and various National Agricultural Statistics Service surveys. Table 2 provides the resulting revenue loss estimates organized by production practice and year grouping. California’s conventional raspberry producers faced a total of $36.1 million in revenue losses due to SWD between 2009 and 2011. These estimated revenue losses are equivalent to 4.62% of realized revenues over the same period. After 2011, effective SWD management techniques in conventional production eliminated virtually all revenue losses. Revenue losses due to SWD between 2011 and 2014 are estimated to be $277 thousand, which is less than 1% of realized revenues over the same period. In total, California’s conventional raspberry producers faced $36.4 million in revenue losses due to SWD between 2009 and 2014. California’s organic raspberry producers faced a total of $3.43 million in revenue losses due to SWD between 2009 and 2014. These estimated revenue losses are equivalent to 5.74% of realized revenues over the same period. Revenue losses of this magnitude are expected to continue in organic raspberry production until more effective chemical, cultural, or biological management programs are discovered. Furthermore, hydroponic nft channel revenue losses incurred by organic raspberry producers could potentially increase dramatically if SWD populations develop greater resistance to the current, limited set of chemical controls approved for organic use.SWD management is multifaceted. In addition to yield losses, managing SWD has significantly increased production costs for raspberry producers. Raspberry growers increase the number of insecticide applications and use additional labor to harvest their crop in response to SWD infestation pressure. These necessary insecticide applications require additional chemical purchases and access to sprayers and specialized equipment through custom application or purchase. Overuse of pesticides can lead to rejections of shipments if residues exceed legal tolerances for the chemicals; however, producers who adhere to mandatory label rates should, theoretically, never encounter this problem. Conventional raspberry producers have access to a variety of insecticides that provide excellent control for SWD populations at present. Raspberry growers observed in the UC Davis study discussed earlier applied SWD-targeting insecticides four to six times for both the fall and spring harvests. The most commonly used insecticides for this purpose were spinetoram, zetacypermethrin, and malathion. Assuming these chemicals are applied at their maximum label rates and with generic purchase prices observed in 2015, the per hectare material costs of these insecticide applications are $179.40, $7.22, and $29.78, respectively. Using a conventional raspberry grower observed in the UC Davis study as a point of reference, an example chemical management program included two applications of spinetoram and a combined application of zeta-cypermethrin and malathion in both the fall and spring harvest seasons.

Each application is estimated to have labor and equipment costs of $61.78 per hectare. In 2015, such a program would cost an estimated $581.14 per hectare in both the fall and spring harvests for a total cost of $1,161.28 per hectare for a single planting. This is consistent with the per hectare treatment program cost of $825.33 observed in Goodhue et al in 2011. Even though conventional raspberry producers have developed effective chemical management programs that virtually eliminate fruit losses due to SWD, organic producers still experience non-trivial yield losses due to more expensive and less effective insecticide options. Most California organic raspberry producers used only two SWD-targeting insecticides, spinosad and pyrethrin, during the time of this study. Of these two insecticides, only the organic formulation of spinosad has efficacy comparable to conventional insecticides. Spinosad applications are more expensive than conventional insecticides and organic growers are limited by its labeled use of two consecutive applications followed by rotation to a product containing another class of insecticide for resistance management. It is typically applied in conjunction with spinosad or other organic insecticides because it does not provide sufficient control on its own. Assuming spinosad and pyrethrin are applied at their maximum label rates and with generic purchase prices observed in 2015, the per-hectare material costs of these insecticide applications are $200.60 and $119.13, respectively. In the UC Davis study, organic raspberry growers were observed applying these insecticides between five to nine times for each seasonal raspberry harvest. Using an organic raspberry grower observed in the UC Davis study as a point of reference, a typical chemical management program included five applications of pyrethrin in the fall, three of which were applied in conjunction with spinosad, and six applications of pyrethrin in the spring, two of which were applied in conjunction with spinosad. Assuming the stated per-hectare material, labor, and equipment costs, such a program would cost an estimated $1,506.35 per hectare in the fall and $1,486.66 per hectare in the spring for a total cost of $2,933.01 per hectare for a single planting. It is important to note that even as these insecticide applications reduce SWD populations, they also provide control for other pests such as the light-brown apple moth, Epiphyas postvittana . As a result, it is difficult to attribute the entire cost of these chemical management programs strictly to the management of SWD. However, few insecticide sprays were applied to California raspberries before the SWD invasion, and the light-brown apple moth, another invasive insect, only impacts portions of the Santa Cruz and Monterey County raspberry production areas at present. The light-brown apple moth can also be effectively controlled more inexpensively with the organic microbial insecticide Bacillus thuringiensis Berliner. Therefore, we can infer that the majority of the observed insecticide applications included in this analysis were intended to control SWD populations.We also consider the additional labor costs associated with managing SWD in order to develop a comprehensive estimate of SWD management costs. Like many other horticultural products, raspberries are extremely labor-intensive to produce. Labor, the primary production cost, includes planting, pruning, weeding, spraying, hauling, cleanup, field sanitation, and harvesting. SWD control programs necessitate labor-intensive management practices in addition to chemical applications.

The grape cases, which had high anisotropy in both the leaf inclination and azimuthdistributions, did incur significant errors due leaf anisotropy for the 1D model. If leaf azimuth is uniformly distributed, this effectively reduces the impact of anisotropy in leaf inclination on the projected area fraction G. Since a leaf with a certain elevation angle could be parallel to the sun at one azimuth and perpendicular to the sun at another, an integration over all azimuths can smear out the effects of leaf inclination alone. As in the virtual canopies of this study, field measurements have shown that leaf inclination distributions are usually highly anisotropic. The azimuthal distribution of leaves may be strongly anisotropic within a single plant, but for relatively dense canopies, the azimuthal distribution is often fairly isotropic. In these cases, the assumption of leaf isotropy is likely to result in minimal errors. However, sparse, row-oriented crops such as vineyards may have highly anisotropic azimuthal distributions, in which case it may be necessary to explicitly calculate G based on measurements. These types of canopies are becoming increasingly prevalent in agricultural applications, due in part to the improved access to mechanical harvesters that a trellised or hedgerow canopy provides.Plant spacing and the resulting heterogeneity had the most pronounced effect on errors resulting from the use of Beer’s law. For the Grape N-S case, the assumption of heterogeneity resulted in an overestimation of the total daily absorbed radiation by 28%, 30%, and 36% on Julian days153, 232, and 305, respectively, with larger instantaneous over estimation near midday. For the Grape E-W case, round planter pot the assumption of heterogeneity also resulted in overestimating the total daily absorbed radiation by 74%, 51%, and 5% on Julian days 153, 232, and 305, respectively.

This was not simply an effect related to L, as was illustrated by the two potato cases. By simply rearranging the potato plants from a uniformly spaced into a row-oriented configuration, errors in the 1D model increased substantially. It is possible that the effect of horizontal heterogeneity can vary in the vertical direction, which appeared to be the case with the Corn canopy. This significantly altered the performance of the 1D model at any given height, although the canopy was dense enough overall that the 1D model performed well when predicting whole-canopy radiation absorption. This could have important implications if the radiation model is coupled with other biophysical models such as a photosynthesis model. The response of photosynthesis to light is nonlinear and asymptotic, so although whole-canopy absorption may be well-represented in some cases by a 1D horizontally homogeneous model, it is unclear if that will result in significant errors in total photosynthetic production given the non-linearity of its response to light. A limitation of this study is that results are only applicable under clear sky conditions. However, results can provide some insight regarding diffuse sky conditions by simultaneously considering all canopy geometries and simulated sun angles. Under a uniformly overcast sky, equal energy originates from all directions. A particular combination of sun angle and leaf orientation bias was required in order to observe a pronounced effect of leaf anisotropy. Thus, for diffuse solar conditions, it is speculated that the impact of leaf anisotropy will be decreased. Sun angle had an important effect on the instantaneous impact of leaf heterogeneity, and most commonly it was observed that low sun angles resulted in a decreased impact of heterogeneity. Therefore, it is likely that highly diffuse conditions will reduce the impact of heterogeneity near midday because a significant fraction of incoming radiation will originate from directions nearer to the horizon. Estimating light interception with Beer’s law is based on the assumption that canopies are homogeneous.

This inherently means that the rate of radiation attenuation along a given path is linearly related to the flux at that location. As the canopy becomes sparse, there are pathways for radiation propagation that allow radiation to penetrate the entire canopy without any probability of interception, which fundamentally violates the assumptions behind Beer’s law or a turbid medium. Therefore, the non-random leaf dispersion in canopies limits the ability of Beer’s law to link light interception to simple bulk measures of plant architecture. It is well-known that this heterogeneity or “clumping” of vegetation usually results in decreased radiation interception as compared with an equivalent homogeneous canopy. A common means of dealing with this problem without significantly increasing model complexity is to add a “clumping coefficient” W to the argument of the exponential function in Beer’s law. While this is a simple and practical means of reducing the amount of radiation attenuation predicted by Beer’s law, the challenge in applying the clumping coefficient approach is that W is a complex function of nearly every applicable variable, and thus is it is difficult to mechanistically specify. Another approach is to use a model that explicitly resolves plant-level heterogeneity, as it may not be necessary to explicitly resolve every leaf if within-plant heterogeneity is small. Row orientation played an important role when estimating light interception from Beer’s law, particularly when the rows were widely spaced. For sparse, row-oriented canopies, the effective path length of the sun’s rays through vegetation can change dramatically with changes in sun azimuth. For East-West rows, absorption is significantly reduced early and late in the day because the rows are close to parallel with the sun’s rays, whereas North-South rows are perpendicular to the sun at this time. As the day of year progresses further from the summer solstice, the sun spends more time closer to the horizon and thus the impact of heterogeneity in an East-West row orientation increased. For the East-West row configuration, G and light interception were surprisingly constant throughout much of the day, which resulted in 41% and 36% less absorption on Julian days 153 and 232, respectively, compared to North-South rows.

In some climates, it may be desirable to maximize sunlight interception, whereas in others it may be desirable to mitigate effects of excess sunlight to reduce temperatures and water use.Despite the simplified assumptions in Beer’s law regarding scattering, there was good agreement between predicted radiation interception using the 1D and 3D models in the PAR band. Scattering did not significantly influence light interception in this band because most of the incident radiation received by individual leaves was absorbed. However, in the NIR band, scattering introduced significant over estimation of absorption using the standard 1D model, since leaves are poor absorbers in this band. Using an ad hoc correction to account for reflection only reduced this over estimation of absorption. An additional correction to account for both reflection and transmission resulted in over correction, and a net under prediction of total radiation absorption.The objective of this work was to evaluate common assumptions used in estimating radiation absorption in plant canopies, namely assumptions of homogeneity or isotropy of vegetation. Our results demonstrated that for relatively dense canopies with azimuthally symmetric leaves, a 1D model that assumes homogeneity and isotropy of vegetation generally produced relatively small errors. As plant spacing became large, the assumptions of homogeneity break down and model errors became large. In the case of a vineyard with rows oriented in the East-West direction, errors in daily intercepted radiation were up to 70% due to heterogeneity alone, round pot for plants with much larger instantaneous errors occurring during the day. If leaves were highly anisotropic in the azimuthal direction, there was also the potential for large errors resulting from the assumption of vegetation isotropy which had the potential to increase errors above 100%. Day of year had an impact on model errors, which was that overall errors tended to decline with time from the summer solstice. In cases of canopies where the plant spacing starts to approach the plant height, it is likely necessary to use a plant-resolving radiation model in order to avoid substantial over prediction of absorbed radiative fluxes. Additionally, if vegetation is highly anisotropic in terms of both elevation and azimuthal angle distributions, it is also likely necessary to explicitly calculate the projected area fraction G based on measurements and the instantaneous position of the sun.Recent shifts in climatic patterns have influenced the frequency, timing, and severity of heat waves in many wine grape growing regions, which has introduced challenges for viticulturists. Growing the same varieties under these altered climatic conditions often requires mitigation strategies, but quantitative, generalized understanding of the impacts of such strategies can be difficult or time consuming to determine through field trials. This work developed and validated a detailed three-dimensional model of grape berry temperature that could fully resolve spatial and temporal heterogeneity in berry temperature, and ultimately predict the impacts of potential high berry temperature mitigation strategies such as the use of alternative trellis systems.

A novel experimental data set was generated in which the temperature of exposed grape berry clusters was measured with thermocouples at four field sites with different trellis systems, topography, and climate. Experimental measurements indicated that the temperature of shaded berries closely followed the ambient air temperature, but intermittent periods of direct solar radiation could generate berry temperatures in excess of 10◦C above ambient. Validation results indicated that by accurately representing the 3D vine structure, the model was able to closely replicate rapid spatial and temporal fluctuations in berry temperature. Including berry heat storage in the model reduced the errors by dampening extreme temporal swings in berry temperature.Increasing temperatures and temperature variability associated with a changing climate have become a major concern for grape producers due to the sensitivity of grape quality to climate, particularly in wine grape production. Short-term temperature extremes associated with heat waves, along with longer-term shifts in seasonal temperature patterns are known to create significant challenges in managing grape quality. Diurnal fluctuations in solar irradiance and air temperature have been shown to affect amino acid and phenylpropanoid berry metabolism at hourly time scales. Elevated temperatures during daily or weekly time periods have been shown to decrease anthocyanin concentration around veraison. Furthermore, the duration of the elevated temperatures not only has an effect on berry composition but also on berry skin appearance. Exposed berries can be damaged by sunburn, and even a few minutes of high temperature exposure can result in cellular damage. Moderate temperatures can also result in berry injury or death after long-term exposure. Grape producers have begun to implement a number of canopy design and management strategies in an attempt to mitigate the negative effects of elevated berry temperatures, including the use of shade cloth, trellis design, and cluster height. However, grape berry microclimate is complex and highly heterogeneous due to interactions between the vine architecture and the environment, making it difficult to understand and predict the integrated effects of mitigation efforts. Experimental field trials are complicated by the fact that measurement of light and temperature at the berry level is labor-intensive and expensive. Furthermore, the relatively slow development of grapevine systems means that field trials are costly and may require many years of data collection. Because it is not feasible to independently vary every parameter that determines berry temperature in field experiments , crop models provide a means for understanding, and ultimately optimizing, how grapevine design and management practices can be used to mitigate elevated berry temperatures. Previous process-based models have been developed to predict berry radiative fluxes and berry temperatures from environmental parameters. However, in these models the calculation of absorbed radiation and the parameters to represent specific geometrical canopy structure are often simplified. Therefore, the models cannot account for the vertical and horizontal variability within the cluster or canopy, making it difficult to represent different design or management choices such as using altered trellis designs or pruning practices. Previous work has developed models for individual grape and apple fruits, and the work of Saudreau et al. successfully developed a 3D model of apple fruit temperature. However, to the authors’ knowledge, previously developed 3D grapevine structural model have yet to be coupled with a physically-based berry temperature model. This work develops and tests a new 3D model for grape berry temperature based on the Helios modeling framework. The berry temperature model was validated using a unique data set that spans four different canopy geometries.

Postmenopausal women aged 50 to 70 years with BMI of 25 – 40 kg/m2 were enrolled. Postmenopausal status was defined as a lack of menses for at least two years or at least six months with a follicle-stimulating hormone level of 23 – 116.3 mIU/mL. Other inclusion criteria were an overall body weight equal to or greater than 100 pounds, and agreement to comply with all study procedures. Exclusion criteria included BMI greater than 40 kg/m2 , blood pressure greater than or equal to 140/90 mm Hg, abnormal values from a lipid panel, complete blood count , or comprehensive metabolic panel , use of prescription medications other than thyroid, daily use of anti-coagulation agents such as aspirin and non-steroidal anti-inflammatory drugs, or use of dietary supplements other than a general formula of multivitamins/minerals that provided up to 100% of the recommended dietary allowances. Additional exclusion criteria were vegetable consumption greater than or equal to 3 cups/day, fruit consumption greater than or equal to 2 cups/day, fatty fish intake greater than or equal to 3 times/week, dark chocolate intake greater than or equal to 3 oz/day, coffee and/or tea intake greater than or equal to 3 cups/day, alcohol intake greater than 3 drinks/week. Women were also excluded if they followed a non-traditional diet , engaged in routine high-intensity exercise, self-reported diabetes, renal or liver disease, malabsorption or gastrointestinal diseases, cancer within the last five years, or heart disease, including cardiovascular events or stroke. After determining initial eligibility through telephone screening, participants were further screened at the laboratory in the morning after an overnight fast. After informed consent was obtained, black plastic plant pots anthropometric measurements were taken, including body weight, height, and waist circumference.

Blood pressure and resting heart rate were measured three times, five minutes apart, after 15 minutes of sitting quietly. Volunteers also completed a diet and health habits questionnaire . A fasting blood sample was collected for a CMP, a CBC, and a lipid panel . If participants reported menses occurring within two years prior to the telephone screening, FSH was measured. Volunteers were excluded if their low-density lipoprotein value was greater than or equal to 190 mg/dL, or for those with zero to one major cardiovascular risk factors apart from high LDL cholesterol if their LDL was greater than or equal to 160 mg/dL, for those with two major cardiovascular risk factors apart from elevated LDL cholesterol greater than or equal to 130 mg/dL, or for those with two major cardiovascular risk factors apart from high LDL cholesterol and a Framingham 10-year risk score of 10 to 20% . Study I was a single-arm, four-week trial . Baseline values were collected at study visit 1 , which then began a run-in period of two weeks during which no mangos were consumed. At SV1, baseline anthropometry, blood pressure, PAT, and blood was collected, and taken again two hours later. At the end of two weeks, study visit 2 began with baseline measures taken, followed by ingestion of 330 gm of pre-packaged, fresh, frozen Ataulfo mangos, and data were collected two hours later. Participants then returned home with a 14-day supply of pre-packaged mangos and instructed to consume 330 gm of mangos daily, with 165 g eaten before noon, and the other half consumed in the evening. Two weeks later, study visit 3 ensued, which followed the same protocol as SV2 . Water was allowed ad libitum during all study visits. Prior to each study visit, participants were instructed to refrain from strenuous exercise for 24 hours before arriving at the laboratory to reduce the potential impact on PAT measurements.

Two 3-day food records were collected, once between SV1 and SV2, and again between SV2 and SV3. The records were analyzed using the Food Processor software . Study II was based on the findings from study I. This single-armed trial design is shown in Figure 2. After an overnight fast, at SV1, anthropometry, blood pressure, heart rate, and blood samples were collected at baseline and at one-hour and two-hour time points. At least 48 hours later, at SV2, baseline measures were taken, followed by ingestion of 330 gm of pre-packaged, fresh, frozen Ataulfo mangos, and data were collected 1h and 2h after intake. After at least two days, at SV3, baseline measures were taken, followed by ingestion of 113 g of white bread, which contained calories and carbohydrates similar to those found in 330 gm of mangos, and data were collected 1h and 2h after ingestion. The inclusion and exclusion criteria were the same for both study I and II. Participants were instructed to refrain from consuming additional mangos before SV1 and throughout their enrollment. Procedures were performed at the same time of the day to minimize circadian effects. The screening and interventions were conducted at the UC Davis Ragle Human Nutrition Research Center. The UC Davis Institutional Review Board approved the protocol, and the study was registered at ClinicalTrials.gov . Microvascular function was assessed by PAT . After resting in a supine position for 30 minutes, a non-invasive, sterile finger probe was fitted to each middle finger. A manual blood pressure cuff was placed on the distal forearm of the non-dominant arm. A baseline reading of peripheral arterial tone was recorded, and then the blood pressure cuff was inflated to a supra-systolic level approximately 60 mmHg above systolic blood pressure to induce occlusion of blood flow for five minutes. Then, the pressure was released, resulting in reactive hyperemia. Two consecutive blood pressure measures were taken immediately before and after the PAT assessment.

The PAT software then automatically calculated the reactive hyperemia index , Framingham Reactive Hyperemia Index , augmentation index , and AI adjusted to 75 beats per minute . Whole blood was collected and rested at room temperature for 15 minutes before centrifugation at 200 x g for 10 minutes. Half of the serum was then aliquoted for use as platelet rich plasma , and the remaining serum was further centrifuged at 1500 x g for 15 minutes to provide platelet poor plasma . An average platelet count of PRP was measured with a hemocytometer. Depending on the platelet number in the sample, a specific ratio of PRP and PPP was combined to create a test sample with a final cell count of 250,000 platelets per µL. Then, the combined plasma was held at room temperature for 20 minutes, after which platelet aggregation was assessed . After calibration using sterile water, 500 µL of the previously prepared combined plasma was placed into glass cuvettes and incubated at 37 ºC for three minutes. Collagen was then added to the PRP to induce aggregation while the PPP was left untouched and served as a control. The collagen was added in separate cuvettes at either 1 or 3 µg collagen per 1 ml of PRP. The changes in aggregation were measured for amplitude , slope , lag time , and area under the curve . Microvascular function, calculated by the RHI, was the primary outcome for study I. Sample size calculation was determined based on a previous study from our laboratory assessing the effects of walnuts on vascular function.23 Microvascular function values were assumed to have a standard deviation of 0.5. Therefore, a sample size of 20 was needed to detect significant differences in RHI with 80% power at a 5% level of significance. Data were checked for normality and homogeneity of variance using the Shapiro-Wilk or Brown-Forsythe tests. The two-week differences in microvascular function, anthropometric and biochemical measures, and nutrient intake were analyzed using paired-t tests. The 2h change values for microvascular function, BP, platelet aggregation, and blood glucose were analyzed by one-way repeated measure Analysis of Variance using treatment as the main factor and participant ID as the random effect. For study II, the acute changes from baseline in BP, blood glucose, and insulin were analyzed by two-way RM ANOVA using time and treatment as the main factors and participant ID as the random effect. For main effects, Tukey’s tests were used for post-hoc analysis, with student t-tests used to determine significance within group pairs. A p < 0.05 was considered statistically significant. Statistical analyses were performed with JMP version 16 . During the two-week mango intake period, the estimated increases in soluble fiber, total sugar, monosaccharides, disaccharides, β-carotene, vitamin C, vitamin E, folate, were expected, black plastic garden pots compared to the reported intakes during the run-in, no-mango period. Despite these increases in carbohydrates during the mango feeding period, fasting glucose and plasma lipid levels, body weight, and waist circumference, did not change. Some animal and human studies suggest that mango intake may benefit blood glucose control. The blood glucose levels after an oral glucose tolerance test were significantly decreased in obese Wistar rats fed a high-fat diet and supplemented with 35 ml of mango juice with or without peel extract for seven days, compared to controls.

Another study reported a significant decrease in fasting blood glucose in diabetic but not normal male Wistar rats 30 days after consuming a diet mixed with dried Tommy Atkins mango powder at 5% of diet weight. The RHI, fRHI, AI, AI75, and platelet aggregation did not differ two weeks after daily mango intake, which may have been due to the relatively short intervention period. In a randomized double-masked, placebo-controlled, four-week trial among healthy individuals aged 40-70 years with a BMI of 19-30 kg/m2 , the RHI as measured by PAT was significantly increased after daily intake of 100 mg intake of unripe mango fruit powder made from the Kili-Mooku cultivar, compared to their baseline levels. When fed the same powder at 300 mg per day, the RHI was significantly increased, but only among individuals with compromised endothelial function. Another study reported that the daily intake of 400 g of fresh frozen Ataufo mango pulp for six weeks significantly decreased SBP in lean individuals aged 18-65 with BMI 18-26.2 kg/m2 , and significantly decreased hemoglobin A1C, plasminogen activator inhibitor-1, interleukin-8, and monocyte chemoattractant protein-1 in participants with BMI > 28.9 kg/m2 . While intriguing, the results need interpreted cautiously since the BMI numbers were not the standard values used for healthy, overweight, and obese criteria. The SBP was significantly reduced in the first two hours after the first mango intake in Study I, compared to baseline or run-in values. In contrast, the SBP was unchanged in Study II at one and two hours after mango intake. The discrepancy between values from Studies I and II may be due to a low number of participants in study II. However, the change in PP was significantly reduced 2h after mango intake in both study I and II. Importantly, the PP also changed after white bread intake, suggesting that the response might be due to a postprandial effect. In study II, although the postprandial changes of SBP and DBP were not significantly different between the mango and white bread groups, the HR changes 1h and 2h after white bread intake were significantly increased compared to no mango intake. This finding is consistent with a report that both supine and standing HRs were significantly increased 1h and 3h after a 790 kcal meal in the morning after an overnight fast. However, the calorie content in mango and white bread in this current study was only 298 kcal. Studies regarding the consumption of fruits and postprandial BP and HR are scarce. Future research is encouraged to investigate whether fruit intake will induce similar hemodynamics as meals. In study I, the 2h change in blood glucose was not different between mango or no mango intake, despite the difference in sugar intake from the fruit. This observation was reinforced further in study II, where the blood glucose was significantly increased 1h after white bread intake but not after eating an isocalorically-matched amount of mango. The insulin level was also significantly increased 1h after white bread intake compared to 1h after no mango or mango intake. In addition, although the 2h change in blood glucose after eating white bread returned to a level similar to baseline values, the 2h change of insulin was still significantly elevated compared to the 2h value seen in the no mango group. These data are consistent with other reports regarding mango consumption and glucose regulation. For example, in obesity-prone mice fed a high-fat diet, the fasting blood glucose, insulin, and homeostatic model assessment for insulin resistance score were significantly decreased after 10 weeks of mango fruit powder intake at each of three levels .

Factors such as the degree of diversification in a region’s economy, prosperity in the region, as well as the size, number, and conditions of the transfers play a role in influencing the magnitude of the regional impacts. Concerns over third-party effects were instrument in IID’s decision to put conditions on its water transfers under the QSA—they limited the extent of land fallowing and required that water transfers to eventually be sourced from on-farm conservation. Strategies to combat such concerns over third party effects likely involve a variety of approaches including social programs and support for land repurposing . Land repurposing as a response to the likely reductions in irrigated cropland is gaining significant attention in California . Developing solar energy, restoring desert and upland habitat, or riparian and wetland areas, expanding water-limited crops, or developing water-efficient urban development in formerly irrigated areas are all possible options for repurposing . In addition, conservation incentive programs could help mitigate the impacts of fallowing on ecosystems and people, and redistributing irrigation water onto fewer irrigated acreage should consider ecosystem services of alternative uses to maintain multifunctional landscapes in a changing climate.Augmenting water supplies through importing water from other regions, or further tapping into local surface or groundwater supplies, are limited at best. Yet supply augmentation options do exist, albeit likely at a higher cost . A portfolio of options needs to be considered, plastic grow pots including better capture and use of flood water, maintaining healthy soils, and more effective monitoring, surveillance, and response to extreme weather events.

Groundwater recharge , water recycling and reuse, and desalination provide opportunities to enhance supply. Increasing the operational efficiency of surface or groundwater storage and transport can also increase water availability. Last, water trading can help reallocate water supplies to reduce costs of both temporary and long-term shortfalls . Groundwater recharge. Managed aquifer recharge is the intentional recharge of water to aquifers for subsequent recovery or environmental benefit . MAR practices have been used in California in its operation of water banks–aquifers used for underground storage–and to avoid saltwater intrusion in aquifers in coastal zones. There is now renewed interest in developing MAR efforts to catch flood flows, especially for its low financial and environmental cost compared to other alternatives . The California Department of Water Resources found that an annual average of almost 2,000 hm3 is available for recharge using current infrastructure without interfering with environmental regulations. Adding new infrastructure could increase recharge opportunities in nearly all California regions over time, and particularly in the Sacramento Valley where significant opportunities exist . The flows that comprise the recharge are often available in large magnitudes for short periods and thus present challenges due to regulation and infrastructure. Current storage and conveyance infrastructure as well as operational and regulatory practices need to be expanded and improved to make full use of this water supply augmentation option. Although most water volumes have been recharged in dedicated basins in California, there is also much interest for on-farm recharge . By recharging water directly on farms, current irrigation infrastructure could be used, thus reducing the costs. Institutional challenges include lack of incentives for farms to accept flows because the individual farm benefits may be small relative to the public benefits.

Additionally, some crops likely are better suited for this than others, e.g., crops that are dormant in winter–such as almonds and vines–may not be negatively impacted by this practice. Additional research on recharge issues is needed to better understand the effects of on-farm recharge on crop yields, water quality, and soil health, among other factors .Wastewater recycling . The California State Water Resources Control Board estimates that 900 hm3 of wastewater was recycled in California in 2020 , with 250 hm3 being used for agriculture. In 2020, the state published its California Water Resilience Portfolio , which aims to recycle and reuse 3,100 hm3 over the next decade. Most of the wastewater in the CV is already being used with further treatment by downstream users or the environment. Therefore, the most promising locations for wastewater reuse and recycling are in Coastal California, where much of the wastewater is not being reused. Furthermore, while wastewater quality varies significantly across sources with more highly polluted water needing more costly treatment, some of those costs might be avoided for some farm uses . Desalination. Salty water can be treated to make it suitable for urban or agricultural use. In California and other western states, desalination has mostly been used to remove salts from brackish water. The lower constituent concentrations in brackish water make the process less costly than ocean desalination and, thus, more feasible for farm use. Currently, 14 seawater desalination plants are spread across California producing 110 hm3 , with another 23 brackish groundwater desalination plants producing 173 hm3 . There are plans to desalinate another 35 hm3 of seawater by 2030 and 104 hm3 of brackish water by 2040. These quantities contribute a small fraction to the overall water supply in California. Also, the infrastructure and energy costs of seawater desalination remain high particularly for agriculture, even without consideration of the likewise costly mitigation of negative environmental effects. Some have identified inland non-seawater desalination as lower cost alternative , yet brine disposal costs at the operation scale needed for irrigation may remain a challenge.

Seawater desalination is mostly used in urban areas of Southern California and the Central Coast, where alternatives are even more expensive. Water trading. California has a small active water market where buyers and sellers trade water . These trades– ranging from 2 to 5% of all water used by cities and farms, reduce the economic costs of shortfalls during droughts and accommodate geographic shifts in water demand, enhancing flexibility in water management . Studies have found that trading could bring significant benefits to agriculture, the environment, and urban users in California . The benefits of an expanded water market grow as water scarcity intensifies, which is likely given the transition to sustainable groundwater use and the reduction in water availability driven by climate change . But a combination of aging infrastructure and complex, conflicting regulatory structures, including volume limits, hinder the expansion of trading . Improving market design, addressing impacts on third parties, securing stakeholder buy-in, and reducing transaction costs are needed to improve California’s water market . Of course, increasing water demand by cities may further drive water from agriculture to cities through water trading agreements . The Mix of Supply- and Demand-Side Options. The combination of supply- and demand-side options will shape the evolution of California’s agriculture. With the expected water availability declines, expanding supplies could mitigate the reduction of California’s agricultural output. But economic pressures constrain supply expansion, as most supply options are too expensive for crop irrigation, which is profitable only if the revenues of the expansion outweigh the opportunity costs . Water trading should incentivize supply expansion, as trading allows water to move to higher profit cropping locations. Federal and state investments can also propel supply expansion. An economic assessment of supply- and demand-side options in the SJV found that around 500 hm3 of supply expansion might be efficiency enhancing—i.e., willingness to pay for supplies is greater than the costs. While 500 hm3 only represents a quarter of the expected decline in water availability, demand reduction will comprise most of the adaptation. Other regions will have different constraints and options. In the Sacramento Valley there will be less water availability declines and more options for groundwater recharge, resulting in less demand reduction. In the Central Coast, high-value crops are more likely to pay for expensive supply options , but even there some demand reductions are likely. In the South Coast, growth of urban demands and the reductions in Colorado water allocations will likely be met by reduced irrigated acreage, although supply expansion partnerships between local farms and urban interests might be feasible . Cropping System Design. For better performance, big plastic pots water stewardship must be accompanied by cropping system adaptations to climate change that reduce water use while regenerating natural resources, maintaining food production, and allowing farms and ranches to build resilience mechanisms. Adapting crop management practices are a main entry point for adaptation through changes in crop location, planting schedules, genotypes, and irrigation . The large range of crops grown in California allows for crop switching based on vulnerability assessments and ecosystem service provision . Management complexities, response to market demand, and downstream infrastructure often make such system adjustments difficult to implement and coordinate at the watershed scale to improve water use and conservation measures. Reallocation of water resources to perennial crops has increased in recent decades with drought-year fallowing of annual cropland.

More comprehensive system-based solutions would create incentives to keep soil covered to provide cobenefits for long-term sustainability with low potential tradeoffs for water use . With climate change, perennial crops are increasingly exposed to year-long stressors that increase their need for irrigation and present growers with less adaptation options to annual variability, such as Relocation and replacing tree species/cultivars . Careful implementation of low-volume irrigation systems is crucial to avoid negative implications on groundwater recharge. Moreover, while subsurface drip irrigation enhances field and plant scale water use efficiency compared to flood irrigation, drip systems can degrade soil health properties important for water infiltration and runoff control, salinity mitigation, and carbon sequestration within the soil profile . While efficiency and technology replacements have a role to play in optimizing water use; they seldom address the ecological, economic, and social drivers of vulnerability Effective adaptation measures must therefore be system based and consider the complex socioecological interactions at play to ensure climate smart outcomes . There is growing evidence that ecosystem-based adaptation options such as cropping system diversification can support adaptation while storing carbon, supporting biodiversity, and securing ecosystem services . This is especially relevant for both California’s organic crop production, and horticultural systems which tend to be more reliant on ecosystem services for pollination and bio-control than field crops. Managing for diversity and flexibility rather than simplification and consolidation enhances adaptive capacity by improving responsiveness to climate changes, lowering vulnerability, and allowing portfolio effects to mitigate impact of disturbances . Diversification using inter-cropping, longer crop rotation, or integrated crop livestock designs have been shown to support water regulation and buffering of temperature extremes as well as other ecosystem benefits which can in turn mediate yield stability and reduce risk of crop loss . Improvements in soil health associated with organic carbon inputs, soil cover, and diversification can mediate groundwater recharge and water and nutrient retention to mitigate yield loss under drought . However, tradeoffs and benefits of ecosystem-based approaches for adaptation and mitigation are context specific, and rigorous assessments of adaptive gains and water footprints are needed. As water scarcity and associated changes in crops and landscape structures unfold, developing approaches that exploit the interconnectedness of diversity at fields, operations, landscapes and food system scales with healthy ecosystems and communities will be critical for sustainable and equitable transitions.Responding to climate change and the accompanying challenges facing agriculture in California is most effectively accomplished with inclusive and innovative approaches involving farm and rural stakeholders and policymakers using information and tools from researchers and advisors. With effective adjustments in response to climate and related water supply and demand concerns, California agriculture can become more economically, socially, and environmentally sustainable in the future. Water is central to that future. Government water management and planning in California has long been institutionally and geographically decentralized. Many local irrigation districts and SGMA groundwater sustainability agencies develop, implement, and maintain plans to weather recurrent droughts and floods. Agencies attempt to facilitate system-wide flexibility in water allocation, which can improve resilience in the case of climate extremes. There is also a role for agencies to improve coordination among stakeholders and facilitate flexibility to allow water to flow where it contributes most to economic, environmental, and social goals. Unfortunately, these broad benefits often are not within the mandate of local agencies. Furthermore, devolution in water management to local agencies rather than to watershed-level governance, creates natural conflicts where one agency’s goals or actions may create conflict and externalities with another nearby agency given water often extends beyond any single agency’s political boundaries.