Month: September 2024

The soil bulk EC values were extracted from the location of each experimental unit, these values were further used to perform regression analysis. Kriging and k-means clustering on plant physiology variables were performed with the R packages “gstat” and “NbClust,” v3.0 . Universal kriging was utilized on plant water status because of the existing trend in longitude and latitude. Variograms were assessed by “automap” package 1.0-14 , and fitted to perform universal kriging. The vineyard was delineated into two clusters by k-means clustering, including Zone 1 with higher water deficit and Zone 2 with lower water deficits. The separation described 78.1% in 2017 of the variability in the plant water status according to the result of between sum of squares/total sum of squares. The resulting maps were organized and displayed by using QGIS software . Cluster comparison was analyzed by “raster” package reported as Pearson’s Correlation between two cluster maps . Data were tested for normality by using Shapiro-Wilk’s test, and subjected to mean separation by using two-way ANOVA with the package “stats” in RStudio . Significant statistical differences were determined when p values acquired from ANOVA were <0.05, and the zones were classified according to Tukey’s honestly significant difference test. Regression analysis was performed by SigmaPlot 13.0 . Correlation coefficient between variables were calculated in by Pearson’s correlation analysis, and p-values were acquired to present the significances of the linear fittings. In our previous work, drainage pot we were unable to deduce a significant relationship between site topography variables such as absolute elevation and berry chemistry .

Bramley et al. showed that soil bulk EC was directly related to soil clay content, which was contradictory to our findings. We attributed this discrepancy to the relatively stable soil texture throughout the season or even several seasons. On the other hand, the effect of soil water content might be the major factor to influence plant development during the season. The soil texture and soil bulk EC sensing analysis conducted in this study were able to explain the variability in plant water status that the site topography could not. Soil texture and soil bulk EC can be related to spatial differences in soil water availability . Specifically, soil texture is a determinant of soil water holding capacity, hence affecting the amount of water available to the plants. In our study, the western section of the vineyard had greater loam proportion, where the grapevines were experiencing more severe water deficits . The eastern section had more sandy soil in both deep and shallow soil, where the grapevines were under less severe water deficits. Our findings are corroborated with previous work, where clay soil would lead to less plant available water, although clay soil had higher water holding capacity than sandy soil . Furthermore, Cabernet Sauvignon grapevines grown in clay soil would result in lower gs and An compared to grapevines grown in soils that had higher proportion of sandy soils . There was evident variability in soil bulk EC in this study. Previous studies reported that when soil bulk EC was proximally sensed, it was closely related to soil water content . We found that soil bulk EC was consistently and directly related to long-term 9 stem over the course of our study. Our findings are corroborated by previous works , where higher soil bulk EC values corresponded to higher soil water content.

Previous studies suggested that the relationship between soil water content and soil bulk EC was soil-specific, and needed to include soil chemical and physical properties to explain variability and plant water status . Due to the limited amount of water put into wine grape vineyards, soil water content would be the major factor affecting soil electrical properties rather than the residual salinity after water evaporation from soil. The significant relationship between soil bulk EC and 9 stem in this study agreed with previous studies, indicating the possibility of soil bulk EC sensing being used to assess plant water status . Moreover, in our study, the spatial variability in grapevine physiology reflected the variability in soil bulk EC very well when assessed by proximal sensing. Due to the relationship of soil bulk EC on the amount of available water to plants reported in previous research , this approach had been utilized to identify the variability in the plant physiology based on the soil sensing technologies and apply targeted management strategies , and our study provided more evidence toward the feasibility of it. The variability we measured proximally in soil characteristics was reflected in plant water status and leaf gas exchange in our study. Previous research had reported that variable soil characteristics in space would cause spatial variations in plant water status . Although the precipitation amounts were vastly different between the two dormant seasons, the uniformly scheduled irrigation did not ameliorate the natural spatial variability in plant water status induced by soil properties. On the contrary, the separations in plant water status and leaf gas exchange were already significant even before the irrigation ceased after veraison. This proved that the spatial variability in the soil dominated the accessibility of the available soil water toward the plant, and made the spatial variability expressed in the grapevine. Our results in the second year corroborated those of the first year, showing that the separation in both plant water status and leaf gas exchange between the two zones were consistent. Leaf gas exchange was closely related to plant water status, and this relationship was shown in previous research .

The relationships between leaf gas exchange and plant water status were evident in our study, where a higher 9 stem would promote a greater stomatal conductance to increase carbon assimilation capacity and decrease intrinsic water use efficiency. In our study, the lowest 9 stem we observed were around harvest with 9 stem of -1.6 MPa and gs of around 50 mmol H2O m−2 ·s −1 , which were not severe enough to impair berry ripening although the photosynthetic activities were still affected. Overall, the gs and AN reached the maximum values at veraison and declined with decreasing plant water status and leaf age toward the end of the season. This further affirmed that the continuous water deficits during the growing season, especially being more pronounced after irrigation was ended after veraison, would reduce stomatal conductance. The water deficits would act as passive hydraulic signals or active hormonal signals with the upregulation in abscisic acid synthesis to limit plant photosynthetic activities, hence lower gs and AN values . According to the previous research, components of yield may be affected by plant water status, where higher water deficits would result in reductions of yield, berry skin weight, and berry weight . In our study, we observed constant separation in plant water status after veraison. However, there was no difference shown in cluster number, yield, berry number, drainage planter pot or pruning weight. The only difference measured in yield components was that berry skin weight was higher in Zone 1 in the second season. Early season water deficit irrigation had higher probability to decrease yield than later season water deficit irrigation . However, a season-long water deficit irrigation would have the lowest yield even despite the season-long water deficit irrigation regime applying double amount of water than the other regimes . Some other studies did not have the same results, as early water deficit irrigation did not show significant influences on yield compared to late water deficit irrigation . Another possible explanation was that Zone 1 had greater water amount held in the soil due to the higher clay content. The clay soil with higher water-holding capacity had a better water status at the early season compared to Zone 2, even though the sandy soil in Zone 2 would benefit the plant growth with irrigation when the season progressed . The later season water deficit was exacerbated in Zone 1 due to its higher clay content, causing Zone 1 lost the benefits from the high water status in the early season, and eventually had similar yield components with Zone 2 at harvest. In our work, we did not see any evidence of Ravaz index being affected by spatial variability of plant water status. These results were corroborated by Terry and Kurtural when grapevine cultivar ‘Syrah’ was exposed to post-veraison water deficits in comparable severity of -1.4 MPa .Water deficits affect advancement of grape berry maturity, they promote TSS accumulation and TA degradation in grape berries . Two factors contributed to these differences between the two zones. First, a greater water deficit advanced the berry maturation, leading to a higher TSS and lower TA . Second, berry dehydration may have occurred and the TSS concentration increased in the berries. In our study, smaller berries were observed in Zone 1, which can confirm the berry dehydration could have led to higher TSS in Zone 1. As for berry TA, one study showed that grape organic acids biodegradation would be faster with more solar radiation and higher temperature .

Although the acid degradation was not related to water deficits, like mentioned above, water deficits would limit the grapevines’ ability to regulate temperature . Thus, water deficits could promote the organic acid degradation and this effect was observed in this study. Mild water deficits increased the flavonoid content and concentration of red-skinned grape berry due to the upregulation in flavonoid synthesis and the advancement of berry dehydration during growing season . A positive relationship was noticed between soil bulk EC and total skin anthocyanins in 2017 at both depths of soil bulk EC measurements. A more prolonged severe water deficit would lead to deleterious stomatal and temperature regulation and eventually resulted in flavonoid degradation, specifically anthocyanins . This was a plausible explanation for the non-significant relationship between soil bulk EC and total skin anthocyanins in 2016, wherein harvest took place at higher soluble solids and Zone 1 berry skin anthocyanins were presumably in decline. Furthermore, the berry weights were higher in Zone 2, which was similar to the observations in our previous work , indicating there was less berry dehydration. Thus, the higher anthocyanins in Zone 2 was mainly due to the upregulation in anthocyanins other than anthocyanins degradation. These effects were also observed in the wines of 2016, where Zone 2 had higher anthocyanin concentrations. However, in the second season, the differences in berry skin anthocyanins at harvest did not carry over into the wines. We contributed this to the more advanced berry maturity levels at harvest in the first season, the skin cell walls could have become more porous during ripening and increased the extractability of flavonoid compounds . With relatively greater amounts of flavonoids extracted, there was a higher chance to pass on the separations of anthocyanins from the berries to the wines. Nevertheless, their biosynthesis and concentration may be modified by water deficits . In 2016, wine total proanthocyanidins and all the subunits were greater in Zone 2. These differences were not observed in the second season. We attributed this lack of consistency in proanthocyanidin disparities between the two zones to the more advanced maturity of the berries were harvested in 2016 than in 2017. We suggest that similar to skin anthocyanins, the more advanced berry maturity in 2016 could have promoted the proanthocyanidin extractability in the skin tissues , which may augment the separations in the concentration of all the subunits between the two zones. Fruit flavor is an elusive trait, influenced by many factors including genetics, environments and cultural practices . Breeders increasingly are focused on meeting the needs of consumers, but genetic improvement of flavor is challenging as a consequence of the chemical and genetic complexities of the flavor phenotype . These challenges are accentuated in heterozygous, polyploid species. For example, fewer significant single nucleotide polymorphisms were detected in genome-wide association study of tetraploid blueberry when diploid models were applied ; in octoploid strawberry, structural variation underlying a locus affecting volatile production was difficult to resolve using a single reference genome . Recent advances have been made via chemical–sensory studies to identified specific volatiles associated with consumer preference . Although important volatile compounds in fruit crops are being identified, too little is known about the metabolomic and genetic diversity within species and breeding populations.

While farmers agreed that gauging soil nitrogen and other key soil nutrients was important to consider and be aware of generally, other aspects of soil management, such as promoting soil biological processes, maintaining adequate soil moisture and aeration, or planting cover crops in regular rotation, were more critical to adequately maintaining soil fertility on their farm. An analogous soil health study similarly found that among predominantly non-organic farmers in the midwestern part of the US, measuring nutrient levels in soil was generally not highlighted by farmers interviewed . When prompted to discuss key aspects of soil health, a majority of farmers in this past study completely omitted mention of the importance of gauging nutrient levels, or in their case “soil mineral fertility,” as an indicator for soil health. This prior finding in combination with our findings here suggests that measuring nutrient availability to crops may not be as important as initially hypothesized to organic and non-organic farmers alike. Importantly, Gruver and Weil posited that the lack of emphasis on soil mineral fertility among these midwestern farmers may have occurred because they perceived that their soil fertility was not currently limited by nutrient availability to crops. Our research with organic farmers in California corroborates this hypothesis, and we suggest further research in other farming contexts to see if this sentiment among farmers is more widespread. We learned that there were three related reasons for why organic farmers in our study expressed that measuring nutrient levels was not particularly relevant for gauging soil fertility on their farm operation.

For one, large pot with drainage as already mentioned, farmers emphasized that they relied on carefully orchestrated soil management practices—such as the application of cover crops and livestock rotations—rather than depending on organic nitrogen-based fertilizers—to supply nutrients to crops. Because a majority of farmers applied less than 25 kg-N/acre of additional fertilizer per growing season, farmers in this context emphasized that their soil chemical and biological processes related to soil fertility may potentially diverge from agriculture that was predominantly or exclusively fertilizer-based. By creating internally regulated farming systems via diverse management practices, these farmers observed that in general nutrient availability to their crops was ensured over the growing season. This key finding shared by farmers overlapped strongly with hallmarks for resilient agriculture outlined by Peterson et al. , who summarized features of internally regulated farming systems and key management practices associated with these systems. Based on knowledge shared by farmers, we suggest that it is possible for farming systems that integrate multiple management practices rather than rely on external fertilizer inputs to create soil conditions that “buffer” soil nutrient levels. In these internally regulated systems, measuring nutrient availability to crops may be less practical or even achievable with available soil indicators, as certain nutrients only become available as needed by local soil processes, and strongly depend on plant root structure, associated mycorrhizal pathways, and microbial communities present . To this end, several farmers hypothesized that available soil indicators were not sensitive to alternative approaches to maintaining soil fertility, likely because these fertility management practices operated on different timescales of nutrient release compared to direct fertilizer application.

These conclusions drawn by farmers on the limits of measuring nutrient availability to crops were not unlike broad thematic gaps in measuring bioavailable nitrogen to crops discussed by Grandy et al. and others previously . In particular, Grandy et al. discussed the importance of considering soil health gradients, especially on farms that are not “ecologically simplified” and do not rely extensively on fertilizer application; such farm systems, like the farms examined in this study, are not as dependent on soil inorganic N and instead rely on what Grandy et al. call “a highly networked supply of organic N.” In other words, as farmers in this study also pointed out in interviews, soil health and fertility depend on a variety of factors, such as plant root accessibility, the microbial communities present, and soil mineral properties . As hypothesized in recent soil health literature, available soil indicators may not fully capture the complex plant-microbe-soil interactions that regulate fertility, particularly on organic farms that use minimal organic fertilizer application—a sentiment supported by farmer knowledge in this region as well. Second, farmers in this study also questioned whether available indicators for soil nutrient levels were calibrated not only to alternative farming approaches but also to local soil conditions. Farmers emphasized that soil test metrics were not grounded in their farm operation and produced inconsistent results that were likely due to a combination of spatial and temporal variations in their land, and also due to differences in inherent soil characteristics. As most farmers also pointed out, soil indicators for fertility did not explicitly calibrate for inherent soil characteristics, such as soil structure and soil type, or soil management history. Yet, to farmers, local knowledge of prior and ongoing soil management were integral to making management decisions that improved, or at least maintained, soil fertility on their farm. Farmers in this region stressed that the synergy of management practices they applied were often calibrated to account for physical soil variability among fields, and therefore were closely informed by their local soil conditions and unique management histories.

While the importance of considering soil aggregate stability, soil texture, and management history when assessing soil indicators is well-documented in the soil health literature , in practice there continues to be a gap in soil health indicators that are tailored to be site-specific and/or farming system relevant . Given that soil indicators can vary by region and soil type, farmer involvement to provide key knowledge of local soil necessary for calibration of soil indicators is one essential way forward toward closing this gap. Merging results of soil tests with farmer knowledge may also help to increase sensitivity and utility of soil indicators across varying local soil contexts.Relatedly, farmers agreed that fine tuning management could alleviate challenges associated with inherent limitations due to physical soil characteristics . Local farmer knowledge from this study established that inherent limitations posed by their soil or poor prior management could not be overcome by adding more N-based fertilizers—even if soil indicators showed the contrary. Interestingly, prior studies in the region found that organic fertilizer use in the early organic movement was potentially more widespread. For example, early organic farmers in Yolo County who were interviewed by Guthman et al. in the early 1990s used high nitrogen-based organic fertilizers such as pelleted chicken manure, seabird guano, and Chilean nitrate to supply fertility to soil in their organic production; based on interviews here, several decades later, farmers appear to have significantly cut back on the use of such high nitrogen-based organic fertilizer products. Several of these farmers have explicitly realized that “more is not better” when it comes to organic fertilizers; as discussed above, the majority of farmers interviewed here have shifted towards implementing a synergy of management practices that promotes good soil structure, increased soil microbial activity and soil organic matter, and adequate soil moisture rather than using high nitrogen-based organic fertilizers. Third, drainage collection pot these organic farmers unanimously agreed that soil test results could be more useful to them if the numerical results were also provided with meaningful interpretation, ideally in the form of a direct conversation—and that importantly, moved beyond prescriptive recommendations for nutrient additions and organic fertilizer application. Farmers interviewed used a variety of rich metaphors to elaborate on this point, such as likening soil test results to the fuel gauge in a car; both provide little insight into the actual mechanics of how well the system, be it an engine or a soil ecosystem, is actually functioning. This key takeaway from farmers in this study suggests that available soil indicators do not fully account for the complexity of their ecological farming systems, and that farmers see the interpretation of soil test results as an essential part of addressing the underlying complexity, and holistic soil function in their broader agricultural ecosystem. Our study provides an initial window into farmer knowledge of soil function in relation to soil fertility; however, as PetrescuMag et al. emphasize, deeper research on this particular gap in farmer knowledge of soil function is essential to determine the specific content of interpretations accompanying soil test results that would be practical and informative to farmers. Another potential way to bridge this gap in applicability for farmers would be to incorporate descriptive indicators for soil fertility in conjunction with available quantitative soil indicators. As Romig et al. suggested several decades ago, descriptive indicators can integrate well with existing soil metrics, and therefore provide mutually acceptable alternatives to discuss soil health and fertility among farmers and scientists alike. Finding a common language through which to engage is at the heart of this current gap in soil health research . Indicators for soil fertility measured here provided limited effectiveness in differentiating between fields deemed by farmers as “most challenging” and “least challenging” , which suggests that current scientifically developed metrics for measuring soil fertility do not align well with farmer developed benchmarks for soil fertility.

This outcome additionally suggests that nutrient availability was not the driving factor for farmer perceptions of soil performance, at least in terms of soil fertility. Of the eight indicators for soil fertility measured in this study, total soil nitrogen was the only indicator that was able to detect differences in soil fertility ; however, fields selected by farmers as “most challenging” showed on average higher values of total soil nitrogen than fields selected by farmers as “least challenging.” Because higher total soil nitrogen values are generally equated with higher soil fertility in the soil health literature, we hypothesized that the “least challenging” fields would show on average higher values of total soil nitrogen . This alternative outcome here suggests that while this soil chemical property shows sensitivity to differences perceived by farmers in their selected fields, this commonly used indicator does not adequately capture the direction of farmer knowledge of soil fertility between their selected fields. On the one hand, it is not surprising that total soil nitrogen was the only soil indicator able to detect differences between farmer-selected “most challenging” and “least challenging” fields, especially given that after nearly a century of research total soil nitrogen remains one of the most predictive measures of soil fertility status . However, the contradictory direction of our results for total soil nitrogen between farmer-selected “most challenging” and “least challenging” fields emphasizes that current scientific application of this soil indicator does not readily transfer for use on-farm. One potential reason for this inconsistency may be because as a soil indicator, total soil nitrogen reflects both the amount of chemically stable organic matter and more active organic matter fractions, and therefore gives a rough indication of nitrogen supplying power in the soil. However, in practice it is possible that fields deemed by farmers as “least challenging” have depleted their nitrogen supplying power due to more frequent crop plantings, for example— compared to fields that are “most challenging” and therefore may be less frequently planted with crops throughout the year. This finding underscores the current lack of interpretation of soil test results in community with both agricultural researchers and farmers present together; the current gap in interpretation of soil testing results was repeatedly emphasized by farmers during interviews, and suggests that— moving forward, contextualizing and interpreting soil test results in local farming contexts is key to disentangling potential mismatches between farmer knowledge systems and agricultural researcher knowledge systems. To move toward this outcome requires deep listening and relationship building on the part of agricultural researchers not currently widely applied . Whereas another similar study found that active carbon was the singular most sensitive, repeatable, and consistent soil health indicator able to differentiate between fields in their study on organic farms in Canada , we highlight that one potential reason for this difference in our results might be as a result of differences in management in each study. While our study consisted of farms along a gradient of organic management , the prior study focused on three organic farms with similar management. This divergence in results highlights the importance of accounting for a gradient in management when evaluating the efficacy of soil health indicators on working farms. Much remains to be learned about how inherent soil properties and dynamic soil processes interact with complex management systems on working farms .

Cover crop frequency was determined using the average number of cover crop plantings per year, calculated as cover crop planting counts over the course of two growing years for each field site. In order to identify farm typologies based on indicators for soil organic matter levels, we first used several clustering algorithms. First, a k-means cluster analysis based on four key soil indicators—soil organic matter , total soil nitrogen, and available nitrogen —was used to generate three clusters of farm groups using the facoextra and cluster packages in R . The cluster analysis results were divisive, non-hierarchical, and based on Euclidian distance, which calculates the straight-line distance between the soil indicator combinations of every farm site in Cartesian space , and created a matrix of these distances . To determine the appropriate number of clusters for the cluster analysis, a scree plot was used to signal the point at which the total within-cluster sum of squares decreased as a function of the increasing cluster size. The location of the kink in the curve of this scree plot delineated the optimal number of clusters, in this case three clusters . To further explore appropriate cluster size, we used a histogram to determine the structure and spread of data among clusters. A Euclidean-based dendrogram analysis was then used to further validate the results of the cluster analysis. In addition to confirming the results of the cluster analysis, drainage collection pot the dendrogram plot showed relationships between sites and relatedness across all sites.

To visual cluster analysis results, the final three clusters were plotted based on the axes produced by the cluster analysis. One drawback of cluster analyses is that there is no measure of whether the groups identified are the most effective combination to explain clusters produced by soil indicators, or whether they are statistically different from one another. To address this gap, we used ANOSIM to evaluate and compare the differences between clusters identified with the cluster analysis above. To formally establish the three farm types and also make the functional link between organic matter and management explicit, we used the three clusters that emerged from the k-means cluster analysis based on soil organic matter indicators, and explored differences in management approaches among the clusters. We then created three farm types based on this exploratory analysis. Specifically, we first analyzed management practices among sites within each cluster to determine if similarities in management approaches emerged for each cluster. Based on this analysis, we used the three clusters from the cluster analysis to create three farm types categorized by soil organic matter levels and informed by management practices applied. Using the three farm types from above, we then analyzed whether our classification created strong differences along soil texture and management gradients using a linear discriminant analysis . LDA is most frequently used as a pattern recognition technique; because LDA is a supervised classification, class membership must be known prior to analysis .

The analysis tests the within group covariance matrix of standardized variables and generates a probability of each farm sites being categorized in the most appropriate group based on these variable matrices . To characterize soil texture, we used soil texture class . To characterize soil management, we used crop abundance, tillage frequency, and crop rotational complexity—the three management variables with the strongest gradient of difference among the three farm types. A confusion matrix was first applied to determine if farm sites were correctly categorized among the three clusters created by the cluster analysis. Additional indicator statistics were also generated to confirm if the LDA was sensitive to input variables provided. A plot with axis loadings is provided to visualize the results of the LDA and display differences across farm groups visually. The LDA was carried out using the MASS R package. To build on the results of the LDA, we performed a variation partitioning analysis to determine the level of variation in soil organic matter indicators explained by the soil texture variables, soil management variables, and their interactions . VPA was performed using the vegan package in R . Using indicator variables for soil organic matter levels, we performed a k-means cluster analysis to develop a meaningful classification of farms. Scree plot results indicated that three clusters produced the most consistent separation of field sites. As shown in Figure 1, the two dimensional cluster analysis produced a strong first dimension , which explained 86.7% of the separation among the 27 field sites. Total N, total C, POXC, and soil protein variables strongly explained this separation of farm types, as shown by the lack of overlap among the clusters along the Dimension 1 axis.

Histogram results provide a visual summary of linear difference among the three clusters and further confirms minimal overlap among clusters; however, Cluster I and Cluster II fields showed low dissimilarity between values 0 and -2 . Results from the average distance-based linkages of the dendrogram analysis similarly further established the accuracy of field site groupings determined by the cluster analysis. These results indicated that Cluster II sites were more closely related to Cluster III sites compared to Cluster I sites . ANOSIM showed strongly significant global differences among the three clusters , where a value of 1 delineates 0% overlap between clusters. Overall, ANOSIM verified the farm types obtained from the cluster analysis. In addition, ANOSIM pairwise t-tests that compared each individual cluster in pairs confirmed strongly significant dissimilarities between Cluster I and Cluster III sites . ANOSIM pairwise t-tests also indicated that Cluster I sites were significantly divergent from Cluster II sites; however, Cluster I and Cluster II showed less dissimilarities than Cluster II and Cluster III sites . ANOSIM pairwise t-test results were in congruence with the results provided by the histogram . Classification of farm sites using k-means clustering closely matched differences in on-farm management approaches . It is important to note that while general trends between clusters and management emerged, the management practices analyzed here do not fully encompass the management regimes of each farm field site, and are intended to be exploratory rather than definitive. Several general trends emerged across the three farm types . For instance, Farm Type I, 10 liter pot comprised of six field sites, consisted of fields with higher crop abundance values and fields that more frequently planted cover crops compared to Farm Type III. These sites used lower impact machines and applied a lower number of tillage passes compared to Farm Type II and III. In contrast, Farm Type II, also comprised of six field sites, and Farm Type III, comprised of fifteen field sites, represented fields on the lower end of crop abundance values and sites that applied cover crop plantings at a lower frequency than Farm Type I. Farm Type III on average applied a higher number of tillage passes and on average were on the lower end of ICLS index compared to both Farm Type I and Farm Type II. In general, Farm Type II used management approaches that frequently overlapped with Farm Type III, and less frequently overlapped with Farm Type I. Overall, farm types significantly differentiated based on indicators for soil organic matter levels . For all four indicators displayed in Figure 2, differences among the three farm types were highly significant . As visualized in the side-by-side box plot comparisons for all four indicators for soil organic matter levels, Farm Type I consistently showed the highest mean values across all four indicators, while Farm Type III consistently showed the lowest mean values across all four indicators. Farm Type I had mean values of 0.21 mg-N kg-soil-1 for total soil N, 2.3 mg-C kg-soil-1 for total organic C, 787 mg-C kg-soil-1 for POXC, and 7.4 g g-soil-1 for soil protein; compared to Farm Type I, Farm Type III had means values 43% lower for total soil N, 48% lower for total organic C, 58% for POXC, and 66% lower for soil protein. Compared to Farm Type I, Farm Type II had mean values 38% lower for total soil N, 26% lower for total organic C, 28% lower for POXC, and 30% lower for soil protein than Farm Type I. Standard errors for all four indicators are shown in Figure 2.Results of the LDA showed that both linear discriminant factors are most strongly explained by soil texture , as shown by the LDA loadings . Management practices all equally, but weakly, influenced LD1 and LD2 . LD1, which explained 66.3% of the variance, was effective at separating the Farm Type I and Farm Type III . However, Farm Type II overlapped with both Farm Type I and Farm Type III for LD1. In contrast, LD2, which explained 33.6% of the variance, did not display a definitive separation between the Farm Type I and Farm Type III; however, LD2 was effective at separating Farm Type II from Farm Type I and Farm Type III.

LDA accurately discriminated between the three farm types, with an overall accuracy of 90.1% , as shown in Table 8. Model accuracy was high for all three farm types . The model had the greatest sensitivity to Farm Type II and Farm Type III , and low sensitivity to Farm Type I . Both Farm Type I and Farm Type III displayed minimal confusion with Farm Type II, as the comparison of training and validation data details . We determined the proportion of variation in the three farm types accounted for by management and by soil texture . Soil textural class contributed 28% of unique variation , while management contributed 18% of unique variation . The shared contribution for all predictors was 1%, and the overall contribution of all predictors was 47%.We found across all 27 farm sites sampled that gross N mineralization rates ranged from 0.05 – 4.82 µg-NH4+ -N g-soil-1 day-1 and gross N nitrification rates ranged from 0.55 – 5.90 µg-NO3- -N gsoil-1 day-1 . We determined net N mineralization rates ranged from 0.07 – 1.51 µg-NH4+ -N g-soil-1 day-1 , while net N nitrification rates had a wider range from 1.53 – 25.18 µg-NO3- -N g-soil-1 day-1 . We visually compare the six key N cycling variables—pools of inorganic N , and net and gross N rates—across the three farm types . Despite the variation in net and gross N mineralization and nitrification rates, using the farm types developed above, we found that N cycling variables were not significantly different across the three farm types for all six variables examined—based on ANOVA results . Given the variation in gross N rates reported above, we further explored the drivers of this variation in gross N rates using mixed modelling approaches. Table 10 shows results provide for the linear mixed models used for the prediction of potential gross ammonification rates . Soil ammonium concentration and % sand were significant predictors of gross mineralization rates. While not significant, indicators for SOM were selected and also included in the model, based on AIC results. We also provide results from the selected linear mixed model used for prediction of potential gross nitrification rates in Table 11. As shown, indicators for SOM emerged as the sole significant covariate . While not significant, crop abundance was also selected and included in the model, as determined by AIC results.This on-farm study found significant differentiation among the organic farm field sites sampled based on soil organic matter levels—and created a gradient in soil quality among the three farm types. While we found that differences in soil quality were generally aligned with trends in management among sites, soil texture—rather than management—emerged as the stronger driver of soil quality. Though initially, we found that net and gross N cycling rates were not significantly different across farm types, gross N cycling rates showed considerable variation among farm types. To determine drivers of this variation, we explored key predictors for soil N cycling and found that SOM indicators influenced gross N mineralization and nitrification rates, in particular gross nitrification rates. Each of the four indicators for soil organic matter used in this study—total soil N, total organic C, POXC, and soil protein—showed a strong correlation with farm type, and collectively, created a gradient in soil quality .

Elder flowers are frequently used in medicinal and herbal teas, tonics, liqueurs, lemonades, and sparkling waters for their subtle and unique floral, fruity, and green aromas and medicinal properties. Infusions of elder flowers have been used in many cultures for the treatment of inflammation, colds, fever, and respiratory illness and for their diuretic and antidiabetic effects. Some studies have found evidence to support their use, such as antimicrobial activity of elder flower extract against Gram-positive bacteria and high vitro antioxidant activity. Much of the interest for using elder flower in health-promoting applications is based on the high content of biologically active phenolic compounds in the flowers. European and American elder flowers contain an array of phenolic compounds, such as phenolic acids , flavonols , flavonol glycosides [isorhamnetin-3-O-rutinoside , rutin ], flavan-3-ols [-catechin, -epicatechin], and flavanones. In European-grown elder flowers, the dominant phenolic acid and flavonol glycoside include chlorogenic acid and rutin, although isoquercetin, isorhamnetin-3-rutinoside and kaempferol-3-rutinoside are also present. For example, in a study of European elder flowers grown in different locations and altitudes, the dominant class of phenolic compounds were the flavonols, namely rutin , whereas chlorogenic acid levels were lower. This study also found that the flowers contain four times more chlorogenic acid than the leaves or berries. The predominant phenolic compounds identified in elder flower syrup, a traditional herbal beverage, include chlorogenic acid and rutin . There has been only one study on the phenolic profile of the flowers of S. nigra ssp. canadensis which appears to be similar to the European subspecies, container vertical farming in that rutin and chlorogenic acid are the primary flavonol and phenolic acid identified, respectively. The aroma of the elder flower is derived from the volatile organic compounds in the flower and is an important characteristic to understand for consumer acceptance in applications.

To date, only the VOCs of elder flowers from the European subspecies have been studied. The American subspecies S. nigra ssp. canadensis has not yet been investigated. As fresh flowers are highly perishable, many commercial products rely on dry, and in some cases, frozen flowers. Thus, it is important to understand how the organoleptic properties of elder flowers change in response to processing. The VOC profile of tea made with elder flowers of three European cultivars using dynamic headspace sampling revealed compounds important to the characteristic aroma to be linalool, hotrienol, and cis– and trans-rose oxide. Similarly, studies indicate that in fresh and dried flowers analyzed by headspace solid phase microextraction coupled with gas chromatography mass spectrometry , linalool oxides are the main aroma compounds. Linalool oxide has a floral, herbal, earthy, green odor. In hexane extracts of dry elder flowers analyzed via HS-SPME/GC-MS, cis-linalool oxide and 2-hexanone were the primary volatiles. The compound 2-hexanone has a fruity, fungal, meaty, and buttery odor. In syrups made from elder flowers, terpene alcohols and oxides were identified as the primary aroma compounds. Studies of the impact of drying on volatiles in the flowers demonstrate that nearly all types of drying change the volatile profile significantly. The aim of this study was to characterize the composition of phenolic compounds and VOCs in flowers of the blue elderberry , and to determine how these compounds change in response to drying and in the preparation of teas. Understanding how the aroma and phenolic compounds compare with current commercially available European and American subspecies will help to establish a role for blue elder flowers in commercial applications such as herbal teas and as a flavoring for beverages, as well as identify unique compositional qualities of this native and underutilized flower. LC/MS-grade acetonitrile and HPLC-grade hydrochloric acid were purchased from Fisher Scientific .

Purissimum grade phosphoric acid was purchased from Sigma Aldrich and filtered through 0.45 µm polypropylene filters under vacuum. Ascorbic acid was obtained from Acros Organics . Ultrafiltered water was obtained by a Milli-Q system . Analytical standards of rutin, quercetin, chlorogenic acid, and -catechin were purchased from Sigma Aldrich . A standard of n-butyl-d9 was purchased from CDN Isotopes . Kaempferol-3-O-rutinoside, isorhamnetin-3-O-glucoside, IR, and isoquercetin were purchased from ExtraSynthese . Elderflowers were harvested from hedgerows on a farm in Winters, CA in May and June 2021. The latitude and longitude coordinates of the hedgerow are 38.634884, -122.007502. Flowers were harvested between 8 and 10 am and were picked from all sides of the shrub. Picked flowers were placed in plastic bags, immediately put on ice, and transported to the laboratory at the University of California, Davis. Flowers were either dried at 25 °C for 24 h in a dehydrator or analyzed fresh. Once dry, stems were removed, and flowers were stored in oxygen-impermeable aluminum pouches. Triplicate samples of fresh flowers were analyzed for their moisture content by drying 1 g of fresh flowers at 95 °C until a consistent weight was achieved so that the same amount of dry matter could be used for fresh and dry flower analyses. An aqueous mixture of ethanol was used to extract the phenolic compounds from flowers. The optimal mixture of ethanol to water was determined by extracting flowers in 0, 25, 50, 75, and 100% ethanol. Solvents also contained 0.1% HCl and 0.1% ascorbic acid . For each extraction, 0.25 g dry flower material and 25 mL solvent were added to 50 mL Eppendorf tubes. The dry flowers with solvent were homogenized for 1 min at 7000 rpm with a 19 mm diameter probe head in the 50 mL tubes. Homogenized extracts were refrigerated overnight at 4 °C, then centrifuged at 4000 rpm for 7 min . The supernatant was filtered through 0.45 µm PTFE, then diluted 50% with 1.5% phosphoric acid before analysis. Three replicates were made for each extraction condition .

Phenolics were extracted from fresh and dried flowers that were either whole or homogenized. Hence, four types of samples were made: fresh whole flowers , dry whole flowers , fresh homogenized flowers , and dry homogenized flowers . Flowers were mixed with the determined optimal extraction solvent and followed the same extraction process as described above, except whole flower samples were not homogenized and instead placed directly into the refrigerator to extract overnight. All sample extracts were analyzed via high performance liquid chromatography using an Agilent 1200 system with diode array detection and fluorescence detection . Separation of phenolic compounds was performed on an Agilent PLRP-S column at 35 °C, using a previously published method. Mobile phase A was 1.5% phosphoric acid in water and mobile phase B was 80% acetonitrile, 20% mobile phase A . The flow was set at 1.00 mL min-1 . The gradient used was as follows: 0 min, 6% B, 73 min, 31% B, 78-86 min, 62% B, 90-105 min 6% B. Most phenolic compounds were detected using a at 280 nm , 320 nm , and 360 nm . Flavan-3-ols were detected using a fluorescence detector . Compounds were quantified using external standard curves employing surrogate standards for each group of phenolic compounds [-catechin for flavan-3- ols, chlorogenic acid for phenolic acids and simple phenols, quercetin for flavonol aglycones, and IR for flavonols]. Standards were prepared at concentrations of 200, 100, 50, 10, and 5 mg L -1 , except IR which included an additional concentration of 500 mg L -1 . Triplicate analyses of each concentration were performed . Compounds were separated using HPLC-DAD-FLD as described above and identified using authentic standards to check retention time and absorption spectra. Several peaks in the chromatograms did not match tR or spectra of authentic standards. Therefore, hydroponic vertical garden fractions of these peaks were collected. Fractions were dried and reconstituted in 1% formic acid in water. These samples were then subjected to high resolution mass spectrometry using an Agilent 6545 quadrupole time-of-flight mass spectrometer , using conditions previously established for elderberry phenolic compounds.39 Data were then analyzed using Agilent MassHunter Workstation Qualitative Analysis 10.0 . To tentatively identify compounds, the mass to charge ratio of the precursor and fragment ions were compared to online libraries of compounds and using formula generation for the peaks in the spectra.Volatile compounds were analyzed by headspace solid phase microextraction gas chromatography mass spectrometry . The equilibration and extraction parameters were optimized using ground dry flowers, prepared using a spice grinder, pulsed 25 times . A 1 g sample of ground dry flowers was placed into a 20 mL glass vial and the vial was sealed by a crimp-top cap with a Teflon septa. Various incubation temperatures , equilibration times , and extraction times were evaluated to optimize for the highest total peak area and unique compounds identified from samples. The fiber used for all analyses was a divinylbenzene/carbon wide range/polydimethylsiloxane , 23 Ga, 1 cm length, with 80 µm phase thickness . After extraction, the fiber was injected into the GC and volatile compounds were desorbed at 250 °C for 5 min.

Compounds were then separated on a DB-Wax column . Helium was used as a carrier gas at 1 mL min-1 . A temperature program was used with the following steps: 35 °C for 1 min, 3 °C min- 1 to 65 °C, 6 °C min-1 to 180 °C, 30 °C min-1 to 240 °C, hold at 240 °C for 5 min. Total run time was 37.167 min. Compounds were detected with a single quad, triple axis mass spectrometer . The mass range for acquisition was 30 to 300 m/z. The MS transfer line temperature was 250 °C, the source temperature was 230 °C, and the quad temperature was 150 °C. The electron ionization was set to 70 eV. To have the same volume of headspace in fresh and dry flower samples, 0.5 g of fresh whole flowers or 1.5 g ground dry flowers were placed in the 20 mL clear glass vials. For tea samples, 4 mL tea was placed in 20 mL vials. To each sample, 10 µl of 1-butanol-d9 in methanol was added as an internal standard. Volatile compounds were identified using Agilent Mass Hunter Unknown Analysis , using the NIST17 library requiring an ≥ 80% match and that compounds were identified in at least three of the five to be considered a volatile compound in the samples. An alkane series was run under the same chromatographic conditions to determine retention indices. Confirmation of identification was performed by comparing the mass spectra and retention indices with those of standards when possible or literature values when standards were not accessible. Relative response was calculated by normalizing peak area for each compound to the internal standard peak area, and relative peak area was calculated using the relative response of a compound divided by the total peak area of a sample. The phenolic compounds were measured in fresh and dry elder flowers of S. nigra ssp. cerulea, both as whole and as homogenized flowers. The treatments used for this study were chosen to reflect the common ways that elder flowers are used in food and beverage applications and to provide more information on how to best extract the phenolic compounds from the flowers. The moisture content of the elder flowers was determined as 75.6 ± 1.7%. To achieve a consistent dry weight used in extractions, either 1.00 g of fresh flowers or 0.25 g of dry flowers were used. The extraction solvent was optimized to increase extraction efficiency of the main phenolic acids, flavonols and flavan-3-ols which included chlorogenic acid, IR, rutin, and -catechin . While chlorogenic acid, rutin, and catechin could be extracted in either 50:50% ethanol:water or 25:75% ethanol:water for maximum concentrations, the levels of IR increased with increasing amounts of water in the solvent system. However, in solvents containing ≥ 75% water, the flowers turned brown in color suggesting extensive oxidation. Therefore, it was determined that 50:50% ethanol:water was the optimal solvent for the extraction of the range of phenolic compounds in elder flowers without excess oxidation. A recent study of the effect of organicsolvents on the extraction of phytochemicals from butterfly pea flowers also found that 50:50% ethanol:water had optimal extraction properties for the phenolic compounds in flowers. These results differ from a study on the extract of phenolic compounds from dry, powdered European elder flower, which found water to be the optimal extraction solvent, specifically at 100 °C for 30 mins, as compared to 80:20% ethanol:water or 80:20% methanol:water. 

Growers transitioning to more sustainable production systems need information on how management practices affect the physical properties, health, organic matter and water retention of soil. We monitored soil microbial activity for arbuscular mycorrhizal fungi and soil microbial biomass, since weed control and cover-cropping can affect populations of benefi cial soil microbes in annual crops . Many California growers are also willing to plant cover crops because they protect soil from nutrient and sediment loss in winter storms , suppress weeds , harbor beneficial arthropods , enhance vine mineral nutrition and increase soil organic matter . Competition between vines and cover crops for soil moisture in spring, when both are actively growing, can lead to severe water stress and reduce grape production . However, wine-grape production is distinct from other cropping systems because water stress may be imposed to enhance wine composition ; this practice has been studied mostly in high-rainfall regions of California. The vineyard production region of Monterey County, in contrast, has low rainfall , and growers must weigh the benefits of cover crops with the possible need to replace their water use with irrigation. In addition, round plastic pots growers must decide on the type of vegetation to utilize in the middles. Resident vegetation is cheap and generally easy to manage. Cover crops can provide specific benefits such as nitrogen fixation or high biomass production and vigorous roots .

There are many choices for cover crops in vineyard systems, ranging from perennial and annual grasses, to legumes . Each species has strengths and weaknesses, as well as associated seed and management costs. Row weed control treatments were: cultivation, post-emergence weed control only and pre-emergence herbicide , followed by post-emergence herbicide applications . Cultivations and herbicide applications were timed according to grower practices and label rates. Cultivations were carried out every 4 to 6 weeks during the growing season using a Radius Weeder cultivator . The cultivator used a metal knife that ran 2 to 6 inches below the soil surface cutting weeds off in the vine row; it had a sensor that caused it to swing around vines. Pre-emergence herbicides were applied in winter with a standard weed sprayer, and postemergence herbicides were applied in spring through fall as needed with a Patchen Weedseeker light-activated sprayer . An early and late-maturing cereal were chosen for the cover-crop treatments; legumes were not considered due to aggravated gopher and weed problems. Cover-crop treatments in the middles were: no cover crop , earlier maturing ‘Merced’ rye and later maturing ‘Trios 102’ triticale . Cover crops were planted with a vineyard seed drill in a 32-inch-wide strip in the middle of 8-foot-wide rows just before the start of the rainy season in November 2000 to 2004 . They were mowed in spring to protect vines from frost, and both cover-crop species senesced by summer. Prior to planting cover crops each November, row middles were disked to incorporate the previous year’s cover crop and stubble and prepare a seedbed.

Periodic spring and summer disking kept bare-ground middles free of weeds. Weed control and cover-crop treatments were arranged in a 3 x 3 split block design with three replicate blocks covering a total of 23 vineyard rows . Each block contained six vine rows and six adjacent middles. Weed control treatments were applied along the entire length of each vine row ; cover-crop treatments were established along one-third of each middle and were continuous across the main plot treatments in each block. Each replicate main plot-by-subplot treatment combination included 100 vines. Soil compaction. Soil compaction was measured in the vine row in November or December 2003, 2004 and 2005 with a Field Scout Soil SC-900 compaction meter . Ten sites in each plot were sampled to a depth of 15 inches. Soil moisture. Soil water storage was evaluated from volumetric soil moisture measurements taken in-row and adjacent middles to a depth of 3.5 feet at 1-foot intervals using a neutron probe. The neutron probe readings were calibrated with volumetric moisture measured from undisturbed soil cores collected at the site. Rainfall and runoff. A tipping bucket rain gauge with an 8-inch-diameter collector was used to monitor daily and cumulative rainfall at the field site. Runoff was collected at the lower end of the plots into sumps measuring 16 inches in diameter by 5 feet deep. Each sump was equipped with a device constructed from a marine bilge pump, a float switch and flow meter, to automatically record the runoff volume from the plots during storm events. During the second and third years the sampling devices were modified to collect water samples for sediment and nutrient analysis. Vine mineral nutrition. One-hundred whole leaves opposite a fruit cluster were collected from each plot at flowering in May 2003, 2004 and 2005. Petioles were separated from leaf blades, and tissue was immediately dried at 140°F for 48 hours and then sent to the ANR Analytical Laboratory for nutrient analyses. Petiole and leaf-blade tissue samples were analyzed for nitrate , ammonium , nitrogen , phosphorus , potassium , sulfur , calcium , magnesium , boron , zinc , manganese , iron and copper .

Soil mineral nutrition. Composited samples from 10 soil cores taken to a depth of 1 foot were collected from the vine rows and middles at flowering as described above. Samples were air dried and sent to the ANR Analytical Laboratory for analyses. Soil samples were analyzed for pH, organic matter, cation exchange capacity , nitrate, Olsen-phosphorus, potassium, calcium, magnesium, sodium , chloride , boron and zinc. Soil microbial biomass. Due to the limited capacity of the laboratory, microbial biomass assays were conducted on selected treatments. Ten soil cores were collected to a depth of 1 foot and then composite samples were made from each replicate of the pre-emergence and cultivation weed-control treatments and the adjacent middles of the ‘Merced’ rye and bare treatments. Samples were collected about four times each year from November 2001 to November 2005 for a total of 14 sets of samples. Soil samples were immediately placed on ice and taken to the laboratory for soil microbial biomass carbon analysis according Vance et al. . Mycorrhizae. Roots were collected, stained and examined as previously reported on April 16, 2003, May 3, 2004, and June 2, 2005. Grape yield, fruit quality and vine growth. Fruit weight and cluster number were determined by individually harvesting 20 vines per subplot. Prior to harvest a 200-berry sample was collected from each subplot for berry weight and fruit composition. Berries were macerated in a blender and the filtered juice analyzed for soluble solids as Brix using a hand-held, temperature compensating refractometer. Juice pH was measured by pH meter and titratable acidity by titration with a 0.133 normal sodium hydroxide to an 8.20 pH endpoint. At dormancy, shoot number and pruning weights were measured from the same 20 vines. Statistical analysis. Analyses of variance were used to test the effects of cover crop, weed control and year on the vine, soil and microbial parameters, according to a split-block ANOVA model in SAS . Cover crop, weed control, year and their interactions were treated as fixed effects. The main and interactive effects of block were treated as random effects. Year was treated as a repeated measure. When necessary, data were log-transformed to meet the assumption of normality for ANOVA, although untransformed or reverse transformed means are presented. Changes in soil moisture among treatments during the winter and the irrigation seasons were determined from significant treatment-date interactions.We conducted evaluations with a penetrometer each fall to determine the impact of weed-control treatments on soil compaction. Soil compaction was not significantly different at any depth in 2003 . However, in 2004 and 2005 soil compaction began to increase in the cultivation treatment compared to the other two weed-control treatments. In 2004, hydroponic bucket soil compaction at the 4- to 7-inch depth was significantly greater in the cultivation treatment compared to the standard treatment , but not more so than in the post-emergence treatment . In 2005, the cultivation treatment had significantly greater soil compaction at the 4- to 7-inch depth than both the post emergence and standard weed-control treatments . At the 8- to 11-inch depth, soil compaction was significantly greater than the standard treatment , but not greater than in the post-emergence treatment . The blade of the cultivator passes through the soil at 2 to 6 inches deep, which may explain why greater soil compaction was measured there. Cultivations often also occurred when the soil was still moist following an irrigation, which may have contributed to the development of compacted layers over time.Moisture. Average, volumetric soil moisture levels at the 6- to 42-inch depth increased after the first rain events of the season, such as in winter 2002-2003 . Soil moisture declined most rapidly with ‘Merced’ rye in the middles during periods without rainfall each year , presumably due to its greater early-season growth and greater potential evapotranspiration, compared to the ‘Trios 102’ triticale. Soil moisture levels were similar between the bare and ‘Trios 102’ triticale treatments until May for all years. During the irrigation season, average soil moisture levels at the 6- to 42-inch depths were higher in rows than middles. Soil moisture in the rows and middles steadily declined during the irrigation season for all treatments during all years . Moisture levels declined most in middles with ‘Trios 102’ triticale cover during each irrigation season, presumably due to the later growth of this cover crop .

In addition, the row soil-moisture levels also declined the most adjacent to ‘Trios 102’ triticale for the 2003 and 2004 irrigation seasons , but not during the 2005 irrigation season . Runoff. Total precipitation at the field trial was 7.4 inches during the 2002-2003 winter, 7.6 inches during the 2003-2004 winter and 9.9 inches during the 2004-2005 winter. A majority of the runoff was collected during December and January for the 2002-2003 and 2004-2005 winters, and February for the 2003-2004 winter. Cumulative runoff collected from individual plots during the three winters ranged from 0.02% to 3% of seasonal rainfall. Runoff was usually collected during rain events greater than 1 inch per day. Runoff was highest during the second and third years of the trial. During three consecutive winters, runoff was significantly lower in the covercrop treatments . ‘Trios 102’ triticale and ‘Merced’ rye had significantly less runoff than the bare treatment . Suspended sediment and turbidity were also significantly lower in runoff collected from the cover-crop treatments than in bare middles during winter 2004, but nutrient levels were similar among all treatments .Weed control and cover treatments did not have any significant effect on the nutritional status of the grape vines as measured by nutrient levels of the leaf petiole tissues, as determined by ANOVA. Although the nutrient levels by year were significantly different, the interactions of weed control-by-cover and weed control-by cover-by-year were not significant . Weed control and cover treatment also had no significant effect on blade nutrient content with the exception of boron and phosphate content. Vines adjacent to cover crops had significantly lower boron and phosphate levels in the leaf blade tissue than vines adjacent to bare row middles. As with the petioles, there was an absence of significance between the interaction of weed control-by-cover and weed control-by-cover-by-year for all nutrients analyzed .Soil cores indicated that most of the vine roots at this site were located under the vine row and few of the roots extended out to the row middles. This root distribution probably occurred because irrigation water was applied under the vines, and low rainfall at the site does not facilitate root growth into row middles. Thus, the lower nutrient levels in vines near cover crops may have been accentuated by irrigation effects that reduced vine root exploration of the soil to a narrow band under the vines. Since cover-crop roots probably grew into this zone there may have been competition between vines and cover crops for some nutrients. Soil. Cultivated rows had significantly lower levels of nitrate-nitrogen . Although the nutrient levels by year were significantly different, there was an absence of significance between the interaction of weed control-by-cover and weed control-by-cover-by-year . The differences observed in nitrate-nitrogen in the cultivation treatment may be due to the impact of loosening soil on water movement and leaching. Weed control treatments had occasional impacts on soil mineral nutrition in the middles, but results were inconsistent from year to year .

Most metabolic pathways that promote desired quality traits in grape berries are induced during ripening. The onset of ripening is accompanied by significant changes in berry physiology and metabolism, including softening, sugar accumulation, decrease in organic acids, and synthesis of anthocyanins and other secondary metabolites that define the sensory properties of the fruit . Berry ripening is controlled by multiple regulatory pathways, and occurs in an organized and developmentally timed manner. Interactions between transcriptional regulators and plant hormones regulate the initiation and progression of ripening processes . Like other non-climacteric fruit, grape berries do not display a strong induction of ethylene production and respiration rate at véraison, and the activation of ripening events does not depend primarily on ethylene signaling. Even though the hormonal control of grape berry development is not completely understood, it is established that abscisic acid , brassinosteroids, and ethylene are positive regulators of ripening processes, while auxin delays the initiation of ripening . In the context of virus–grape berry interactions, dissecting the mechanisms that regulate ripening and plant defenses may provide new opportunities to develop vineyard management strategies to control viral diseases and ameliorate the negative effects on berry quality. In this study, we integrated genome-wide transcriptional profiling, stackable planters targeted chemical and biochemical analyses, and demonstrated that grapevine red blotch disrupts ripening and metabolism of red-skinned berries.

We sampled berries at different ripening stages from vines infected with GRBaV and healthy vines in two vineyards. We identified grape metabolic pathways that were altered in ripening berries because of the viral infection. We determined that GRBaV-induced pathways that are normally associated with early fruit development in berries at late stages of ripening, and suppressed secondary metabolic pathways that occur during normal berry ripening and/ or in response to stress. Using targeted metabolite profiling and enzyme activity analyses, we confirmed the impact of GRBaV on phenylpropanoid metabolism. Remarkably, these processes included alterations in ripening regulatory networks mediated by transcriptional factors, post-transcriptional control, and plant hormones, which lead to berry developmental defects caused by red blotch.To determine the impact of grapevine red blotch on berry physiology, we studied naturally occurring GRBaV infections in distinct wine grape-growing regions in northern California . We sampled red-skinned grape berries from two different vineyards, one in Oakville and one in Healdsburg . We used multiple vineyard sites to focus on observations consistently made across environments and, thus, to exclude factors associated with specific environmental or cultural conditions. Prior to sampling, vines were screened for the presence of GRBaV and other common grapevine viruses. The appearance of red blotch symptoms on leaves of GRBaV-positive vines and not on those of healthy controls confirmed the initial viral testing. We sampled grape berries from vines that tested positive for GRBaV and negative for other common grapevine viruses. At the same time, we also collected berries from vines that tested negative for all viruses and included them in the study as healthy controls. In order to determine the impact of the disease on berry development and metabolism, we collected GRBaV-positive and control berries at comparable developmental stages: pre-véraison , véraison , post-véraison , and harvest . This sampling strategy also aimed to limit confounding effects due to differences in the progression of ripening between berry clusters of GRBaV-positive and healthy vines. In some cases, we observed that GRBaV-positive vines presented grape clusters with evident uneven ripening .

Comparisons between berries from GRBaV-positive vines and healthy controls indicated that, at equivalent stages of development, berries affected by red blotch had reduced soluble solids and total anthocyanins in agreement with previous reports on red-skinned wine grapes . Sampled berries were used for genome-wide transcriptional profiling of viral and grape genes. RNAseq was performed using 3–4 biological replicates of each ripening stage, infection status, and vineyard. We first confirmed the presence of the virus in the berries of GRBaV-positive vines by qPCR amplification of viral DNA . Viral activity in the berries was also assessed by quantifying plant-derived mRNA transcripts of GRBaV genes in the RNAseq data. Plant expression of five out of the six predicted genes in the GRBaV genome was detected in all berry samples obtained from GRBaV-positive vines but not in berries collected from the control vines . The most expressed GRBaV genes in the berries corresponded to V1, which encodes a coat protein, and V3 with unknown function. Expression levels of the GRBaV genes appeared to change as berries ripened. However, we could not determine to what extent the progression of ripening or other environmental factors influenced the plant’s transcription of viral genes because their pattern of variation between ripening stages differed in the two vineyards . Expression of 25 994 grape genes was detected by RNAseq across all berry samples. Principal component analysis was carried out with the normalized read counts of all detected genes. The two major PCs, which together accounted for 42.97% of the total variability, clearly separated the samples based on ripening stage, regardless of the vineyard of origin or their infection status . These results indicated that the intervineyard variation was smaller than the ripening effect, and the overall progression of ripening was similar between berries from GRBaV-positive and control vines.

Therefore, we hypothesized that GRBaV infections in berries have altered the expression of particular grape genes and/or molecular pathways, which could subsequently have led to developmental and metabolic defects.While the PCA described above indicated that overall transcriptome dynamics associated with berry ripening were not perturbed by the infection, the lower levels of soluble solids and anthocyanins in GRBaV-positive berries, particularly later in development, suggested that red blotch may affect specific primary and secondary metabolic processes. We therefore focused the RNAseq analyses to identify grape molecular pathways that were differentially regulated as a result of GRBaV infections. We identified grape genes with significant differential expression due to red blotch by comparing GRBaV-positive and GRBaV-negative berries at each ripening stage and independently for each vineyard. We then looked at the intersection of differentially expressed genes between the two vineyards to identify common responses to red blotch. A total of 932 grape DE genes were found to be consistently down- or up-regulated in infected berries in both vineyards at a given ripening stage, and were classified as GRBaV-responsive genes . On average these GRBaV-responsive genes showed 0.49 ± 0.22-fold changes compared with the healthy controls. Comparing berries at similar ripening stages may have contributed to exclude more dramatic changes in gene expression associated with more pronounced ripening delay due to GRBaV. Key metabolic processes that were suppressed or induced as a consequence of red blotch in ripening berries were identified by enrichment analyses of the functional categories defined by Grimplet et al. in the set of GRBaV-responsive genes . GRBaV infections altered the transcription of several primary metabolic pathways. Amino acid biosynthetic pathways were repressed in GRBaV-positive berries, while amino acid catabolic pathways were induced. Changes in carbohydrate metabolism were also observed; in particular, genes involved in glycolysis/gluconeogenesis and starch metabolism had reduced expression in GRBaV-infected berries. Genes involved in nucleic acid metabolism, including RNA processing and surveillance, showed higher expression in GRBaV-infected berries. These pathways coincided at véraison with the induction of genes involved in stress responses to virus . RNA metabolic pathways are commonly altered in plants during viral infections and have been related to resistance or susceptibility depending on the particular plant–virus interaction . Red blotch also impacted the transcription of several abiotic stress response pathways. In particular, berries after véraison showed lower expression of genes encoding hypoxia responsive proteins and heat stress transcription factors, among others . A prominent feature of the GRBaV-positive berries was the transcriptional suppression of secondary metabolic pathways, stacking pots in particular the biosynthesis of phenylpropanoids, stilbenoids, and lignin . Because the lower anthocyanin content observed in the GRBaV-positive berries may have resulted from reduced metabolic flux in the core phenylpropanoid pathway and alterations in flavonoid and anthocyanin biosynthesis, we pursued a deeper evaluation of these pathways using an integrated approach of transcriptional and metabolite profiling coupled to enzymatic analyses.Most enzymes involved in phenylpropanoid metabolism are encoded by large gene families. There is also high redundancy among these genes, which ensures the functional integrity and plasticity of the phenylpropanoid-related pathways . Therefore, to test the hypothesis that the red blotch-induced transcriptional changes had an actual impact on phenylpropanoid metabolism, we measured the activity of key enzymes and the abundance of compounds involved in these pathways . We detected significant reductions in activity of seven enzymes that catalyze important steps in the core phenylpropanoid, stilbene, flavonoid, and anthocyanin biosynthetic pathways due to GRBaV infections of berries at three ripening stages . In addition, the first enzyme committed to flavone and flavonol biosynthesis, flavonol synthase , had significantly lower activity at post-véraison and harvest stages .

Red blotch altered the accumulation of 17 compounds that result from the phenylpropanoid metabolism and two compounds upstream of this pathway, shikimic acid and gallic acid . Most of these compounds showed significantly lower abundance in the GRBaV-positive berries compared with the controls at later stages of ripening. The main anthocyanins present in grape berries: malvidin-3-O-glucoside, petunidin-3-O-glucoside, delphinidin-3-O-glucoside, pelargodin-3-O-glucoside, and cyanidin-3-O-glucoside, were significantly reduced by red blotch at harvest. Gallic acid, sinapic acid, and quercetin also showed lower abundance in infected berries. Few exceptions to this general suppression of phenolic accumulation were the accumulation of the precursor shikimic acid, which significantly increased in infected berries at harvest, and of resveratrol that showed significantly greater accumulation at véraison and postvéraison. Additional experiments are necessary to understand the accumulation of these two metabolites in the presence of GRBaV: preliminarily, we can hypothesize that the higher abundance of resveratrol is due either to a restriction of subsequent enzymatic steps in stilbene metabolism for which this compound is a substrate, or to the enzymatic hydrolysis of resveratrol glycosides or stilbenoid dimers previously synthesized. The integrated analysis of transcriptomic, metabolite, and enzyme activity data supported a general repression of the core and peripheral phenylpropanoid pathways, which are normally triggered in red-skinned berries throughout ripening and in response to stress . These results suggest that GRBaV infections disrupt secondary metabolic pathways by altering the regulation of berry ripening processes and/or signaling mechanisms related to plant defense. Interestingly, GRBaV infections seemed to have a more a pronounced impact on enzymatic activities and metabolite accumulation than on the expression levels of the genes in the pathway, which in general displayed small fold change differences between healthy and infected samples. This observation further confirms the importance of evaluating metabolic perturbations at multiple regulatory levels.Understanding how plants respond to external stimuli in the field is crucial to improve agricultural traits under naturally fluctuating conditions. Most studies on plant–pathogen interactions are performed with model organisms in the greenhouse or laboratory, which reduce the confounding effects of the environment, but also challenge the reproducibility of the results in the field . Compatible plant–virus interactions in perennial woody crops are complex due to the presence of multiple and systemic infections,tissue and developmental stage-specific responses, differences between species and cultivars, and the combination of biotic and abiotic factors during the crop season . The application of a system biology approach to study red blotch under multiple vineyard conditions allowed us to explore grapevine responses to GRBaV infections in real agronomic settings and to characterize the influence of viral activity on berry physiology. In almost all viral diseases occurring in the vineyard, the virus is distributed systemically throughout the grapevine. Once introduced in the host, viral particles move rapidly within the vascular tissue towards sink tissues and establish infections . Although we detected the presence of GRBaV in vegetative and berry tissues during growing and harvest seasons, symptoms of red blotch were only evident after véraison, which suggests that the disease onset is mostly dependent on grapevine phenology and not necessarily linked to viral accumulation. Similar observations have been made during grapevine leafroll disease, supporting the hypothesis that the appearance of viral disease symptoms in the vineyard may result from the interaction between pathogen and host cellular factors at specific phenological stages . Whether GRBaV is able to modulate its infection strategy as a function of plant development and/ or grapevines have distinct responses to red blotch throughout the season remains to be resolved. GRBaV shares several similarities with geminiviruses, including a small singlestranded DNA genome that encodes six potential proteins .

In vineyards, studies in California in the late 1990s have reported net primary productivity or total biomass values between 550 g C m−2 and 1100 g C m−2. In terms of spatial distribution, some data of standing biomass collected by Kroodsma et al. from companies that remove trees and vines in California yielded values of 1.0–1.3 Mg C ha−1  year−1 woody C for nuts and stone fruit species, and 0.2–0.4 Mg C ha−1  year−1 for vineyards. It has been reported that mature California orchard crops allocate, on average, one third of their NPP to the harvested portion and mature vines 35–50% of the current year’s production to grape clusters. Pruning weight has also been quantified by two direct measurements which estimated 2.5 Mg of pruned biomass per ha for both almonds and vineyards. The incorporation of trees or shrubs in agroforestry systems can increase the amount of carbon sequestered compared to a monoculture field of crop plants or pasture. Additional forest planting would be needed to offset current net annual loss of above ground C, representing an opportunity for viticulture to incorporate the surrounding woodlands into the system. A study assessing C storage in California vineyards found that on average, surrounding forested wild lands had 12 times more above ground woody C than vineyards and even the largest vines had only about one-fourth of the woody biomass per ha of the adjacent wooded wildlands .The objectives of this study were to: measure standing vine biomass and calculate C stocks in Cabernet Sauvignon vines by field sampling the major biomass fractions ; calculate C fractions in berry clusters to assess C mass that could be returned to the vineyard from the winery in the form of rachis and pomace; determine proportion of perennially sequestered and annually produced C stocks using easy to measure physical vine properties ; and develop allometric relationships to provide growers and land managers with a method to rapidly assess vineyard C stocks.

Lastly, we validate block level estimates of C with volumetric measurements of vine biomass generated during vineyard removal.The study site is located in southern Sacramento County, California, USA , nft channel and the vineyard is part of a property annexed into a seasonal floodplain restoration program, which has since removed the levee preventing seasonal flooding. The ensuing vineyard removal allowed destructive sampling for biomass measurements and subsequent C quantification. The vineyard is considered part of the Cosumnes River appellation within the Lodi American Viticultural Area, a region characterized by its Mediterranean climate— cool wet winters and warm dry summers—and by nearby Sacramento-San Joaquin Delta breezes that moderate peak summer temperatures compared to areas north and south of this location. The study site is characterized by a mean summer maximum air temperature of 32 °C, has an annual average precipitation of 90 mm, typically all received as rain from November to April. During summer time, the daily high air temperatures average 24 °C, and daily lows average 10 °C. Winter temperatures range from an average low 5 °C to average high 15 °C. Total heating degree days for the site are approximately 3420 and the frost-free season is approximately 360 days annually. Similar to other vineyards in the Lodi region, the site is situated on an extensive alluvial terrace landform formed by Sierra Nevada outwash with a San Joaquin Series soil . This soil-landform relationship is extensive, covering approximately 160,000 ha across the eastern Central Valley and it is used extensively for winegrape production. The dominant soil texture is clay loam with some sandy clay loam sectors; mean soil C content, based on three characteristic grab samples processed by the UC Davis Analytical Lab, in the upper 8 cm was 1.35% and in the lower 8–15 cm was 1.1% .

The vineyard plot consisted of 7.5 ha of Cabernet Sauvignon vines, planted in 1996 at a density of 1631 plants ha−1 with flood irrigation during spring and summer seasons. The vines were trained using a quadrilateral trellis system with two parallelcordons and a modified Double Geneva Curtain structure attached to T-posts . Atypically, these vines were not grafted to rootstock, which is used often in the region to modify vigor or limit disease .In Sept.–Oct. of 2011, above ground biomass was measured from 72 vines. The vineyard was divided equally in twelve randomly assigned blocks, and six individual vines from each block were processed into major biomass categories of leaf, fruit, cane and trunk plus cordon . Grape berry clusters were collected in buckets, with fruit separated and weighed fresh in the field. Leaves and canes were collected separately in burlap sacks, and the trunks and cordons were tagged. Biomass was transported off site to partially air dry on wire racks and then fully dried in large ventilated ovens. Plant tissues were dried at 60 °C for 48 h and then ground to pass through a 250 μm mesh sieve using a Thomas Wiley® Mini-Mill . Total C in plant tissues was analyzed using a PDZ Europa ANCA-GSL elemental analyzer at the UC Davis Stable Isotope Facility. For cluster and berry C estimations, grape clusters were randomly selected from all repetitions. Berries were removed from cluster rachis. While the berries were frozen, the seeds and skins were separated from the fruit flesh or “pulp”, and combined with the juice . The rachis, skins and seeds were dried in oven and weighed. The pulp was separated from the juice + pulp with vacuum filtration using a pre-weighed Q2 filter paper .

The largest portion of grape juice soluble solids are sugars. Sugars were measured at 25% using a Refractometer PAL-1 . The C content of sugar was calculated at 42% using the formula of sucrose. Below ground biomass was measured by pneumatically excavating the root system with compressed air applied at 0.7 Mpa for three of the 12 sampling blocks, exposing two vines each in 8 m3 pits. The soil was prewetted prior to excavation to facilitate removal and minimize root damage. A root restricting duripan, common in this soil, provided an effective rooting depth of about 40 cm at this site with only 5–10 fine and small roots able to penetrate below this depth in each plot. Roots were washed, cut into smaller segments and separated into four size classes , oven-dried at 60 °C for 48 h and weighed. Larger roots were left in the oven for 4 days. Stumps were considered part of the root system for this analysis.In vineyard ecosystems, hydroponic nft annual C is represented by fruit, leaves and canes, and is either removed from the system and/or incorporated into the soil C pools, which was not considered further. Structures whose tissues remain in the plant were considered perennial C. Woody biomass volumes were measured and used for perennial C estimates. Cordon and trunk diameters were measured using a digital caliper at four locations per piece and averaged, and lengths were measured with a calibrated tape. Sixty vines were used for the analysis; twelve vines were omitted due to missing values in one or more vine fractions. All statistical estimates were conducted in R .An earth moving machine was used to uproot vines and gather them together to form mounds. Twenty-six mounds consisting of trunks plus cordons and canes were measured across this vineyard block . The mounds represented comparable spatial footprints within the vineyard area . Mound C stocks were estimated using their biomass contribution areas, physical size, density and either a semi-ovoid or hemispherical model.A real-time kinematic global positioning system was used to map boundaries of each mound, with vertices placed every 1.5 m to measure circumference. Average mound height was calculated using a stadia rod and laser inclinometer range finder. The circumsurficial distance over the major axes of each mound was measured with a calibrated cord. Combined, these measurements were used to estimate pile volume using semi-ovoid and hemispherical models .The present study provides results for an assessment of vineyard biomass that is comparable with data from previous studies, as well as estimates of below ground biomass that are more precise than previous reports. While most studies on C sequestration in vineyards have focused on soil C, some have quantified above ground biomass and C stocks. For example, a study of grapevines in California found net primary productivity values between 5.5 and 11 Mg C ha−1 —figures that are comparable to our mean estimate of 12.4 Mg C ha−1 . For pruned biomass, our estimate of 1.1 Mg C ha−1 were comparable to two assessments that estimated 2.5 Mg of pruned biomass ha−1 for both almonds and vineyards.

Researchers reported that mature orchard crops in California allocated, on average, one third of their NPP to harvestable biomass, and mature vines allocated 35–50% of that year’s production to grape clusters. Our estimate of 50% of annual biomass C allocated to harvested clusters represent the fraction of the structures grown during the season . Furthermore, if woody annual increments were considered this proportion would be even lower. Likewise the observed 1.7 Mg ha−1 in fruit represents ~14% of total biomass , which is within 10% of other studies in the region at similar vine densities. More importantly, this study reports the fraction of C that could be recovered from winemaking and returned to the soil for potential long term storage. However, this study is restricted to the agronomic and environmental conditions of the site, and the methodology would require validation and potential adjustment in other locations and conditions. Few studies have conducted a thorough evaluation of below ground vine biomass in vineyards, although Elderfield did estimate that fine roots contributed 20–30% of total NPP and that C was responsible for 45% of that dry matter. More recently, Brunori et al. studied the capability of grapevines to efficiently store C throughout the growing season and found that root systems contributed to between 9 and 26% of the total vine C fixation in a model Vitis vinifera sativa L. cv Merlot/berlandieri rupestris vineyard. The results of our study provide a utilitarian analysis of C storage in mature wine grape vines, including above and below ground fractions and annual vs. perennial allocations. Such information constitutes the basic unit of measurement from which one can then estimate the contribution of wine grapes to C budgets at multiple scales— fruit, plant or vineyard level—and by region, sector, or in mixed crop analyses. Our study builds on earlier research that focused on the basic physiology, development and allocation of biomass in vines. Previous research has also examined vineyard-level carbon at the landscape level with coarser estimates of the absolute C storage capacity of vines of different ages, as well as the relative contribution of vines and woody biomass in natural vegetation in mixed vineyard-wildland landscapes. The combination of findings from those studies, together with the more precise and complete carbon-by-vine structure assessment provided here, mean that managers now have access to methods and analytical tools that allow precise and detailed C estimates from the individual vine to whole-farm scales. As carbon accounting in vineyard landscapes becomes more sophisticated, widespread and economically relevant, such vineyard-level analyses will become increasingly important for informing management decisions. The greater vine-level measuring precision that this study affords should also translate into improved scaled-up C assessments . In California alone, for example, there are more than 230,000 ha are planted in vines. Given that for many, if not most of those hectares, the exact number of individual vines is known, it is easy to see how improvements in vine-level measuring accuracy can have benefits from the individual farmer to the entire sector. Previous efforts to develop rough allometric woody biomass equations for vines notwithstanding, there is still a need to improve our precision in estimating of how biomass changes with different parameters. Because the present analysis was conducted for 15 year old Cabernet vines, there is now a need for calibrating how vine C varies with age, varietal and training system. There is also uncertainty around the influence of grafting onto rootstock on C accumulation in vines. As mentioned in the methods, the vines in this study were not grafted—an artifact of the root-limiting duripan approximately 50 cm below the soil surface.

The farms in the sample that monocrop do so on the vast majority of their farm, not just on specific plots. In each survey year over half of the plots being monocropped are growing maize, with approximately 10% each growing beans and coffee. As shown in Table B.3.1 of Appendix B.3, there is no discernible difference in monocropping across farm sizes, although ejido farms are marginally more likely to employ monocropping than non-ejido farms. To account for potentially persistent negative productivity shocks we generate a dummy variables for whether the household suffered crop or livestock loss in either of the previous two years. The MxFLS asks households about their participation in a variety of government programs. The two most important programs are Progresa/Oportunidades and Procampo. Procampo is an income transfer program designed to support agricultural producers of staple crops. Progresa, later renamed Oportunidades, is a conditional cash transfer program designed to combat poverty and incentivize investments in children. Data limitations do not allow us complete information on participation in Progresa14 so we focus exclusively on participation in Procampo. Table B.3.1 in Appendix B.3 shows the share of farms participating in Procampo by year and farm size. With the exception of the largest farms, participation increases with farm size. In addition, nft system we consider participation in Alianza, a government-run program designed to aid farmers’ transition into crops for export.

While less than 3% of the sample participated in this program in any survey round, we consider participation in this program for its potentially important impact on farmers. Having access to credit is an important determinant of agricultural productivity, and the existence of credit constraints and differential access to credit is one theoretical source of a relationship between farm size and productivity. Table B.3.3 in Appendix B.3 shows “access to credit” by farm size, where a household is considered to have access if the household head knows where they can go to borrow or ask for a loan. This is a crude measure as it does not account for credit rationing and the likelihood that a household could succeed in obtaining a loan. A follow up question regarding the source of that credit allows us to identify if access is through a formal or an informal financial institution. There are no clear relationships between farm size and this measure of access to credit. We introduce an indicator variable to control for access to formal lines of credit.As with much of the literature, we begin the discussion of the farm size – productivity relationship using land productivity, measured as output per hectare. Figure 2.1 shows the non-parametric relationship between the log of farm size and the log of output per hectare in 2002, where output is measured using the Fisher quantity index. There is a clear inverse relationship between farm size and land productivity over the entire range of farm sizes, and while not shown here this relationship is strikingly consistent across the three survey waves. Land productivity falls rapidly up to approximately 1 ha, at which point the relationship levels before resuming a dramatic decline in land productivity after approximately 20 ha.As shown in chapter 1, an inverse relationship between farm size and land productivity is neither necessary nor sufficient for the existence of an inverse relationship between farm size and total factor productivity. For reference, the linear relationship between land productivity and farm size is estimated.

Farm size is inversely related to land productivity at the 1% level of significance, as shown in column 1 of Table 2.6, where we estimate the elasticity of land productivity with respect to farm size to be -0.82. We then estimate the average production function identified by equation assuming four alternate specifications of the farm size – productivity relationship that vary in their flexibility. These regressions measure output using the quantity index, weight observations by the expansion factors provided by MxFLS, use the preferred measure of the family labor index, employ community fixed effects, and cluster standard errors at the community level. Coefficients for the farm size variables, the primary variables of interest, are displayed in Table 2.6. Table 2.7 displays the coefficients for additional household controls, and technology coefficients are included as Table B.4.1 of Appendix B.4. The results indicate an inverse relationship between farm size and TFP, as shown by the negative and statistically significant coefficient on the linear Farm Size variable in model 2. In the sample, a 1% increase in farms size is associated with a 0.81% decrease in output per hectare, ceteris paribus. The farm size coefficient is slightly less negative than in model 1, but not statistically different, indicating that the relationships between farm size and land productivity and farm size and TFP are almost identical in this sample. Models 3 and 4 allow for a quadratic and cubic relationship between farm size and TFP, but the coefficients on the higher ordered terms are either not statistically significant or do not have a noticeable impact on the linear model. Model 5 captures some nonlinearity in the farm size – TFP relationship by using dummy variables for 7 farm size bins.The smallest of farms, those less than one half of a hectare, are significantly more productive than all other farms, while the largest, those greater than 20 hectares, are significantly less productive than all smaller farms.

Productivity between these two extremes, however, appears relatively stable. This closely mirrors the non-parametric relationships between farm size and land productivity shown in Figure 2.1, highlighting the need to assume a flexible functional form to fully understand the farm size – productivity relationship. The linear relationships identified in the parametric specifications 2 through 4 do not capture these subtleties. We see little change in the inverse relationship over time across all models, as none of the farm size and survey year interaction terms are statistically significant. The finding of a time invariant inverse relationship between farm size and productivity – when using both land productivity and TFP – suggests that the IR is alive and well in Mexico. There is, however, evidence for a decline in average productivity over time in this sample, as the 2009 dummy variable is negative and statistically significant. Results for the household explanatory variables, displayed in Table 2.7, show that monocropping and operating as a subsistence farm have a consistently negatively relationship with TFP. In contrast, participating in Procampo is positively associated with productivity . It is important to reiterate that these are potentially endogenous explanatory variables, hydroponic gutter and we should not interpret the coefficients as identifying causal relationships. Having more education is positively related to TFP, but with the exception of a college education these results are not consistently statistically significant at standard levels. Estimates of equation explore heterogeneity in the farm size – productivity relationship across different groups of Mexican family farms by interacting indicator variables for those groups with farm size. For simplicity, we assume the farm size – TFP relationship to be linear and time invariant. 17 Table 2.8 displays the results from interacting farm size with being located in the more commercially oriented agricultural region of Northern Mexico, participation in Procampo, practicing monocropping, operating as a subsistence farm, and whether or not the household head has any education beyond secondary school. Overall, the farm size – TFP relationship remains stable, as none of these additional interactions contribute to explaining the farm size – TFP relationship that we have identified. 18 In addition, we interact controls for farms having ejido status, various forms of property rights, and access to credit in Table 2.9. These are of special interest given the reforms of the ejido system and rural credit markets. Again, the IR is unaltered across these subgroups as these interactions are not statistically significant. The relationship between farm size and TFP is the same for ejido farms as for non-ejido farms, is the same regardless of how property rights are documented, and is the same whether or not farms have access to formal credit markets.The farm-size – TFP relationship is subjected to a series of robustness tests. We assume the farm size – TFP relationship is best captured by the linear and dummy variable models used above, as the quadratic and cubic models provide little additional information. Table 2.10 contains the results from the linear models and Table 2.11 from the dummy variable specification. First, model 1 introduces household-level fixed effects to control for timeinvariant, unobserved, household heterogeneity. The model omits time-invariant household controls, clusters standard errors at the household level, and provides a superior approach to addressing potential omitted variable biasrelative to the model with community level fixed effects. Second, model 2 tests the sensitivity of the relationship to decisions regarding the construction of the family labor index by using an alternative index of family labor described in Appendix B.2. Third, we test the impact of choice of weighting of the observations.

Whereas the core results apply the MxFLS weights designed to make the sample statistically representative of Mexican households in each survey year, model 3 shows results when we apply no weighting at all. We explore sensitivity to the use of weights because we are interested in Mexican agriculture, not rural Mexican households, and the treatment of the data reduces the sample size; therefore, it is not clear that these weights remain appropriate. Fourth, model 4 uses an alternative measure of the dependent variable – farm output. Whereas the core results uses the preferred approach of calculating a quantity index for each household , model 4 deflates the nominal value of production in each year for each household and uses the real value of output . Lastly, model 5 uses the real value of output as in model 4, but estimates the relationship over the repeated cross-sections. This final robustness check speaks to the potential for households to be selecting into or out of the unbalanced panel. Overall, these alternative treatments of the data generate qualitatively similar results to the core regressions in Table 2.6 for our primary variables of interest. This is true in terms of the coefficient signs and orders of magnitude. The exception is model 2 using the alternative index of family labor, for which the farm size coefficients are diminished in magnitude although negative and still statistically significant. The consistency across models is reassuring that treatment of the data is not driving the core results regarding the farm size – TFP relationship. In similar fashion, estimated coefficients on household explanatory variables are quite robust. The coefficients identifying farms engaged in monocropping and operating as subsistence farms remain negative and statistically significant in almost all of the robustness exercises, while the coefficients for participation in Procampo and college education remain positive and statistically significant. In results not shown here, we estimate the core models using crop production only in measuring output and the conclusions regarding the farm size – productivity relationship are robust to this dimension as well.Estimating a stochastic frontier complements analysis of the average production function by identifying productivity at the frontier and production inefficiencies. Together, these components determine average TFP identified with the average production function. In similar fashion, whereas the estimation of the average production function allows us to assess the relationship between farm size and average productivity, stochastic frontier analysis allows us to assess any relationships between farm size and productivity at the technical frontier and between farm size and technical inefficiency. The results of five specifications of the stochastic production frontier are shown in Table 2.12, with the top and bottom panels displaying the results from the frontier and variance of inefficiency equations, respectively. Model 1, the baseline model, has no additional household controls in either the frontier or the inefficiency equations . Model 2 includes dummy variables for the household head’s level of education in the frontier equation and includes a dummy variable for the household head being of indigenous ethnicity in the inefficiency equation. Model 3 alternatively assumes that education of the household head should be included as a control in the inefficiency equation but not the frontier equation. Model 4 assumes that education belongs in both equations. Model 5 includes education in the frontier equation only, adding interaction terms between farm size and the survey year dummies in both the frontier and the inefficiency equations.

In 1998, there were no yield differences among the vegetable rotation plots; however, in 2000,broccoli rotation plots had the highest and lettuce plots had the lowest strawberry yield, with the yield in cauliflower rotation plots being intermediate. Strawberry production under fumigation incurred the highest production costs but also provided the highest returns. Average total cost of production in 1998 and 2000 was estimated to be $81,000 per hectare with a net profit of $10,500 per hectare . In contrast, the cost of strawberry production without fumigation decreased to an estimated $77,000 to 79,000 but also led to losses between $17,000 and 19,000 depending on the production site . Production of strawberry under crop rotation involved giving up the annual strawberry production during the time rotation crops were grown but resulted in net profits because of income from rotation crops and higher strawberry yield. However, the overall profits were reduced by 20 to 30% a year relative to the production under fumigation. The total cost of producing strawberry following two crops of broccoli was estimated to be nearly $82,000 that resulted in a net profit of $6,800 to 7,800 per hectare per year, depending on location . This study demonstrated that rotations with broccoli and Brussels sprouts followed by the post harvest incorporation of the respective residues reduced the number of V. dahliae microsclerotia in soil that resulted in concomitant reductions in the incidence of Verticillium wilt and increases in fruit yield of strawberry.

None of the rotations, however, hydroponic dutch buckets reduced Verticillium wilt or increased yield as much as fumigation with methyl bromide + chloropicrin. The benefits of rotations were more evident with broccoli than with Brussels sprouts. Although the results with broccoli rotations are consistent with those obtained on cauliflower , this is the first demonstration of successful rotations with broccoli and Brussels sprouts on a highly Verticillium wilt-sensitive, deep-rooted , and long-duration crop such as strawberry. Rotations with lettuce increased the numbers of microsclerotia in soil significantly over pre-rotation levels consistent with it being identified as a new host of V. dahliae and the strawberry strain being pathogenic on lettuce and vice versa . None of the rotations influenced the overall populations of Pythium spp. in soil, but it was unclear whether specific rotations influenced the species composition of this population. This often was not apparent on strawberry plants because disease caused by Pythium spp. does not have distinct symptoms on this host that enable diagnosis based on visual symptoms alone . Adaptation of successful rotations with broccoli entails giving up the annual strawberry production following fumigation during rotation and nearly 30% of the annual profits on a per hectare basis. While these short-term losses accrue, growers reap the benefits of reducing soil inoculum over the long-term. As with cauliflower , the greatest reduction in the number of microsclerotia at the Watsonville site was observed soon after the incorporation of broccoli and Brussels sprouts residues. This was followed by additional reductions in microsclerotia of V. dahliae during the second cycle of broccoli rotation. The numbers of microsclerotia increased marginally in broccoli plots during the subsequent strawberry season but remained lower than in the Brussels sprouts plots.

In contrast, at the Salinas site, even with no detectable V. dahliae propagules, broccoli rotations increased strawberry yields as evidenced by higher plant health ratings, suggesting that broccoli may suppress pathogens other than V. dahliae or result in enhanced growth of strawberry plants. Even though this study focused on Verticillium and Pythium spp., other soilborne pathogens such as R. solani, binucleate Rhizoctonia spp., and Cylindrocarpon spp. also were present at this test site and common in strawberry production systems in California . One can infer from the results obtained at the V. dahliae–free Salinas site that rotations with broccoli have benefits beyond the pathogens tested in the current study. In contrast to the reductions in V. dahliae microsclerotia and wilt on strawberry observed in rotations with broccoli and Brussels sprouts, rotations with lettuce resulted in significant increases in V. dahliae microsclerotia and wilt on strawberry. Prior to 1995 , lettuce was not even considered to be a host of V. dahliae, but wilt caused by this pathogen currently is a major problem on lettuce in the central coast of California. Recent studies have clearly established that the strawberry and lettuce strains of V. dahliae belong to the same phylogenetic group based on the sequence similarities of the intergenic spacer region and the combined sequences of the IGS region and the β- tubulin gene. Furthermore, the two strains were also cross-pathogenic to both hosts. Previous molecular profiling based on random amplified polymorphic DNA analysis also concluded that lettuce and strawberry strains displayed the closest phylogenetic relationship relative to the other host-adapted isolates tested . Unlike in most other hosts of V. dahliae, microsclerotia develop along the veins of lower, senescing lettuce leaves prior to plant death and result in abundant augmentation of soil inoculum after an infected crop.

Therefore, it is not surprising that microsclerotia of V. dahliae increased in the soil of lettuce-rotated plots and resulted in higher severity of Verticillium wilt on strawberry and reduced fruit yield compared with other rotations. Residues of other Brassica spp. have proven effective in reducing several other soilborne pathogens . Keinath reported significant reductions of gummy stem blight of watermelon in soil amended with cabbage residue. Chan and Close demonstrated the control of Aphanomyces root rot from Brassica residue amendments. Brassica spp. are well known for their characteristic sulfurcontaining compounds, known as glucosinolates, and for the disease-suppressive effects of the toxic byproducts derived from the breakdown of these compounds . Although this may explain, in part, the successful use of broccoli residues to reduce the number of microsclerotia in soil, other factors also may play an important role in the suppressive effects of Brassica spp. in general. Shetty et al. found that, despite the apparent lack of foliar symptoms and few root symptoms, broccoli roots still were colonized by V. dahliae to the same degree as cauliflower, except when soil microsclerotia levels were high. Under high soil inoculum density, the colonization rate of cauliflower roots was about 1.5-fold higher compared with broccoli roots. Microsclerotia never developed within broccoli root tissues, even 60 days after decapitating plants at the crown. In addition, there was no apparent inhibition of growth of V. dahliae on a medium with broccoli root extracts. This led to the hypothesis that perhaps the reduction in V. dahliae soil populations was caused by the combined effects of broccoli acting as a trap crop to force the germination of microsclerotia and the activation of resident microflora with an ability to degrade lignin-rich broccoli residue in addition to the melanized microsclerotia of V. dahliae . Fungal ligninases have been found to have activity against melanin as well, but microorganisms with melanolytic activity also may be involved . Data from broccoli-rotated plots demonstrated a 1,000-fold increase in bacterial and 100-fold increase in actinomycete populations relative to the unamended control or cauliflower-rotated plots, bato bucket suggesting a biological basis for the suppression of V. dahliae . It also is possible that the reduction in V. dahliae soil populations is partly due to oxygen depletion, created by the increased microbial activity from the incorporated broccoli residue, or from increases in anaerobic activities induced within the oxygen-depleted environment. Blok et al. determined that broccoliamended or rye grass-amended soils covered with a plastic cover created anaerobic environment sufficient to reduce soil inoculum of V. dahliae, Fusarium oxysporum f. sp. asparagi, and R. solani. In contrast, Subbarao et al. found that the effects of incorporated broccoli residue were identical in both open and plasticcovered plots. Perhaps the differences in these two studies can be attributed to the quantity of broccoli residue incorporated and the different field soils. In addition to the effects of glucosinolates on plant pathogens, there may be impacts on the broader soil microbial community, perhaps favoring beneficial organisms. Other studies also have attributed a biological basis of pathogen suppression from Brassica residues or by other means in naturally suppressive soils. Suppression of take-all in wheat caused by Gaeumannomyces graminis in acidic soils was associated with fungal antagonism by Trichoderma spp. . Smith et al. failed to observe changes in microbial communities by Brassica tissues when the following crop was wheat. In in vitro studies , Trichoderma spp. were tolerant to isothiocyanates while Aphanomyces, Gaeumanomyces, and Phytophthora spp. were sensitive, suggesting both a direct suppression from the toxicity of isothiocyanates and favoring of antagonism by Trichoderma spp. The effects of Brassica residues on Pythium propagules in soil have been variable. Stephens et al. reported that mustard tissue incorporation decreased grapevine establishment in soils with high numbers of Pythium propagules. Similarly, Walker and Morey found that, in citrus orchards, the number of Pythium propagules in soil as well as in the root systems were increased by mustard and rapeseed tissue amendments.

Although P. sulcatum and P. violae were highly sensitive to isothiocyanate from Brassica residues, the highly pathogenic P. ultimum was tolerant . In a recent study, Brassicaceae seed meals  stimulated Pythium populations in certain soils whereas B. juncea alone had no effect. In combination with B. napus, however, B. juncea eliminated the stimulation of resident Pythium spp. typically observed when B. napus seed meal was applied alone. Furthermore, elevated populations of Pythium spp. in S. alba or B. napus seed meal-treated soils contributed to significant weed suppression. This weed suppression was lost when Ridomil -methoxyacetylamino]-propionic acid methyl ester was applied to B. napus-treated soil and significantly diminished in S. alba-treated soils, confirming that the high Pythium numbers contributed to weed suppression . In the current study, incorporation of broccoli, Brussels sprouts, cauliflower, or lettuce residues did not alter the total Pythium populations in soil. Because the pathogenic Pythium spp. were not quantified separately, the possibility that incorporation of residue from various crops had some effect on this segment of Pythium population could not be ruled out. The impact of diseases or methods to ameliorate diseases in strawberry is ultimately measured by their effect on yield. As expected, the fumigated control provided the highest yield and correspondingly the highest profits. Even though none of the rotations equaled the level of pathogen and disease suppression observed in the fumigated control, strawberry yield in broccoli-rotated plots was a close second. The unique cost-benefit analysis employed in this study also supported this conclusion. Despite giving up yearly strawberry cultivation that is practiced in some commercial strawberry fields, rotations with broccoli and, to some extent, Brussels sprouts would be a profitable, environmentally friendly method of managing Verticillium wilt in strawberry that is effective in both conventional and organic strawberry production systems. The geometries and topologies of leaves, flowers, roots, shoots, and their arrangements have fascinated plant biologists and mathematicians alike. As such, plant morphology is inherently mathematical in that it describes plant form and architecture with geometrical and topological techniques. Gaining an understanding of how to modify plant morphology, through molecular biology and breeding, aided by a mathematical perspective, is critical to improving agriculture, and the monitoring of ecosystemsis vital to modeling a future with fewer natural resources. In this white paper, we begin with an overview in quantifying the form of plants and mathematical models of patterning in plants. We then explore the fundamental challenges that remain unanswered concerning plant morphology, from the barriers preventing the prediction of phenotype from genotype to modeling the movement of leaves in air streams. We end with a discussion concerning the education of plant morphology synthesizing biological and mathematical approaches and ways to facilitate research advances through outreach, cross-disciplinary training, and open science. Unleashing the potential of geometric and topological approaches in the plant sciences promises to transform our understanding of both plants and mathematics. The study of plant morphology interfaces with all biological disciplines . Plant morphology can be descriptive and categorical, as in systematics, which focuses on biological homologies to discern groups of organisms . In plant ecology, the morphology of communities defines vegetation types and biomes, including their relationship to the environment. In turn, plant morphologies are mutually informed by other fields of study, such as plant physiology, the study of the functions of plants, plant genetics, the description of inheritance, and molecular biology, the underlying gene regulation .

According to the FDA, “Generally, domestic and foreign food facilities that are required to register with Sect. 415 of the Food, Drug, & Cosmetic Act must comply with the requirements for risk-based preventive controls mandated by the FDA FSMA as well as the modernized Current Good Manufacturing Practices of this rule ”. Traditionally, the conventional seafood industry is regulated by the FDA, except for catfish , which along with meat products are regulated by USDA. Cell-cultivated seafood production is considered a novel or alternative food production system. Thus, labeling is also an important part of the regulations for food products. Developing a common terminology to increase transparency is required for clean labeling. There was a comprehensive study for seafood products indicating that two “common or usual names,” “Cell-cultivated Seafood” and “Cell-Cultured Seafood,” met regulatory criteria. By displaying these two phrases on packages of frozen Atlantic Salmon, both “Cell-cultivated” and “Cell-Cultured” enabled participants to differentiate cell-cultivated seafood from “Farm-Raised” and “Wild-Caught” fish. There is a need to develop reliable test kits and rapid detection sensors to validate the safety of cell-cultivated seafood products. Testing methods are essential for assessing allergenicity in seafood products, blueberry packaging containers including those produced through cellular aquaculture. These methods need to encompass not only the cultured cells themselves but also the biomaterial scaffolds employed in the process. In silico assessments can determine sequence homologies and identify structural similarities of newly expressed proteins to existing allergenic examples while other testing methods approved by the EFSA and the FDA for allergenicity verification include the pepsin resistance test and immunochemical crossreactivity testing with Immunoglobulin E from the serum of allergic individuals.

Traceability of cell-cultivated seafood will also be a major topic as is the case with conventional meat products. The conventional seafood industry is highly fragmented with very little connection from the point of harvest to the point of consumption. In contrast, cell-cultivated seafood could be easily traced back to the source of production.One concern with cell-cultivated seafood is that in the future, by developing this novel food production system, the declining need for animals, including fish and crustacea, could negatively impact the fishing industry and the associated communities. However, cell-cultivated seafood is strategically positioned to complement traditional methods like wild-caught species and aquaculture farming, to support sustainability of these communities well into the future. Moreover, the capacity to harvest and culture cells from unconventional seafood sources provides new possibilities for these communities, simultaneously enriching food choices available to consumers. Figure 5 summarizes some of the benefits and challenges/ concerns associated with the cell-cultivated seafood industry.Businesses involved in cell-cultivated meat, including seafood, have been gaining significant importance across the globe, reflected in investments of about $2.8 billion since 2016 among 156 companies dedicated to cell-cultivated meat and seafood production. Cell cultivated seafood is an important niche within the cell cultivated protein sector with industrial investment of $896 million for cell-cultivated meat and seafood with many startups and established companies pursuing cell-cultivated seafoods in 2022. This includes companies in the US, Singapore; Europe; Canada; South Africa; Israel, South Korea, Hong Kong and India. The majority of companies are focused on business-to-consumer and business-to business , with fewer companies in the B2B business model space. Supply chain issues of cell-cultivated seafood will also need to be addressed as the market expands. The market potential for cell-cultivated seafood remains an unknown at the early stages, with price being one of the determinants.

Costs are expected to decrease with cheaper ingredients and with scaling, but this has to be demonstrated in the coming years .Cell-cultivated seafood as a technology offers a potentially transformative impact for foods of the future. This is based on the scientific tools now available, coupled with the features of the technology itself. For example, the potential to directly alter cell composition to provide healthier seafood products is compelling . This impact can be further enhanced pending the acceptability of GM-based approaches, where seafood cells can be bio-engineered to provide even further nutritional and perhaps even therapeutic benefits. Food safety can also be greatly enhanced, as shelf life, microbial community, tracking, and overall freshness can potentially be improved, along with a major reduction in antibiotic use. All of these potential benefits remain to be demonstrated as the field moves forward, but the underlying science to achieve such goals is already in place. In addition, improved food security, food access, novel foods and many other future outcomes can be anticipated. Nutrition – Omega-3s and other inputs – Although fish are recognized as one of the best sources of nutritionally-important long-chain omega-3 fatty acids, the source of these compounds is actually the marine algae, bacteria, and protists. Fish consume these organisms either directly or indirectly via other fish or zooplankton, thereby bio-accumulating omega-3 fats in their tissues. The fact that animal cells—including those of fish and aquatic invertebrates—are incapable of synthesizing omega-3 fats de novo means that producers of cell-cultivated seafood will need to acquire appropriate sources of omega-3 fatty acids as ingredients. These sources could include farming of microalgae, precision fermentation, plant molecular farming, or cell free systems. However, this latter strategy has not yet been explored for omega-3 production to our knowledge, and the former three strategies will still require substantial effort before they can be scaled to the levels that may be required to support the cell-cultivated seafood industry.

Cellular engineering approaches could also provide an opportunity to engineer fish cells to synthesize long-chain omega-3 fatty acids. Codon-optimized transgene expression of omega-3 desaturase gene of C. elegans in a fish cell culture and zebrafish model enhanced the conversion of n-6 PUFA to n-3 PUF. This study also illustrated that combined transgene expression of fat-1 and fat-2 enhanced the synthesis of n-3 PUFA. In addition, cellular engineering may provide a potential solution to enhance the accumulation and stability of omega-3 fats. These approaches may include the use of exogenous reactive oxygen scavengers in the media to promote cell proliferation and suppress oxidation processes, as well as genetic modifications to over-express antioxidant genes, such as superoxide dismutase . Furthermore, cellular engineering approaches also enable the design of media compositions to promote the synthesis of omega-3 fats. Other compounds with important impacts on nutrition and organoleptic properties of seafood are also ultimately derived from the diets of aquatic animals. This includes the carotenoid astaxanthin, which is responsible for the color of salmon and shrimp, as well as for protecting membrane lipids from oxidation. As is the case with omega-3 fats, astaxanthin and other compounds that are diet-derived in conventional seafood will need to either be sourced as ingredients for addition to cell cultivated seafood or synthesized by engineered cells. Notably, the U.S. government recently acknowledged the need to “bolster research into alternative feed ingredients for livestock and aquaculture, including plants, algae, or seaweeds, that can enhance or replace feed ingredients”. Marine-derived feed ingredients such as omega-3s and astaxanthin may be a shared need across both conventional and alternative protein production platforms.Cell-cultivated seafood is in its infancy. There is growing research among academic labs, and a growing corporate effort mainly among startup companies worldwide to tackle the increasing consumer demand for seafood. In these early stages, the focus is on cell sources, media optimization and scaffolding, while with time these efforts will mature into scaling production for impact. With scale, pricing will be reduced and availability will increase. The vision is that this emerging approach to cell-cultivated seafoods will offer safer and healthier alternatives for consumers, while enhancing environmental sustainability goals .For this growing industry to reach its potential, government support for research and commercialization efforts will be essential. A report by the UK Foreign, Commonwealth & Development Office and the ClimateWorks Foundation estimated that annual global public spending on R&D and commercialization—including that of plant based proteins, precision fermentation, insects, blueberry packing boxes and cell cultivated meat—would need to increase to a total of US $10.1 billion to unlock the full benefits of alternative proteins. Whereas terrestrial cell-cultivated meat benefits from a strong foundation of biomedical tissue engineering research, and a fairly detailed understanding of mammalian and avian cell biology generally, this is less true for cell-cultivated seafood. Therefore, basic research aimed at understanding piscine and invertebrate cell types, differentiation processes, and metabolic requirements is still needed. Public funding of such research will reduce duplication of effort and provide a strong foundation for commercial efforts, thereby benefiting the field as a whole, everyday consumers, and the planet. Universal in this evolution to grow cell-cultivated seafood as a major option for alternative food for consumers around the world, safety, flavor and texture are paramount. Thus, regulations and methods to properly assess these new foods and to provide tracking will be a foundational need. In total, the potential for this emerging field to transform the seafood that we consume, while providing major benefits to sustainability, quality and food safety are expected to continue to drive the growth of this field. Both fishing and aquaculture already face major environmental challenges, and cell-cultivated seafood offers a new approach to address these issues, while also expanding our palates in ways never before possible. The future is exciting, but the path will need to be built upon a strong scientific foundation linked to consumer willingness to try these new foods and eventually to embrace them.

California has led the nation in farm sales since 1950, when Los Angeles County had more farm sales than any other county in the United States, largely because of specialization in the production of high-value fruit, nut and vegetable crops. California’s farm sales in 2015 were $47 billion, including $18 billion from the sale of fruits and nuts, $9 billion from vegetables and melons, and $5 billion from horticultural specialties such as floriculture, nurseries and mushrooms. That is, $32 billion, or two-thirds, of farm sales were from these FVH crops. The leading farm counties, Tulare, Kern and Fresno, each had farm sales of almost $7 billion in 2015 . The production of many fruits and vegetables is labor-intensive, meaning that labor represents 20% to 40% of production costs for table grapes, strawberries and other commodities. Average employment of 421,300 farmworkers in 2015 represents 12 monthly snapshots of persons on the payroll during the pay period that includes the 12th of the month. However, total wages of $12.8 billion are all wages paid to all workers, including those who were employed at other times during the month and those who earned wages from non-farm employers. A worker who was employed 2,080 hours — the number of hours California’s Employment Development Department considers full-time and full-year employment — would earn an average annual pay of $30,300, which prompted the Los Angeles Times to ask why, despite implied hourly wages of almost $15 per hour, U.S.-born workers reject farm jobs . The answer is that few farmworkers are employed year-round; many are employed fewer than 2,080 hours a year. In 2015, the average earnings of all workers with at least one farm job was $20,500. EDD does not collect hours of work data from employers who are paying unemployment insurance taxes, but does collect the earnings and employment data that we use in this article. The National Agricultural Workers Survey collects hours of work data from California crop workers, and found that they were employed an average of 47 hours during the week before they were interviewed in 2015–16. American Community Survey data, also collected from workers, shows that both crop and livestock workers were employed slightly more than 40 hours a week. The NAWS and ACS do not collect data on annual hours worked. However, if workers averaged more than 40 hours a week over 52 weeks, average hourly earnings would be lower than $15. Non-supervisory production workers do most of the work on the state’s largest farms that produce labor intensive FVH crops. About 90% of California crop workers were born in Mexico, and 60% are unauthorized, according to the NAWS, which is 10 percentage points higher than the U.S. average of 50% unauthorized crop workers . The reason for more unauthorized workers in California is that it has a higher share of foreign-born workers: most foreign born workers are unauthorized, and California’s 90% share of foreign-born crop workers exceeds the 60% foreign-born share in the rest of the United States. The dominance of labor-intensive crops in California, and the Trump administration’s efforts to step up border and interior enforcement, has increased interest in the availability of farmworkers.