Author: naturehydro

The ancestral reconstruction allows us to consider the second question posed earlier: is the donor consistent with the X. fastidiosa subsp. fastidiosa genotypes found in the United States? The answer is very clearly “no.” The original donor carried cysG12 and holC19 . These alleles are both found in isolates from Central America, but no X. fastidiosa subsp. fastidiosa isolate found in the United States comes close to matching this criterion: the most similar U.S. ST has a 12-bp mismatch. There has been extensive sampling of X. fastidiosa subsp. fastidiosa within the United States, based on 85 isolates sampled across the United States from 15 different host plants . There is very little variation within X. fastidiosa subsp. fastidiosa isolates from the United States, consistent with the hypothesis that all X. fastidiosa subsp. fastidiosa isolates currently found in the United States are derived from a single strain introduced from Central America . Based on these data, we conclude that the X. fastidiosa subsp. fastidiosa donor was introduced into the United States from Central America and recombined with a native X. fastidiosa subsp. multiplex genotype similar to ST45; however, this donor lineage of X. fastidiosa subsp. fastidiosa was ultimately unsuccessful and died out. We can never conclusively prove the absence of this genotype from North America. However, X. fastidiosa has been extensively sampled from many plant species throughout the United States, and no isolates of X. fastidiosa subsp. fastidiosa have been found that carry alleles similar to the inferred donor alleles cysG12 and holC19 ; indeed all X. fastidiosa subsp. fastidiosa isolates so far found in the United States are consistent with the introduction into the United States of just a single genotype . The transient presence of the donor genotype is consistent with a single large-scale introgression event founding the recombinant group. This raises the possibility that conjugation might have been involved; however, if this was the case,10 plastic plant pots the genomic DNA was broken into pieces prior to homologous recombination, since the data show short regions of recombination.

The data from the MLST loci plus pilU show 7 significant recombination events , and 3 of them included at least one recombination break point. Since these loci range in length from roughly 400 to 700 bp, this result would be consistent with an average recombination length of no more than a few kb, similar to the 2.6-kb average length observed by Nunney et al. in a comparison of two X. fastidiosa subsp. fastidiosa genomes . Similarly, the regions identified from the data of Parker et al. showed the same pattern, with a high proportion of recombination breakpoints identified within the sequenced loci . In this context, it is important to note that Rogers and Stenger have found a conjugative plasmid in X. fastidiosa. Furthermore, a high rate of transformation has been demonstrated in the lab , and it has been shown that this process can result in efficient recombination with only a few hundred bases of homologous sequence . Both conjugation and transformation may have been involved in the evolution of the recombinant group, since the data raise the possibility of both large-scale intersubspecific and smaller-scale intrasubspecific recombination . The results support the general conclusion that successful recombination is a rare but important event, a possibility emphasized by Wiedenbeck and Cohan in their review of bacterial adaptation to new environments. However, given the high rates of recombination observed experimentally in X. fastidiosa , this rarity is somewhat surprising, perhaps suggesting that in X. fastidiosa the majority of intersubspecific recombination events fail due to their negative fitness consequences. Fitness loss due to recombination is consistent with the high level of plant host specificity observed among the genotypes of X. fastidiosa . On the other hand, it is clear that recombination can create combinations that are beneficial to the species, enabling it to invade new plant hosts. Specifically, the successful invasion of blueberry and blackberry appears to have resulted from large-scale recombination between two subspecies, a pattern that appears to be repeated in the invasion of mulberry . Furthermore, Nunney et al. suggested that introgression into X. fastidiosa subsp. pauca in South America from a donor may have enabled X. fastidiosa subsp. pauca to infect citrus, causing the economically devastating disease of citrus variegated chlorosis.

This would help explain why CVC did not appear in Brazil until the 1980s , despite the presence of the native pathogen and vectors ever since citrus was introduced several hundred years ago. These observations raise an important concern: that mixing of genetically divergent forms of the same species can result in recombinant forms capable of invading new niches—in this case, new plant hosts. Thus, the presence of a pathogen in an area should not lead to the assumption that further introductions will cause no further harm; in fact, as a result of recombination, further introductions may result in a qualitative worsening of the problem.Highbush blueberries , native to the northeastern United States, are important commercial fruit and are the most planted blueberry species in the world . In the United States, blueberries traditionally have been grown in cooler northern regions; however, the development of new southern cultivars with low chilling-hour requirements has made possible the expansion of blueberry production to the southern United States and California .Blueberry production in California was estimated in 2007 at around 4,500 acres and is rapidly increasing. Common southern cultivars grown include ‘Misty’ and ‘O’Neal’, but other improved southern high bush cultivars are now being grown from Fresno southward, such as ‘Emerald’, ‘Jewel’ and ‘Star’ . Southern high bush “low-chill” cultivars are notable for their productivity, fruit quality and adaptation , and require only 150 to 600 chillhours, making them promising cultivars for the San Joaquin Valley’s mild winters . Since 1998, we have conducted long-term productivity and performance evaluations of these cultivars at the University of California’s KearneyAgricultural Center in Parlier . North American production of high bush blueberry has been increasing since 1975, due to expansion of harvested area and yields through improvements in cultivars and production systems. In 2005, North America represented 69% of the world’s acreage of high bush blueberries, with 74,589 acres producing 306.4 million pounds . Acreage and production increased 11% and 32%, respectively, from 2003 to 2005. The U.S. West, South and Midwest experienced the highest increases in acreage. In 2005, 63% of the world’s production of high bush blueberries went to the fresh market. North America accounts for a large part of global high bush blueberry production, representing 67% of the fresh and 94% of the processed markets . Blueberry consumption is increasing, which is encouraging increased production. As a result, fresh blueberries are becoming a profitable specialty crop, especially in early production areas such as the San Joaquin Valley .

In general, a consumer’s first purchase is dictated by fruit appearance and firmness . However, subsequent purchases are dependent on the consumer’s satisfaction with flavor and quality, which are related to fruit soluble solids , titratable acidity , the ratio of soluble solids to titratable acidity, flesh firmness and antioxidant activity . The sugars of the larger high bush blueberry cultivars that are grown in California are fructose, glucose and traces of sucrose. Lowbush blueberries — which are wild, smaller and grow mostly in Maine — lack sucrose. . The composition of organic acids is a distinguishing characteristic among species. In high bush cultivars, the predominant organic acid is usually citric , while the percentages of succinic, malic and quinic acids are 11%, 2% and 5%, respectively. However, in “rabbiteye” blueberries the predominant organic acids are succinic and malic, with percentages of 50% and 34%, respectively, while citric acid accounts for only about 10% . These different proportions of organic acids affect sensory quality; the combination of citric and malic acids gives a sour taste,plastic pots large while succinic acid gives a bitter taste . In addition to flavor, consumers also value the nutritional quality of fresh fruits and their content of energy, vitamins, minerals, dietary fiber and many bio-active compounds that are beneficial for human health . Fruits, nuts and vegetables are of great importance for human nutrition, supplying vitamins, minerals and dietary fiber. For example, they provide 91% of vitamin C, 48% of vitamin A, 27% of vitamin B6, 17% of thiamine and 15% of niacin consumed in the United States . The daily consumption of fruits, nuts and vegetables has also been related to reductions in heart disease, some forms of cancer, stroke and other chronic diseases. Blueberries, like other berries, provide an abundant supply of bio-active compounds with antioxidant activity, such as flavanoids and phenolic acids . For example, a study performed in rats showed that when they were fed diets supplemented with 2% blueberry extracts, age-related losses of behavior and signal transduction were delayed or even reversed, and radiation-induced losses of spatial learning and memory were reduced . Some studies have shown that the effects of consuming whole foods are more beneficial than consuming compounds isolated from the food, such as dietary supplements and nutraceuticals. Because fruit consumption is mainly related to visual appearance, flavor and antioxidant properties, we decided to evaluate fruit quality attributes, antioxidant capacity and consumer acceptance of the early-season blueberry cultivars currently being grown in California. We characterized the quality parameters of six southern high bush blueberry cultivars grown in the San Joaquin Valley for three seasons , and evaluated their acceptance by consumers who eat fresh blueberries.For the quality evaluations at UC Kearney Agricultural Center, we used three patented southern high bush blueberry cultivars — ‘Emerald’ , ‘Jewel’ and ‘Star’ , and three non-patented cultivars — ‘Reveille’, ‘O’Neal’ and ‘Misty’. The plants were started from tissue culture and then grown for two seasons by Fall Creek Farm and Nursery in Lowell, Ore. Before planting these cultivars in 2001, the trial plot was fumigated to kill nut grass .

Because blueberries require acidic conditions, the plot’s soil was acidified with sulfuric acid, which was incorporated to a depth of 10 to 12 inches with flood irrigation, resulting in a pH ranging from 5.0 to 5.5. A complete granular fertilizer was broadcast-applied at a rate of 400 pounds per acre . The plants were mulched with 4 to 6 inches of pine mulch and irrigated with two drip lines on the surface of the mulch, one on each side of the plant row. Irrigation frequency was two to three times per week in the spring and daily during June and July. The emitter spacing was 18 inches , with each delivering 0.53 gallon per hour of water acidified with urea sulfuric acid fertilizer to a pH of 5.0. The plot received an application of nitrogen in the first season, as well as in subsequent growing seasons. The rate was 80 pounds nitrogen per acre at planting, 60 pounds the second year, 90 pounds the third year and 120 pounds the fourth year. Annual pest control was limited to one application of Pristine fungicide in February for botrytis management, and two or three herbicide treatments of paraquat . In year three, the plants received one insecticide treatement of spinosad for thrips management. Twenty-eight plants per cultivar were planted in a randomized block design using seven plants per block as an experimental unit, replicated in four rows. Rows were spaced 11 feet apart, with the plants in the rows spaced 3 feet apart, with a space of 4 feet between plots. Fruit was harvested at times when it would have been commercially viable if it had been in a commercial field. Fruit from each of the seven plant blocks was harvested and a composite sample of 80 random berries per each replication was used for quality evaluations.Berries were randomly selected from each replication for quality evaluation at the first harvest time for each respective season . During the 2007 season, in addition to the initial quality evaluations, harvested berries were stored at 32°F in plastic clam shells, and measured for firmness 15 days after harvest and for antioxidant capacity 5, 10 and 15 days after harvest. Three replications per cultivar were measured for each quality parameter. The initial firmness of 10 individual berries per replication was measured with a Fruit Texture Analyzer. Each berry was compressed on the cheek with a 1-inch flat tip at a speed of 0.2 inch per second to a depth of 0.16 inch and the maximum value of force was expressed in pounds force .

Power analysis from a larger set of black spruce tree ring increments from three stands in Interior Alaska , indicated that as few as four tree ring widths could be used to estimate site ANPP within a 95% confidence interval. Such a low sampling intensity for tree rings may be a reflection of the even-aged, structurally simple nature of black spruce forests. The mean of the average annual ring width for the last 10 years was used with the stem allometry equation to calculate secondary growth for each tree measured in the inventory. Stand biomass was also calculated with our inventory but regional Alaskan or Canadian equations , and these values were compared to estimates derived from the local equations.Above ground biomass of vascular plants, mosses and lichens was measured across all sites by destructive harvest in July 2001 at approximately peak biomass. To more closely examine the dynamics of regrowth in the first several years after fire, biomass was also measured in the 1999 dry site 2 months after the fire as well as mid-summer in 2000–2002; it was also measured in 2000, 2001, and 2002 in the mature dry site for comparison. Trees less than 1.37 m in height that were excluded from the inventory described above were included in these harvests. In harvests of the 1999 dry site, we determined whether each species was a re-sprouter by assessing the presence of charred stems or large rhizomes. We also monitored species or generic richness on an annual basis in these sites by recording the presence of all species within a 144-m2 plot surrounding the 1 m2 harvest blocks. We did not survey species richness in the other sites. In each site, above ground biomass was clipped from either 6 or 12 randomly located 1 m2 quadrats. In the mesic chronosequence sites and the 1987 dry site,blueberry container size above ground biomass of vascular species was clipped from six 1 m2 quadrats randomly located along two 100 m long permanent transects .

Mosses and lichens were collected from a 400-cm2 organic soil plug sawed from a randomly selected corner of the 1 m2 quadrat following vascular plant clipping. In the 1987 dry site and the 1994 mesic site, we also harvested tall shrubs in a 4-m2 quadrat surrounding the 1-m2 quadrat to account for their larger stature. Vegetation was harvested similarly in the 1999 and 1921 dry sites, except that 12 quadrats were harvested. Samples were returned to the lab and sorted into species and tissues within 1 day of harvest. Each vascular species was separated into several tissue categories including current year and previous year leaves, current year and previous year stems, and fruits or inflorescences following methods modified from Shaver and Chapin and Chapin and others . Mosses were separated to species and lichens to genera. We included all structurally intact moss and lichen tissue in the live biomass category. This was determined by tugging gently on the brown part of the moss or lichen ramet; the part that broke off was determined to be litter. Large samples were chopped into small pieces, mixed, and then sub-sampled for fresh and dry weights. Tissues were then dried at 60 C for 48 h or more before weighing. Above ground vascular net primary production was estimated as the sum of the current year‘s apical growth, including leaves and stems. We did not measure secondary growth for under story plants and thus our ANPP values represent an underestimate for shrubs where secondary growth is likely important, mainly Salix spp. and trees less than 1.37 m tall. Apical growth was defined as that produced from apical or intercalary meristems during the current growing season; it was calculated by summing the masses of all current year‘s leaf, stem, and reproductive tissues in the quadrat harvested. Harden and others measured moss production in these sites by measuring the apical growth of individual species and then scaling growth to the plot level with digital mapping. At each site, an average of ten 60 · 60 cm2 moss plots were arranged along greater than 100 m transects with plots spaced every 20–40 m. Percent cover values for up to five dominant moss species within each plot were recorded in fall 2001 via digital photos, extensive field notation, and digitization with Arcview 8.0 software .

Apical growth for each species within each plot was based on growth between June and September of 2001. Within each plot, 10 cm2 dense, generally single-species patches of moss were dyed with a fluorescent brightener in early June. Sprayed moss samples were harvested in late September using a coring device of known area and refrigerated until measurement. Apical growth of each ramet was measured individually under a black light using calipers and new growth was harvested, dried, and weighed to estimate per ramet production. The density of stems per m2 was determined from the % cover plots described above. Moss NPP per species was then estimated on a per plot basis as the mass of apical growth per ramet times the ramet density per unit area times the areal coverage. To validate this method of estimating moss NPP, Harden and others compared estimates of H. splendens productivity to estimates based on a morphological growth marker . They found that the fluorescent dye method underestimated H. splendens production relative to the morphological method, possibly due to an offset in the timing of harvest of the two methods. We have chosen to report the fluorescent dye methods here because these estimates likely represent the most conservative estimate of moss NPP.In the 1999 dry site where we followed growth for 4 years, ANPP was surprisingly resilient to fire disturbance and returned to the level of the mature 1921 dry stand by year four despite radical changes in species composition. Treseder and others observed that root length production was not different between these two sites in 2002, suggesting that below ground production was similarly resilient. The re-sprouters that dominated post-fire productivity must have had roots and/or rhizomes buried in deep organic or mineral soil because over 70% of organic soil depth was consumed in the fire . Rhizomes and roots may have been lingering in mineral soil since the last burn, which is a pattern observed for grasses in black spruce/feather moss sites , as well as trembling aspen which may re-sprout from root suckers after fire . Species known to root in the organic layer dominated pre-fire under story biomass, namely blueberries and cranberries ; these species recovered slowly after fire in the 1999 dry site.

The 1999 mesic site had substantially thicker organic soil layers both before and after fire then the 1999 dry site . Blueberry biomass was not different but ANPP was twice as large in the 1999 mesic site then in the mature mesic chronosequence site . Cranberries, by contrast,growing raspberries in container had 93% less biomass in the 1999 than in the 1886 mesic site, which may be due to the fact that their rhizomes tend to be only 2–3 cm deep in the moss litter layer , whereas blueberries tend to root in the fibric layer . Most important in the rapid recovery of ANPP after fire appears to be the survival of key species, which may, in part, be related to whether meristems are protected in unburned layers of soil. Other factors that likely contribute to the rapid rate of ANPP recovery include increased resource availability due to release of N and P via combustion , decreased competition , warmer soils stimulating microbial decomposition and mineralization of nutrients from soil organic matter , and more available moisture due to reduced evapotranspiration . Across all sites, ANPP was highest in the 1987 dry site where deciduous trees and shrubs dominated biomass and production. Aspen and willow resprouts were present in the 1999 dry site , and stems of dying or dead aspen and willow were visible in the dry mature site , so it is plausible that these sites are part of a successional trajectory that includes co-dominance of black spruce with deciduous trees during mid-succession . In the mesic chronosequence, deciduous trees and tall shrubs were present but at low abundance in intermediate aged sites and were absent from the mature site. Therefore, in contrast to the dry chronosequence, the mesic sequence may represent self-replacement, or spruce-to-spruce successional trajectory . Fire severity, drainage and soil temperature have been identified as factors driving the abundance of aspen and tall shrubs in Interior Alaska. Aspen density was positively related to fire severity in a nearby mesic site that burned in 1994 and aspen and willow were more abundant in more frequently burned sites in the Yukon presumably due to the better survivorship of species with rhizomes and roots in the mineral soil. Across the landscape, single species stands of aspen are found in relatively warm, well-drained sites . Severe or frequent fires can increase soil temperature though the removal of insulating moss and soil organic matter and increase drainage through thermal erosion of permafrost. Thus, the high abundance of deciduous tree and tall shrub species in the 1987 dry site could be related to thermal effects of severe fire or alternatively, the site could just be an anomalously warm, well drained patch of the landscape.

Finally, stochastic processes such as the proximity to seed sources could play a role in the establishment of deciduous species as well and cannot be ruled out as a factor contributing to compositional differences between the chronosequences. Black spruce density, basal area, biomass and ANPP in the mature sites were within the range reported for Interior Alaska . Biomass in our mesic 1886 stand was 90% of peak black spruce biomass estimated from large-scale forest inventory measurements across the state of Alaska , suggesting that this stand may be at or near peak biomass. The mature dry stand, by contrast, accumulated 78% of biomass predicted for its age class . If the biomass accumulation curve for this chronosequence is projected to 150 years estimate of maximum stand biomass, peak biomass would be 2,831 g m)2 ; still substantially less than peak biomass in the mesic chronosequence. Lower black spruce biomass and ANPP in the mature dry site than in the mature mesic site appears to be driven primarily by higher tree density in the latter site because biomass and ANPP per tree were similar between sites . Yarie and Van Cleve similarly found black spruce production to be constant over variably drained stand ages ranging from 50 to 150 years when productivity was standardized to full stocking rate. Lower density in the mature dry than in the mature mesic site could be related to processes directly attributable to drainage, such as self thinning due to water competition , interactions with abundant deciduous tree species or feedbacks between fire and drainage . Alternatively, differences in density could be caused by processes that are relatively independent of drainage, such as climatic extremes during the sensitive early years of spruce seedling establishment. Our 1956 mesic site had only 25% of the tree density of the 1886 mesic site and contained only 16% of black spruce biomass predicted for its age class by Yarie and Billing‘s accumulation curve. Because upland black spruce stands tend to be comprised of a single cohort , it seems unlikely that density will quadruple in the next 50 years. These differences in density, then, may represent poor site matching in the mesic chronosequence and confound estimates of biomass accumulation and ANPP. To explore the impact of this on our biomass estimates, we multiplied per tree biomass of the 1956 mesic site 1 by density of the mature mesic site , yielding an estimate of 1,135 g m2 , which is within 10% of Yarie and Billing‘s estimate for this age class. When this value was plotted on our chronosequence biomass accumulation curve , the curve is still best-fit by a linear equation 169.4, R2 = 0.97, P = 0.01, suggesting that the functional shape of biomass accumulation would still differ between mesic and dry chronosequences if density was held constant across the mesic chronosequence. Our Alaskan black spruce stands had less biomass and were less productive than comparable well drained stands in Manitoba, Canada, due to both lower tree density and lower per tree biomass and growth.

Many human studies reporting positive health outcomes have used freeze-dried wild blueberry powder, which is a natural source of concentrated polyphenolics. However, the freeze-dried WBB powder may be tart or astringent and not always palatable to consume. This can be problematic in feeding trials in children and adults. In our previous work, we developed five food products prepared with freeze-dried WBB powder that were evaluated for children’s acceptability and desire to eat. These results are useful in designing food products as well as menu items that could be used in clinical trials of WBB-rich diets. In addition to evaluating sensory properties, it is important to validate the storage stability of polyphenolics in these products, before use in clinical trials, to ensure that a consistent dose of polyphenolics can be maintained. Blueberry polyphenolics, especially anthocyanins, are unstable in various processed forms such as juices, jams, purees, and canned berries when stored at ambient temperature. Additionally, anthocyanins in freeze-dried WBB powder are susceptible to degradation when stored at ambient temperature with a reported half-life of 139 days at 25 ◦C. The mechanism responsible for loss of anthocyanins during storage is unknown, but anthocyanin losses are commonly accompanied by increased polymeric color values, suggesting that anthocyanins form polymers with proanthocyanidins. In addition to polymerization, many other factors can affect the stability of anthocyanins including exposure to elevated temperatures, light, oxygen, metals, sugars, and ascorbic acid. At present, refrigeration of blueberry products such as jam and juices is the best approach to mitigate polyphenolic losses during storage. This study was undertaken to determine the stability of anthocyanins, flavonols, plastic potting pots chlorogenic acid, and percent polymeric color in five blueberry products prepared with freeze-dried WBB powder.

Gummy, oatmeal bar, graham cracker cookie, and juice were stored at 21 ◦C and 4.4 ◦C and evaluated for anthocyanin, flavonol, and chlorogenic acid content and percent polymeric color over eight weeks of storage. An ice pop product stored at −20 ◦C was evaluated for its anthocyanin and chlorogenic acid content over eight weeks of storage. Samples of juice, ice pop, gummy, oatmeal bar, and graham cracker cookie, each containing 15 g of WBB powder per serving, were prepared and packaged as previously described. One serving of oatmeal bar, ice pop, and graham cracker cookie was equivalent to one piece each , a juice serving was 135 g, and a gummy serving was 7 pieces, or 113 g. The amount of 15 g of WBB powder used in product formulations was calculated and converted from previous animal studies to humans. This involved only the use of brief microwave heating to solubilize the ingredients in order to avoid thermal loss of phenolic compounds, but still obtain a ready-to-consume non-baked product. The blueberry juice and ice pop were prepared with an anthocyanin concentrate, previously extracted from the WBB powder. This procedure was used to produce juice and ice pop products with no particulates. The formulation was adjusted with water so the anthocyanin content ofthe products was equivalent to that found in 15 g of WBB powder per serving. The preparation and processing of the samples for the storage study were performed in two separate experiments, using the same sample of wild freeze-dried blueberries obtained from FutureCeuticals Inc. . The WBB powder was stored at 15.5 ◦C for four months between the two experiments. The samples from Experiment 1 were stored at 21 ◦C and the samples from Experiment 2 were stored at 4.4 ◦C. The ice pop products prepared in Experiment 1 were stored at −20 ◦C. Three samples of each packaged product were evaluated at time 0 and after 2, 4, 6, and 8 weeks of storage.

Polyphenolics were extracted by homogenizing 5 g of WBB-containing food product or 1 g of WBB powder in 25 mL of extraction solution containing methanol/water/formic acid , to the smallest particle size using a Euro Turrax T18 Tissuemizer for 1 min. Homogenates were centrifuged for 5 min at 10,864 × g. The pellet was re-extracted two additional times with 25 mL of extraction solution and centrifuged for 5 min at 10,864 × g. The filtrates were pooled and adjusted to 100 mL with extraction solvent in a volumetric flask. Prior to HPLC analysis, 5 mL of extract were dried in a Thermo Savant Speed Vac Plus SC210A and reconstituted in 1 mL 5% formic acid in water. All samples were passed through 0.45 µm nylon syringe filters into 1 mL HPLC vials prior to HPLC analysis. The ice pop and juice samples did not undergo extraction due to prior extraction of anthocyanins to make the concentrate used in the formulation but were filtered using the 0.45 µm nylon syringe filters prior to HPLC analysis. Anthocyanins and chlorogenic acid were analyzed by HPLC using the method of Cho and others. Samples were analyzed using a Waters HPLC system equipped with a model 600 pump, a model 717 Plus autosampler, and a model 996 photodiode array detector. Separation was carried out at room temperature using a 4.6 mm × 250 mm Symmetry C18 column preceded by a 3.9 mm × 20 mm Symmetry C18 guard column. The mobile phase was a linear gradient of 5% formic acid and methanol from 2% B to 60% B for 60 min at a flow rate of 1 mL/min. The system was equilibrated for 20 min at the initial gradient prior to each injection. Detection wavelengths of 320 nm and 510 nm were used to monitor chlorogenic acid and anthocyanin peaks, respectively. Individual anthocyanin monoglucosides and acylated anthocyanin derivatives were quantified as delphinidin, cyanidin, petunidin, peonidin, and malvidin glucoside equivalents using external calibration curves of a mixture of authentic standards .

Chlorogenic acid was quantified using external calibration curves of an authentic standard . Results are expressed as mg of anthocyanin or chlorogenic acid per g of WBB powder. Flavonols were analyzed by HPLC using the same HPLC system described above according to the method of Cho et al.. Separation was performed at room temperature on a 4.6 mm × 250 mm Aqua C18 column preceded by a 3.0 mm × 4.0 mm ODS C18 guard column. The mobile phase was a linear gradient of 2% acetic acid and 0.5% acetic acid in water and acetonitrile from 10% B to 55% B in 50 min and from 55% B to 100% B in 10 min at a flow rate of 1 mL/min. The system was equilibrated for 20 min at the initial gradient prior to each injection. A detection wavelength of 360 nm was used to monitor flavonol peaks. Flavonols were quantified as rutin equivalents using an external calibration curve of an authentic standard , with results expressed as mg of rutin equivalents per g of WBB powder.An analytical Hewlett Packard 1100 series HPLC instrument equipped with an autosampler, binary HPLC pump,raspberry container growing and UV/Vis detector was used. For HPLC/MS analysis, the HPLC apparatus was interfaced to a Bruker model Esquire-LC/MS ion trap mass spectrometer . Mass spectral data were collected with the Bruker software , which also controlled the instrument and collected the signal at 520 nm. Typical conditions for mass spectral analysis conducted in positive-ion electrospray mode for anthocyanins and negative-ion electrospray mode for flavonols included a capillary voltage of 4000 V, a nebulizing pressure of 30.0 psi, a drying gas flow of 9.0 mL/min, and a temperature of 300 ◦C. Data were collected in full scan mode over a mass range of m/z 50−1000 at 1.0 s per cycle. Characteristic ions were used for peak assignment. For compounds where chemical standards were commercially available, retention times were also used to confirm the identification of components. The effect of storage time on anthocyanins, flavonols, chlorogenic acid, and % polymeric color in each blueberry product was evaluated using the Fit Model platform of JMP, and the percent retention of each compound after 8 weeks of storage was calculated using the fit model equation. The effect of storage temperature on phenolic compounds stability was not evaluated in this study due to the length of time the WBB powder was stored between processing the products in Experiment 1 and Experiment 2 . During this four-month storage time, the powder stored at 15.5 ◦C presumably absorbed moisture evident by powder clumping, resulting in different amounts of polyphenolics in the products immediately after processing.

Principal component analysis was performed with the total and individual anthocyanins variables, using the Multivariate platform in JMP, on the mean value of each sample per time point and using the correlation method. Correlations among total anthocyanins and percent polymeric color were determined by pairwise correlations method in the multivariate platform of JMP.The WBB powder used to prepare the products contained at least 22 anthocyanins , which were identified by comparing their mass-to-charge values and elution orders with previous studies. Blueberries are unique in that three different sugars are commonly attached to the five anthocyanidins. This was confirmed in our study; however, we were unable to detect peonidin-3-arabinoside using our HPLC method. We were unable to obtain complete separation of all of the anthocyanins present in the extract due to the complexity of the anthocyanin profile. Peak 15 contained two co-eluting compounds, namely cyanidin-3- galactoside and cyanidin-3- galactoside, and peak 18 was composed of three co-eluting compounds, namely delphinidin-3-rutinoside, cyanidin-3- glucoside, and malvidin-3- galactoside. We were unable to identify peak 17, which appeared to be a delphinidin derivative based on its aglycone m/z of 303, but the molecular ion m/z value was ambiguous. Many of the anthocyanins were present in acylated form. Two of the cyanidin glycosides were acylated with malonic acid, whereas delphinidin, cyanidin, and malvidin galactosides as well as petunidin, peonidin, and malvidin glucosides were acylated with acetic acid moieties. The total anthocyanin content of the ice pop over eight weeks of storage at −20 ◦C is shown in Figure 1. The total amount of anthocyanins significantly decreased with storage time , but the percent retention remained high with 93% of total anthocyanins retained in the product after eight weeks. Consistent with our results, total anthocyanin content of frozen blueberries was stable over three months of storage at −20 ◦C . Changes in major individual anthocyanins in the ice pop over eight weeks of storage at −20 ◦C are shown in Figure S1. Most of the individual anthocyanins did not significantly decrease over storage. For the anthocyanins that decreased during storage, their percent retention after eight weeks remained over 87%: malvidin-3-glucoside , malvidin-3-galactoside , cyanidin-3-galactoside , malvidin-3- glucoside , petunidin-3-glucoside .The total anthocyanin content of the juice decreased with storage time for each storage temperature . The total anthocyanin content of juice stored at 4.4 ◦C and 21 ◦C is shown in Figure 2. After eight weeks of storage, the juice stored at 4.4 ◦C retained 90.7% of total anthocyanins compared with control samples , whereas the juice stored at 21 ◦C retained 69.1%. Concentrations of anthocyanins are known to readily decline during storage of blueberry juice at ambient temperature, but refrigeration is an effective treatment to ameliorate anthocyanin losses. Changes in the major individual anthocyanins in the juice stored at 4.4 ◦C and 21 ◦C over eight weeks of storage are shown in Figure S4. At 4.4 ◦C, peonidin-3-galactoside, cyanidin-3-arabinoside, malvidin-3-galactoside, malvidin-3-glucoside, and malvidin-3- galactoside remained stable over the eight weeks of storage. At 4.4 ◦C, all anthocyanins showed >50% retention, with the minimal percent retention being 57.7% for the unknown delphinidin derivative. This compound, however, did not significantly decrease over storage at 21 ◦C, along with the two co-eluting anthocyanins galactoside + cyanidin-3- galactoside. Besides these two compounds, the percent retention of anthocyanins at 21 ◦C ranged from 59% glucoside to 75.5% . The total anthocyanin content of the gummy product decreased with storage time for each storage temperature . The total anthocyanin content of the gummy product stored at 4.4 ◦C and 21 ◦C is shown in Figure 2. After eight weeks of storage, the gummy product stored at 4.4 ◦C and 21 ◦C retained 43.2% and 50.6%, respectively, of their original total anthocyanin content . Consistent with our findings, levels of total anthocyanins declined in gelatin gels prepared with grape pomace extract over 24 weeks of storage at 21 ◦C, with losses most pronounced in gels exposed to neon light.

Cattle also went into the bottom land to get water, especially during droughts when large holes were dug into the riverbed to provide livestock and people with access to the perennial groundwater for drinking . Reclamation projects stimulated by new state laws promoting land use converted marshy areas around valley’s sloughs into agricultural land during 1850-1870 . According to records from American land surveyors and the British naturalist William Brewer who traveled the river corridor in the mid-1850s and early 1860s , the Salinas was a desolate, dry bed of wind-blown sand and treacherous quicksand at that time, yet newspapers also reported on devastating floods, such as in 1852 when the Salinas plain was inundated. Overall, a fundamental transformation in the landscape was wrought by Europeans, even as regional climate remained the same with no long term trend throughout that period , although the characteristic feature of the climate has been frequent recurrences of droughts and floods . Similar to the observed threshold change in sedimentary response to post-European land transformation in the Chesapeake Bay region , the pre-European Salinas landscape was likely resilient to natural climatic events , even with Ohlone fire practices, but after European land transformation the landscape became highly sensitive to perturbation.Today, the Salinas River is heavily changed due to modern land use and flow regulation. Excessive surface irrigation and the regional drought of 1880 were the major stimuli for the onset of groundwater pumping , fueled by the wood from the valley’s remaining trees. By 1901 pumping was well underway, with wells drawing water from as deep as 75 m below the ground surface and lowering the water table below the ground by 3-5 m . Agriculture is now the largest anthropogenic disturbance in the watershed in terms of area,blueberries in containers followed by urbanization and dam emplacement .

Three major dams were constructed on the mainstem and two major eastern tributaries from 1941–1965, impounding the runoff of some 1,970 km2 , or about 17% of the total watershed, primarily in the mountainous subbasins in the wetter western mountains . Urbanization has increased significantly in the basin over the past century but represented only ~2% of land area by 2010.The Salinas River watershed of central California was used as the test case for this study because of the preponderance of data documenting diverse hydrologic events in the watershed, and its history as a preeminent test bed for watershed-scale sediment flux over the last few decades. This work provided the initial forensic setting from which to explore the roles of wildfire and agricultural activity in a semi-arid, mountainous watershed. Previous studies found evidence for the primary role of the Salinas River in supplying sand to Monterey Bay and the significant reduction of coarse sediment export to the coast due to damming on the Salinas and neighboring Rivers . External controls on decadal to inter-decadal scale fluvial sediment flux patterns have been further investigated in terms of El Niño Southern Oscillation cycles . Integrated expressions of external and internal factors in the form of antecedent hydrologic conditions that are affected by ENSO have also been examined as controls on suspended sediment discharge regimes . One previous study has also addressed the importance of wildfire in controlling the sediment export from a major Salinas tributary . Inman and Jenkins conducted a regional scale study on suspended sediment flux from central and southern California coastal rivers with a focus on episodic events and their relationship to regional climate cycles.

They found that large events dominated sediment transfer from the rivers in this region, including the Salinas, and that decadal scale wet and dry cycles lead to concomitant increases and decreases in suspended sediment flux to the ocean. Their approach to calculating suspended sediment discharge through the lower Salinas utilized a rating curve constructed from data collected by the USGS gauge station near Spreckels, CA during water years 1969-1979, which they applied to monthly averages of daily water discharge from 1944-1995, resulting in an estimated average annual suspended sediment discharge of 1.7 Mt yr-1 . Farnsworth and Milliman also examined the role of large discharge events in the estimation of total suspended sediment load at gauge S1, and used the same set of S1 USGS data to compute a power law rating curve that was then applied to daily water discharge data from 1930-2000 for an average annual suspended sediment discharge of 3.3 Mt yr-1 . Gray et al.thoroughly investigated suspended sediment dynamics in the Salinas River in relation to climatic and hydrological drivers. It was found that CSS-Q behavior was influenced by antecedent hydrologic conditions at event, annual, interannual anddecadal time scales , and that the temporal trend in discharge-corrected suspended sediment concentrations was negative over the 1967-2011 period of record . Notably, no change in the relationship between precipitation and discharge was found over this time period . By taking these factors and the temporal dependence of sediment behavior into consideration, the average annual suspended sediment flux was estimated as 2.1 to 2.4 Mt . The Arroyo Seco drains ~ 700 km2 of the wetter western mountains in the Salinas River watershed. The majority of the Arroyo Seco remains largely undeveloped and is undammed, unlike the other major Salinas River subbasins in this region. For this reason, the Arroyo Seco is a significant contributor of water and sediment to the Salinas River .

Warrick et al.found that sediment export from the Arroyo Seco was highly controlled by the sequence of wildfire and subsequent high precipitation/discharge events. Nearly complete burning of the upper Arroyo Seco watershed in the summers of 1977 and 2008 facilitated a pairwise comparison of post fire sediment flux past gauge A3 , in relation to storm timing and intensity differences . A rare , high intensity wet season during the 1978 water year led to an annual sediment flux ~ 100x pre-fire, while moderate precipitation in 2009 and 2010 resulted in only 5x and 9x increases, respectively. Warrick et al.estimated that the Arroyo Seco’s post-fire sediment flux may have caused the total Salinas River sediment to export to double in 1978.Discharge and a number of fluvial constituents, including suspended sediments, are monitored in the lower Salinas River at S1 and S2 . This study was based on 286 suspended sediment samples collected by the USGS between 1967 and 2010 , and 44 by the authors between 2008 and 2011 from gauging stations S1 and S2 and the Davis Street bridge crossing 4 km downstream from S1 . Potential biasing of the suspended sediment data set in terms of hydrologic regime representation and temporal distribution was examined and discounted in Gray et al. . Paired samples were collected by the authors from the water surface at cross-channel stations of one-quarter, one-half, and three-quarters wetted channel width , which were then processed for CSS and particle size distribution as per Gray et al. . Suspended sediment samples for this study were collected from the surface of river flow. For this reason coarse suspended sediment particles were expected to be underrepresented. Geographical and temporal wildfire data recorded by the California Department of Forestry and Fire Protection Fire Resource Assessment Program from 1911- 2010 were clipped to the geographic extent of the Salinas watershed . Contributing areas behind dams were masked for the time periods of their operation,planting blueberries in pots as the trapping efficiency of these dams was previously estimated as > 90% for fine sediment . The areal extent of fires in undammed portions of the watershed was then summed by year for further computations of effective burn area . The lasting, time-dependent effect of a given wildfire, or set of wildfires, on the landscape can be modeled as “effective” burn area, which is the initial burn area modified by standard exponential decay functions operating over the elapsed time since the fire . The range of relevant half-life values for the EBA decay function found for semiarid southern California systems is between 0.5–14 years, with 1.4 years identified as the best fit for the Arroyo Seco . In this study the EBA approach was applied to annual burn area data with a range of t½ values from 0.5–10 years in 0.5 year steps. As most Salinas fires occur during hot, dry summers, the EBA associated with a given CSS sample was calculated by summing of effective burn area contributions from all previous years, with the year before the water year of the sample treated as t = 0, while the annual burn area from the water year of the sample was excluded.

Spatial coverage of crops by year from 1965-2011 was obtained from the Monterey County Agricultural Commissioner’s Office Crop Reports and sorted into ‘field’ and ‘row’ crops . Data on the areal coverage agriculture by irrigation technique from 1993-2010 and urbanization from 1984-2010 were extracted from Monterey County, 2015b.Testing for the control of wildfire and agricultural activity on sediment production in the lower Salinas River was based on comparing the temporal trends of LOESS residuals for both fines and sand with the temporal trends of metrics for effective wildfire burn area, bulk and crop specific agricultural areas, and areas under given irrigation types. Temporal trend analysis was conducted with Mann-Kendall and linear regression. It was hypothesized that the decreasing CSS–Q relationships found in the lower Salinas were caused by one or a combination of the following changes in sediment controls over the sample period: decreased wildfire activity, decreased agricultural land area, changes in agricultural composition to less erosive crops, or changes to less erosive irrigation techniques. All data sets were examined for temporal trends in light of the 1967-2011 base period of CSS data, although the irrigation technique data set did not begin until 1993. Factors with statistically insignificant temporal trends and/or trend directions opposing those expected in light of decreasing CSS–Q were eliminated as potential controls. A correlation test was also performed between LOESS residuals and the wildfire burn area metric EBA, as short term responses in basin scale sediment production would be expected from wildfire disturbances. A significant positive correlation between the wildfire and suspended sediment magnitudes would be considered indicative of wildfire as a potential dominant control on decadal scale trends in sediment production, since wildfire has been generally found to increase the production of sediment to a greater degree than water in the steep, brush dominated environments typical of the primary source areas in the Salinas River . Correlation tests were not performed on the agricultural metrics as they were expected to only produce decadal to inter-decadal sediment production responses rather than abrupt shifts in sediment supply dynamics due to the slow rate of change of these factors. As Warrick et al.had found that wildfire affected ~ 100 times more sediment yield from the Arroyo Seco subbasin when followed by a winter of intense storm events, EBA was also examined in concert with peak daily Q for each year. The values of LOESS residuals for years with high EBA values were then compared with consideration given to the peak daily Q experienced by these years, to determine if the convolution of wildfire and subsequent large storms was responsible for temporal patterns of suspended sediment behavior at the annual scale.Beginning in the mid-1960s dry field products like barley and animal feed declined and root products such as sugar beets and potatoes all but disappeared . Intensively irrigated row crops and utilization of land for multiple cropping seasons expanded, including rapid increases in leaf lettuce beginning in the early 1980s, while grape production expanded rapidly from 6–141 km2 between 1971 and 1974, mostly on converted field crop and grazing lands. As a result of these crop changes and the limited use of efficient drip irrigation , groundwater withdrawals are estimated to have increased during the first half of the suspended sediment record . Monterey County, which is mostly contiguous with the Salinas watershed, began recording groundwater withdrawals in 1993 . Between 1993 and 2010 total ground water extraction actually decreased from 0.62 to 0.57 km3 , although irrigated agricultural land area increased from 702 to 732 km2 and total crop area increased from 1270 to 1588 km2 .

These studies include a wide range of process-based crop models as well as empirical papers, published between the late 1990s and 2012, and they vary substantially in the geographic regions examined as well as the extent to which they include on-farm adaptations. In this paper we focus on maize, rice, soy and wheat, four crops that make up a major part of the scientific literature on climate impacts on crops. These crops collectively account for approximately 20% of the value of global agricultural production, 65% of harvested crop area, and just under 50% of calories directly consumed . For these four crops, the database contains 1010 data-points . Of these, 451 are reported as including some form of onfarm, within-crop agronomic adaptation. The majority of these adaptations involve adjusting either planting date , cultivar or both . In total, 28 models are represented in the 56 studies used for the estimation, made up of 17 process-based model families and 11 statistical models. For this analysis we have complemented the existing database in two ways. Firstly, we coded each study based on whether a process-based or empirical approach was used. Secondly we added baseline growing-season temperatures to the database. To do this, each data point was assigned to a country. 86% of points come from studies located in a single country. For the remaining 14% coming from studies with an international scope the assigned country was the country with the highest production of the relevant crop. Average growing season temperatures were calculated using planting and harvest dates from Sacks et al and gridded monthly temperature from the Climate Research Unit . We treat the database of studies as a kind of ‘ensemble of opportunity’ . The benefits of this approach are that predictions from multi-model ensembles have been shown to consistently out-perform individual models in both climate modeling and, increasingly, in agricultural modeling .

Though not derived from a formal ensemble modeling project,growing raspberries in pots the universe of individual studies contained in the database can be thought of as draws from a set of possible models, each of which captures the response of crops to changing climate conditions with some error. The sampling of models is not systematic or random, but instead has emerged from scientific research and associated peer review of work on climate effects on crop yields over the last two decades. The approach described here takes advantage of an implicit weighting derived from representation of models within the scientific literature. To the extent that this representation reflects researchers’ judgements about the best model to use for particular crops in particular locations, it may be that this implicit weighting emerging from the scientific literature is preferred to simpler one-model one-vote aggregation schemes . We use multiple regression to aggregate the results from individual studies to an ensemble average response function. This approach allows us to estimate common response functions at an appropriate level of aggregation. For example, every study in the database examines the effect of change in temperatures on yields, allowing us to estimate separate yield response functions by crop and by baseline temperature, as well as by type of study. Fewer studies examine the effect of CO2 fertilization or adaptation, limiting our ability to model heterogeneity in response to changes in these variables.The effect of adaptation on crop yields is modelled with both an intercept term . This is prompted by the observation that in many studies that report including on-farm agronomic adaptations, adaptation is represented by changing management practices that would improve yields even in the current climate . Failing to include an adaptation intercept in this context will lead to an over-estimation of the potential of the adaptation actions included in these studies to reduce the negative impacts of a warming climate. We therefore include an adaptation intercept in the estimating equation but then subtract it out to produce a damage function that goes through the origin. The true effect of adaptation is captured by the interaction with temperature change, given by the b8 term in equation .

This term reflects the effect of management changes that are not beneficial today but that will be beneficial under a changed climate, the standard definition of adaptation. To estimate the impact of local warming for a prespecified increase in global mean temperature we use pattern-scaling between local and global temperature changes based on the CMIP5 multi-model mean for RCP8.5 . The multi-model mean was calculated using the Climate Explorer tool using methods documented in van Oldenborg et al . For each grid cell we take the change in temperature between a future and baseline period and divide by the mean global warming over this time period. Local warming is greater than global average warming over land areas and is larger at high latitudes and in continental interiors. Gridded local temperature changes are combined with the response functions estimated using equation and baseline growing season temperatures based on CRU and Sacks et al to give projected changes in yield with warming on a 0.5 degree grid. Global average yield changes are calculated by production-weighting the gridded data using production data for the relevant crop in the year 2000 . In presenting results, we focus on global temperature changes ranging from 1 to 3 °C and use 4 cases to examine the importance of different variables. The reference case is based on the temperature response curves for process-based models, including CO2 and adaptation. The ‘No CO2’ case is the same as the reference except without CO2 fertilization. ‘No Adaptation’ is the same as the reference except excluding adaptation. And finally, the ‘Statistical’ case is the same as the reference except the temperature response comes from statistical studies. In order to assess the economic implications of alternative studies of climate impacts on crop yields, we use the Global Trade Analysis Project model . GTAP is a global, computable general equilibrium model which seeks to predict changes in bilateral trade flows, production, consumption, intermediate use and welfare, owing to changes in technology, policies or other exogenous shocks. In this case, we treat the climate-induced yield changes as Hicks-neutral productivity changes. Thus, a 10% yield loss would mean that, if farmers did not alter their practices in the face of the changing climate, application of the same inputs to the same amount of land would result in 10% less output. The economic model does allow for changes in area planted, as well as input intensities, in response to changing relative prices, so actual yields will not change by 10%.

In this sense, all of the economic results reported here allow for economic adaptation . In order to implement the yield shocks under the different climate scenarios, we aggregate the gridded impacts for each of the four crops to the level of the 140 countries/regions in the version 9 GTAP data base . Since maize and soybeans are part of larger crop aggregates in the GTAP data base , the climate-induced yield shocks are diluted by multiplying them by the share of the country-commodity aggregate made up of maize and soybeans, respectively. Thus in a region which does not produce soybeans, the climate shock would be zero, whereas a country in which maize was the only coarse grain produced would experience precisely the productivity shock specified by the aggregated maize results for that geographic region. To be consistent with these incomplete agricultural yield shocks,plant pot with drainage when it comes to reporting the welfare losses, we report the losses as a share of that incomplete production value .Figure 2 shows the temperature response functions estimated from equation . Warming has a negative impact on yields that is worse for maize and wheat than for the more heat-tolerant rice. It is striking that we find very little evidence for any yield benefits from warming over most growing areas—our point estimates are negative even for warming less than 1 °C and even in the 25th percentile of growing season temperatures . This negative impact is generally statistically significant for process based model results at warming above 2 °C. Standard errors for results from statistical models are much larger and bracket zero in almost all cases. The interaction with baseline temperature is in the expected direction: warming is less damaging for crops in cooler locations. Figure S1 shows the gridded yield responses to 2 °C of global average warming for each crop. While many areas see negative impacts, there are some positive effects in the boreal zone and in cooler temperate areas. In figure S2 we show a comparison between our estimated response to a 1 °C warming and the mean of multiple process-based crop models calibrated to specific locations as part of the Agricultural Modeling Inter-comparison and Improvement Project for the three crops that are available: maize , wheat , and rice . The results from these two very different methods are close for both wheat and maize while the findings for rice show more variability. In all cases the AgMIP data are well within the 95% confidence interval produced in this study. Figure 3 shows how the type of study, inclusion of adaptation , and the CO2 fertilization effect affect the climate change response. Point estimates for b3 and b4 suggest that on average results from statistical studies are slightly more optimistic than results from process-based models for small amounts of warming and more pessimistic for higher levels of warming. Error bars for the statistical model are extremely large though, particularly for warming beyond 2 °C, which is perhaps unsurprising given the concentration of empirical results at 1° warming documented in figure 1. The point estimate for the effect of adaptation is in the opposite direction from what would be expected , but the error bars are large and the effect is not distinguishable from zero.

Studies that include adaptation do have more positive yield outcomes than studies that don’t, but those benefits are captured entirely in the adaptation intercept term . Therefore, these findings suggest that most within-crop agronomic adaptation measures represented in process based modeling studies would provide the same benefit under the current climate as they would under future climates. In other words, they are actions that shift the supply curve out to the right but do not change the marginal impacts of future warming, consistent with the ‘adaptation illusion’ identified by Lobell . Finally, the CO2 response functions show statistically-significant benefits of CO2 fertilization that a symptote at 17.3% for C3 crops and 10.6% for C4 crops. Given the functional form assumption, this translates to yield gains of 11.5% and 8.5% for a doubling of CO2 from pre-industrial levels. For C3 crops, this value is close to that obtained from FACE experiments which range between 12 and 14% yield gains for a doubling of CO2 . and therefore this may not be a fully independent validation of the meta-analysis results.Fewer FACE experiments have been performed for C4 crops but available experimental data, as well as theory, suggest C4 crops will benefit less from CO2 except under water-stressed conditions. Figure 4 shows global production-weighted yield losses for a global temperature change of 1 °C–3 °C for four cases. Except for soybeans, the reference case that includes CO2 fertilization and adaptation shows positive effects on yields at low levels of warming, becoming negative between 2 °C–3 °C of warming. Variation between the different cases reflects what might be expected given the response curves shown in figure 3. CO2 fertilization is most important– excluding the CO2 effect produces substantial losses for all crops ranging from 14% to 25% at 3 °C of warming. The effect of excluding adaptation is very small. The effect of statistical as opposed to process-based studies is small and slightly positive for 1 °C–2 °C, becoming slightly negative at 3 °C. At higher levels of temperature change we would expect this effect to become more negative . The 95% confidence intervals are large and mostly bracket zero, with the exception of the No CO2 case at 3 °C of global average warming. Uncertainties are particularly large for soybeans and for the statistical case at 3 °C of warming—both instances where the number of data points in the meta-analysis are limited.

With respect to this aim of separate the role of demand for and supply of labor as drivers of sectoral reallocation, our work is, in fact, mostly related to Lee and Wolpin . Lee and Wolpin devised and structurally estimated a rich model to study the process of labor reallocation from manufacturing to services in the United States. We see our work as complementary to it, to the extent that we are interested in a similar question, but we tackle it from a different perspective. Specifically, our approach aims to impose the minimal structure to interpret the data, closer in spirit to the accounting literature. Relative to the three papers above, we also depart in extending our empirical analysis to as many countries as we could gather data for, rather than focusing only on the United States. Using multiple countries allows us to provide additional ways to identify the size of the mobility frictions, which is an important component of our analysis. More broadly, our work is related to a rich literature that studied the contribution of human capital to growth and development. This literature showed that the level of human capital is significantly correlated with consequent growth , Barro , Mankiw et al.. However, the effects of changes in human capital stocks have been much more elusive and Pritchett. Pritchett in particular, in a famous article, asked: “Where has all the education gone?”. In this respect, our work provides some encouraging answers: we show that growth of human capital stocks matters for explaining reallocation out of agriculture. Methodologically, we are in debt to the approach developed by the growth and developing accounting literature , Barro , Hsieh and Klenow , and more recently Gennaioli et al.. Relative to this literature, we show that observable variation across birth-cohorts can be used in an accounting framework,gallon pot and we introduced a way to measure the role of human capital without having to rely on prices.

In terms of the purely empirical contribution of this paper – that is, documenting novel cross country patterns in reallocation out of agriculture by cohort – our work relates to Kim and Topel , Lee and Wolpin , and Perez , which document sectorial reallocation by cohort but limit their focus to, respectively, South Korea and United States and Argentina; and especially to Hobijn et al. , which, in ongoing work, is also using the IPUMS dataset to document patterns on reallocation across sectors by cohorts, and use them to motivate a model linking demographic forces to structural change. Our model combines elements and insights already presents in Matsuyama , Lucas , and Herrendorf and Schoellman . To the best of our knowledge, we are the first to provide a tractable framework to analytically characterize reallocation within and across-cohorts in a context with general mobility frictions. Hsieh et al. also exploits year and cohort effects to calibrate a model of allocation of talent. It uses them to discipline the relative role, for the aggregate efficiency of the allocation of talent, of changes in frictions that affect human capital investment and frictions that distort the labor market. Relative to this paper, we focus on a simpler framework that allows us to consider fixed-cost-type frictions, which turn out to be crucial to correctly identify the role of human capital. Finally, our work relates to a growing literature that uses longitudinal wage data to reconsider the agricultural productivity gaps and that shows that these gaps are more consistent with sorting across-sectors than with large mobility frictions; , Herrendorf and Schoellman , and Hicks et al.. We contribute to this literature in two ways: we provide a model that highlights when wage data can be informative on frictions; and we show, without relying on wage data, additional evidence corroborating the sorting explanation and casting doubts on the presence of large mobility frictions.We next describe how we use data to quantify the role of human capital in labor reallocation out of agriculture. This is motivated by the results in Proposition 2, which provide an accounting framework to link within- and across-cohorts labor reallocation to the relative contribution of human capital vs. prices/productivity in reallocation out of agriculture. In this Section, we start by documenting a number of novel cross-country facts about reallocation by cohort using micro level data for a large set of countries.

Most of the cross-country evidence available to date only covers aggregate rates of reallocation. Our paper is among the first to provide micro level evidence on the behavior of different cohorts of workers in the process of structural transformation. We present the patterns descriptively to convey information on what the data say in a transparent format, focusing on the novelty of the findings rather than on their role in our approach. In Section 4 we will instead interpret the observed patterns through the lens of theory. There, we make explicit how Proposition 2 can be brought to the data to make inference on whether human capital matters for the movement of workers out of agriculture. Below we introduce the data and measurement approach, and then discuss the novel crosscountry findings on reallocation by cohort.We use micro level data from the Integrated Public Use Microdata Series 8. The data are either censuses or large samples from labor force surveys that are representative of the entire population. We include in our analysis all IPUMS countries for which we have available at least two ten-years apart repeated cross-sections with available information on age, gender, and working industry. This gives us a sample of fifty two countries covering about two thirds of the world population. For fifty one countries, the IPUMS data also include geographical information at the sub-national level which we use in our analysis as a source of additional variation.9 For twenty three countries, we observe four or more cross-sections, for seventeen we observe three or more. On average, we observe countries over a period of 28 years. For some countries, such as United States and Brazil, our data cover a long time span of half a century or more of labor reallocation. Table A.1 in the Appendix lists the countries in our sample, the income level of each country, in 2010, relative to the one of United States, the years of coverage, the agricultural employment shares, and the number of observed cross-sections. The countries in the sample comprise a wide range of income levels, from the United States to Liberia and El Salvador. Eight countries are high-income countries, twenty five are middle-income countries and the remaining nineteen are low-income10. Our sample also spans a large geographical area, covering Asia and Oceania , Africa , Central and South America , and Europe and North America . We focus on males and restrict our attention to those aged 25 to 59. This is meant to capture working age and identify the period after education investment is completed, which allows to consider human capital as constant.

We exclude women from the current analysis given the large cross-country differences in female labor force participation.In the model, we assumed that each cohort has equal size and that size is constant over the life-cycle. In the data, however, we observe that cohorts have different sizes,gallon nursery pot and that the size of a given cohort changes over time due to mortality. We may be concerned that these demographic compositional effects are relevant in explaining the cross-country variation in cohort and year effects. We here perform a series of exercise to show that, reassuringly, demographic composition does not mechanically drive our estimates. First, we compute the year effect weighting each cohort by its share in the population at time t + k rather than at time t. In Figure 8a, we show that this change does not make any difference. Second, we compute the year effect with the raw data – i.e. without smoothing the demographic distribution, which we did in the benchmark exercise to adjust for age-heaping. As is well know, Indian data suffer from extreme age-heaping. Consistent with this, we see in Figure 8b that only in India the year effect estimated with the raw data is different from the benchmark one. Third, we recompute the rate of labor reallocation out of agriculture keeping constant the demographic structure at time t – i.e. we compute LA,t+k weighting each cohort according to !t . Figure 8c, shows that the estimated rate of labor reallocation are almost identical to the benchmark ones. Finally – in Figure 8d – we perform the same exercise, but keeping constant the demographic structure at time t + k. Again, we conclude the demographic changes do not have relevant mechanical effects. In Figure A.3 in the appendix, we recompute the same exercise using sub-national units. We find similar results.This section introduces our empirical approach and discusses how we make use of the data and patterns described above to quantify, in an accounting sense, the relative contribution of human capital and prices/productivity to labor reallocation out of agriculture. Our main starting point is Proposition 2, which tells us that we can do so by leveraging within- and across-cohorts reallocation. Proposition 2 provides a mapping between two observable objects, year and cohort effects, and our two main objects of interest, the contribution of human capital and of prices/productivity to labor reallocation. The mapping, however, is made challenging by the possibility that labor mobility frictions bind, and by general equilibrium. The spirit of our empirical exercise in this section is to exploit – through multiple approaches – observable variation in year and cohort effects both across- and within-countries to discipline the size of the frictions. We then use further micro level data to calibrate the strength of the general equilibrium. We provide a range of estimates depending on parameter values, but the overarching conclusion, which we reach in Section 4.1, is that human capital explains approximately one third of total reallocation out of agriculture. In Section 4.2, we show that, at the same time, human capital has at most a minor role in explaining why some countries have faster reallocation rates than others.Next, we provide estimates for the size of the friction through a series of different exercises. The results of our estimates are summarized in Table 2. Conceptually, we follow two main alternative methods, each tied to a source of variation that can be exploited to back out the size of the frictions. Our first method builds on Proposition 3, which relates the size of labor mobility frictions to the reallocation rates of individuals of different ages. The fixed cost traps in agriculture individuals that would otherwise move to non-agriculture in a frictionless environment. This effect, however, is not symmetric across ages, and is instead stronger for older individuals: they benefit from future increases in non-agriculture wages for fewer years and hence, for given fixed cost, face a stronger constraint. This means that the presence of frictions causes old individuals – the constrained – to reallocate at a slower rate than young individuals – the unconstrained. Fact 3 in Section 3 showed that old and young individuals reallocate at similar rates, thus providing evidence against a sizable role for mobility frictions. The following Lemma shows that this intuition can be used to provide a direct estimate for the size of the frictions.In this section, we provide a proof of concept that it is possible to trigger reallocation out of agriculture through policies that successfully increase the educational attainment of the population. To do so, we identify the causal effect of schooling on labor reallocation out of agriculture in the context of a school construction program in Indonesia. Following the seminal work of Du- flo , we use the INPRES school construction program, which built 61,000 primary schools between 1974 and 1978, to provide quasi-experimental variation in schooling. While the intensity of the program, captured by the number of new schools per pupil, was not random, only somecohorts, those younger than 6 at the time the program started, were fully exposed to the program. Therefore, we can run a fairly standard difference-in-difference exercise: we compare cohorts fully exposed to the treatment to those not exposed to it, in districts with higher or lower treatment intensity.

With respect to anionic retention, etherification of rice straw increased the maximum adsorption capacity of sulfate from 11.68 to 74.76mg g−1 by introducing amino groups . Generation of positively charge, −C-N+ moieties, was responsible for the removal of sulfate by electrostatic interactions. Furthermore, triethylamine-etherification significantly accelerated the adsorption rate of wheat straw for anionic constituents . Adsorption equilibrium was achieved in 35min for HCrO4 − and 15min for H2PO4 −, compared with 3hr for the unaltered biomass. Accordingly, the adsorption capacity of modified wheat straw for HCrO4 − and H2PO4 − were 0.98 and 1.15mmol g−1 , respectively. Electrostatic attraction, complexation and ion exchange mechanisms were the most prominent sorption mechanisms contributing to cationic and anionic retention by etherified adsorbents.Carbonization is a thermal decomposition process of organic materials resulting in the production of a carbonaceous residue with a concomitant removal of distillates. Conversion of agricultural wastes into bio-char is a common carbonization process that has been extensively studied . Biochar products have a larger specific surface area, greater porosity and more functional groups than the raw agricultural wastes from which they were formed . The alkaline nature and the existence of mineral constituents on bio-char surfaces also promote the formation of metal precipitates on the bio-char surface . The fixation ability of original bio-char for pollutants is relatively limited and many studies have explored altering pyrolysis conditions to generate activated bio-chars with altered functional groups and rougher surfaces to enhance sorption capacities for various pollutants. Carbonization temperature has an especially strong effect on the properties of bio-char generated from agricultural waste . In general, bio-char produced at lower temperatures contains a greater functional group content,black plastic plant pots whereas those produced at higher temperatures have a more porous surface and overall porosity .

For example, aldehyde and ketone functionalities formed at ~200°C and became dominant at 300~500°C, increasing the adsorption capacity for various pollutants . However, the drastic fusion of the ring structures in bio-char occurred from 550 to 650°C, which decreased the content of functional groups and weakened the chemical fixation for pollutants. The influence of feedstock source is also an important factor determining the efficiency of pollutant removal by agricultural waste-derived bio-chars. The type of functional groups and chemical components comprising various agricultural wastes are different, thereby significantly affecting the adsorption performance. The most important of these properties in relation to the adsorption capacity were the O/C ratio, the P content and the ash content . The capacity of the bio-char to retain Cu present in solution depended on the size of the inorganic fraction and varied in the following order: rice bio-char > chicken manure bio-char > olive mill waste bio-char > acacia bio-char > eucalyptus bio-char > corn cob bio-char. The addition of chemical amendments to the biomass feedstock has an appreciable effect on bio-char characteristics and the adsorption performance of the bio-char material. The selective introduction of functional groups, heteroatoms, metal atoms into bio-char can improve its specific properties. In-situ synthesis of bio-char refers to the preparation of modified bio-char in only one step by simultaneously pyrolyzing reagents and agriculture wastes. As a result, the modified bio-char has the optimized physicochemical structures and properties. Postoptimization of bio-char is useful to further enhance its potential value after pyrolysis, a special bio-char could be designed via the further surface modification and pore regulation. For example, H2SO4 addition during carbonization generated excellent sorption performance for methylene blue, brilliant green, crystal violet and orange G by formation of -SO3 groups on the bio-char surface that increased the chemical afinity for pollutants through covalent bonding . Similarly, HNO3 addition to the feedstock generated N-containing functional groups on bio-char, with the positive ≡N+ group interacting with the negative -O≡, N=C and heterocycle N-C groups on methylene blue to facilitate adsorption . In addition, the weakly acidic FeCl3 would be transformed to Fe2O3 during the preparation process of bio-char, increasing the Fe-O functional groups strongly on the surface of bio-char .

The peaks of Fe 2p1/2 and Fe 2p3/2 shifted after the stabilization of Cd, suggesting that the iron-bound functional groups participated in the Cd retention actively. The addition of alkaline reagents was shown to increase both surface area and functional group content of bio-chars. Activation of orange peel bio-char with CO2 and KOH strongly altered several physicochemical properties . During pyrolysis, CO2 reacted with K2CO3 to generate NH3 and create a more porous structure with increased N-containing groups, which correspondingly increased the retention of methylene blue. Addition of oxidizing agents is a common technique for generating higher concentrations of oxygenated functional groups on bio-char surfaces . Co-pyrolyzed peanut hulls with H2O2 resulted in an increase in the O content and surface carboxyl functional groups, improving chemical adsorption . As another example of oxidation effects, a KMnO4-treated hickory wood bio-char surface was covered with ultrafne MnOx particles exhibiting a surface enrichment of O-containing functional groups and a higher surface area . Metal retention by this engineered bio-char mainly resulted from surface adsorption mechanisms involving both the surface MnOx particles and O-containing functional groups. A myriad of studies have demonstrated the efficacy of carbonization as an effective method for improving the adsorption performance of agricultural wastes, especially for modified bio-char adsorbents . The total adsorption capacity for Cd2+ increased due to the introduction of C-S complexes on bio-char that facilitated the retention of Cd2+ through a strong bond with S . Wang et al. showed that maximum acid red 18 dye sorption capacities for NH4Cl/CH3COONH4-modified adsorbents were 1.41 and 1.18 times higher than for non-N-doped bio-char. The enhanced sorption affinity was mainly attributed to π-π EDA interactions between pyridine-N groups and acid red 18. The N functional groups enhanced the surface polarity, thereby increasing interactions with adsorbates, such as acid red 18. In addition, the amino functional group greatly improved the adsorption behavior of Cr6+ by polyethylenimine-treated rice husk bio-char . A pseudo-second-order kinetic model indicated a maximum Cr6+ adsorption capacity of 435.7mg g−1 compared to only 23.09mg g−1 for natural bio-char. Moreover, this new material exhibited excellent cyclic adsorption ability making it a low cost, ecofriendly adsorbent.

In summary, carbonized agricultural wastes are an economical and effective approach for pollutant removal from water owing to their intrinsic physicochemical characteristics. Abundant functional groups, high surface area and a porous structure allow bio-chars to rapidly interact with pollutants through a variety of adsorption mechanisms . Furthermore, introduction of chemical amendments to the carbonization process can generate specific chemical moieties that selectively target the removal of specific pollutants.Magnetization of modified agricultural wastes, such as bio-char, is a strategy to introduce transition metals or their oxides into the organic matrix to create a material that is easily separable with an external magnet. Importantly, magnetic adsorbents can be easily removed making them highly effective for the removal of pollutants from aqueous solutions. Moreover, the doping of metals or metal oxides onto the surface of agricultural wastes can target specific functionalities to enhance adsorption properties, such as the modification of lignocellulose by magnetic materials to improve sorption of chloramphenicol while simultaneously allowing easy recovery and reuse of the material during adsorption applications . Nanoscale-zero-valent, iron-coated wheat straw exhibited better adsorption performance for removal of Cu2+ than the raw adsorbent . A portion of the Cu2+ was directly adsorbed by the wheat straw, whereas another fraction of the Cu2+ was frst reduced to zero-valent Cu and Cu2O, which were subsequently attached to the surface of the material as part of the crystalline Fe-oxide structure. Further, doping agricultural wastes with Fe oxides was shown to promote the removal of anionic As5+ due to enhanced surface interactions associated with the Fe oxides . The Fe-oxide modified sugarcane bagasse rapidly removed H2AsO4 − oxyanions by electrostatic interactions, ligand exchange and chelation reactions between the positively charged surface ≡FeOH2+ group and the negatively charged H2AsO4 −. Loading Fe3O4 on the surface of organic materials is a common technique for magnetization that can be achieved by ferrofuids,black plastic pots for plants microwave assisted and mechanochemical techniques . Co-precipitation is also widely adopted to synthesize and load Fe3O4 on material surfaces, the changes for -OH and Fe-O groups in Fe3O4-modified straw resulting from the combination of O in the straw with the Fe of Fe3O4 . The modification further increased the BET surface area from 3.37 to 23.56 m2 g−1 . Pb2+ was retained on the Fe3O4-modified straw through diffusion adsorption and chemical fixation with O-containing functional groups. Similarly, Khandanlou et al. demonstrated an increase in the pore volume and BET surface area by 12 and 22 times after Fe3O4 modification, respectively. The enhanced porosity and functional group activation prominently promoted the adsorption of Cu2+ and Pb2+. Further, a Fe3O4−doped organic adsorbent exhibited excellent sorption capacity for Cd2+ and Pb2+, with Fe-OH groups playing an important role in the adsorption mechanism . The modified adsorbent also had a more robust regeneration efficacy than the raw bagasse as the Fe3O4 stabilized the surface properties. The effect of SnO2/Fe3O4 doping on the adsorption affinity of reactive blue 4 and crystal violet by treated rice bran was investigated by Ma et al. .

They found that pore size was increased by 2~4nm and the FTIR peaks for Sn-O and Fe-O strengthened upon doping with SnO2/Fe3O4. The numerous -OH groups associated with the SnO2/Fe3O4-adsorbent interacted with the reactive blue 4 and crystal violet to effectively remove these polluting dyes from solution. The removal efficiencies of magnetic biosorbents for pollutants are listed in Table 6. Magnetization of wheat straw increased the adsorption capacity for Pb2+ by 23%, with an optimal contact time of 30min facilitating rapid processing . Baldikova et al. found that magnetized barley straw increased adsorption of methylene blue and crystal violet by 16.7% and 54.9%, respectively, due to creation of a larger surface area. Further, the SnO2/Fe3O4-doped rice bran showed a high adsorption affinity for reactive blue 4 and crystal violet with sorption capacities of 218.8 and 159.2mg g−1 , respectively . Characterization of the SnO2/Fe3O4-absorbent indicated that the surface was covered with abundant -OH functional groups, which played an important role in increasing the adsorption capacity through strong electron attraction. Moreover, Fe oxide-modified sugarcane bagasse showed a superior potential for H2AsO4 − removal compared to several other modification methods . Positively charged ≡FeOH2 + and negatively charged H2AsO4 − rapidly interacted through electrostatic interactions and ligand exchange reactions. Overall, magnetic adsorbents have demonstrated the capacity to efficiently remove heavy metals, anionic constituents, antibiotics and various dyes from water. The most prominent feature of magnetic modification is that a product can be easily recovered and reused, with improved the utilization efficiency and reduces production/use costs.Surfactants are compounds that consist of hydrophilic heads and hydrophobic tails, which can reduce the surface tension between different media when used as detergents and dispersing agents . The charge characteristics of the hydrophilic head classify the surfactants as ionic and non-ionic surfactant groups . Various functional groups occur in the structure of ionic surfactants, favoring the selective adsorption of various pollutants in solutions. Thus, surfactant modifications improve the surface hydrophobic/hydrophilic properties and enrich the variety and quantity of functional groups comprising agricultural wastes. Linear alkyl benzene sulphonates, secondary alkane sulphonates, alkyl trimethyl ammonium halides and quaternary ammonium-based compounds are common surfactants with widespread applications . Cationic surfactant treated agricultural wastes demonstrate a good potential for removal of anionic pollutants. Cetyl trimethyl ammonium bromide modified wheat straw obtained higher N , C and H contents than that of the origin wheat straw due to the loading of the surfactant . FTIR spectra indicated that the -CH2 peak strengthened while the -NH2 and -OH bands broadened in wheat straw after modification. Changes in these characteristics indicated that modification increased the number of ammonia functional groups, which combined with Congo red dye through ionic interactions. Moreover, the adsorption results indicated that π-π dispersion interactions between the surfactant and dye played an important role in the removal of the Congo red dye. Furthermore, Tamilarasi et al. found that cetyl trimethyl ammonium bromide formed a surfactant bilayer containing anion exchange properties due to reaction with acidic functional groups on palm fruit husk surfaces. The surfactant loading on the surface of the palm fruit husk surface reached a maximum at a cetyl trimethyl ammonium bromide concentration of 1.0% in solution.

Solutions will also require the provision of quality education to rural populations, including on the use of digital technologies,so that the agricultural and rural workforce can maximally benefit from new technologies and off-farm employment opportunities. To mitigate problems that arise during the farm labor transition and help prevent a reversal to agricultural policy distortions, adequate social protection systems that mitigate calls for agricultural protectionism must be developed. The decoupling of social protection from employment holds promise in that regard , with the massive expansion of social protection provisions across the globe in response to COVID-19, especially through cash transfers, providing useful experiences and platforms to build upon. The remainder of this paper discusses the impact and evolution of these different forces and reflects on a policy agenda that can leverage the future global food system to generate decent employment, accelerate poverty reduction, and attain shared prosperity. Work in agriculture tends to be seasonal and dispersed across space, with labor productivity often low and unpredictable. High fertility among rural and agricultural populations, partly in response to low and variable agricultural earnings, often contributes to low labor productivity. As countries become more affluent, their demand for nonfood goods and services increases, and their work forces shift out of agriculture into more stable, high-paying, jobs in industry and services.The development of food manufacturing and services is particularly important in the process of narrowing cross-sectoral income differences. These nodes of the AFS tend to be more labor-intensive and less high tech than other industries and services, more likely to employ women and unskilled workers ,drainage planter pot and less spatially concentrated .This pattern of structural transformation is evident historically in high-income countries and is currently unfolding in low-income countries .

Against this broad and sweeping background of structural transformation, what role will the AFS play as a source of employment and shared prosperity in the future? First, on-farm work will continue to be a major source of employment in poor countries. In low-income countries, as in much of Sub-Saharan Africa, a decrease in the share of the workforce employed in agriculture is still accompanied by an increase in agricultural employment in absolute terms. Given high population growth, the agricultural workforce is projected to continue swelling in the foreseeable future before it starts to decline . Therefore, in low-income countries, where most of the global agricultural workforce is still concentrated, the transition out of agriculture in the short run does not necessarily imply a smaller agricultural workforce overall. In these settings, the primary challenge is to improve the quality of farmers’ jobs, while also facilitating the transition out of agriculture. In many middle income countries, on the other hand, as well as historically in high income ones, the absolute number of agricultural workers has decreased over time,farm populations have “grayed,”and farm labor shortages in specific commodities at specific points in time have become a feature of the agricultural landscape. Second, agricultural labor productivity will continue to rise.The existence of a persistent and large productivity gap between non-agricultural and agricultural activities is received wisdom in development economics. It is often seen as proof that agriculture is intrinsically less productive and as suggestive that the policy solution for agricultural labor in the developing world lies in removing barriers that prevent people from exiting agriculture . Recent research, however, suggests that agricultural labor productivity is understated .

Using micro household data instead of national macro accounts, controlling for skill differences, and expressing productivity in terms of value per hour of labor , labor productivity in agriculture is not lower than in other sectors . This finding suggests that agriculture is not intrinsically less productive but, rather, underemployment in the sector is high, at least in the earlier stages of development. Underemployment is likely linked to the seasonal nature of agricultural production and high fertility rates . If the productivity gap is much smaller than generally assumed, a disproportionate focus on policies to remove barriers to sectoral or spatial migration, however well-intentioned, may be misplaced. In fact, if agricultural labor is only in surplus during the agricultural slack season , such policies may prove ineffective, or they may even exacerbate agricultural labor shortages during planting and harvesting . Improving agricultural productivity would enable a productive move out of agriculture, leaving a more productive agricultural labor force behind. This could be accomplished through the development of complementary activities during the slack season, such as double cropping through irrigation and mixed farming systems .These types of developments would maximize poverty reduction , in contrast to a scenario in which people leave agriculture due to distress following under investment. The road out of agriculture runs importantly through a path that increases labor productivity in agriculture. This agricultural job paradox remains underappreciated. It will eventually leave far fewer people in farming, but they will have better employment conditions, and there will be greater quantities of relatively cheap food available for those in the rest of the economy. This process is still not underway in earnest in many African low-income countries . Third, the successful exit of labor out of agriculture is intimately tied to a successful agricultural transformation . Food expenditure shares decline as incomes increase.

Food consumption patterns also change from primary staples to more protein- and micro-nutrient-rich diets .Eventually, societies tend to demand more processed and prepared foods; they may even develop food consumption patterns that involve eating as an “experience.” Societies become more dependent upon the downstream AFS as a result. This, in turn, opens up new employment opportunities off the farm in food processing, marketing, logistics, food retail, and food services. A fair number of farm workers who leave the farm remain within the broader food supply chain. In many low-income countries, off-farm work in the AFS already makes up about 25 to 33 percent of overall off-farm work . Off-farm AFS work is still relatively small as a share of total employment ; however, it rises to 25 percent when expressed in full-time equivalent employment as opposed to the number of people employed .The importance of off-farm employment in the overall AFS rises with income, from 9 percent of total AFS employment in Eastern and Southern Africa to 52 percent in Brazil and 80 percent in the United States . The share of off-farm AFS employment in total employment first rises and then falls . Asia’s experience shows that more successful countries develop their off-farm AFS as they pass through the structural transformation, and this leads to a more rapid reduction in poverty . In China, India, and Vietnam, the “supermarket revolution” has been more intense and rapid than in other developing regions, driven in part by private vertical coordination that has generated economic growth through the introduction of contracts, the creation of new credit and input markets, and tighter linkages between farmers and buyers . As agri-food systems develop, processing, logistics, and wholesale operations become more consolidated, incorporating advanced technologies in order to reduce costs and ensure timely availability of quality goods . In China and Vietnam, there has been an emerging shift from small- to large-scale processing, logistics, and storage,plant pot with drainage driven by large foreign investments in fixed plants . India and the Philippines enacted laws that prevented foreign direct investment from entering the retail food sector, leading to slower growth . Domestically funded market hubs have emerged in India, and they are expanding rapidly, effectively bringing modern markets to farmers .14 Non-farm AFS jobs are often also more easily accessible for women and poor workers leaving the farm, given their proximity and low entry requirements in terms of capital and skills.A large part of employment opportunities within the AFS is happening in secondary cities and towns , increasing their potential for poverty reduction, as most of the poor live in the rural hinterlands of these intermediate centers . Several recent case studies support the beneficial effects of the AFS and related development of agri-food value chains on labor force participation, income, working conditions , and, in some cases, smallholder participation in modern markets. Examples from the Future of Work in Agriculture conference include Sauer et al. for domestic food systems in Tanzania, Edwards for post farm oil-processing farms in Indonesia,and Maertens and Fabry for horticulture exports from Senegal to European markets.

The latter shows how vertical integration of production to meet the quality and standards requirements for European markets increased not only labor force participation, employment, and income in the source areas but also educational attainment and a reduction in fertility rates—evidence that the development of agri-export supply chains contributes to the broader socio-demographic transformation, in addition to reducing poverty. COVID-19, by disproportionately affecting small and medium enterprises, may jeopardize the potential of these beneficial effects. The downstream AFS has expanded rapidly in developing countries across the globe as part of the transformation of food markets. Even in Africa and Asia, consumers now purchase 80 percent of all food consumed, implying that food value chains provide 80 percent of all food consumed . As a result, food value chains in the developing world have become longer, stretching from rural to urban areas. Fragmented into many labor-intensive, informal, small and medium enterprises,AFS nodes often operate in clusters such as dense sets of food processing SMEs, scores of meal vendors at truck stops, and dense masses of wholesalers and retailers in public wholesale and wet markets . This concentration of activity is vulnerable to lock downs and other restrictions. Since the COVID-19 outbreak, food supply chain disruptions have been widely observed across the developing world. Many of the system’s smaller actors are under capitalized, informal, and ineligible for government support. They stand to suffer the most without adequate SME support, paving the way for accelerated consolidation and lower labor intensity in the mid and downstream AFS nodes. Fourth, fears of a mass exodus of African youth out of agriculture, disproportionate with normal patterns of youth transition out of agriculture as countries develop, appear to be overblown. Given Africa’s youth bulge, youth employment is especially high on the continent’s policy agenda. There is a perception that African youth may no longer be interested in agriculture . Exit from agriculture is a normal part of the structural transformation, and rural youth, in general, are less involved in agriculture than their older cohorts. It is mostly through youth that the structural transformation occurs: young people on average are more agile, educated, and adaptive to changing labor market conditions. Rural youth typically have less access to land than their parents did at the same age because many parents are not ready to transfer the farm and land rental markets are underdeveloped. A recent study of sectoral employment transitions in six African countries shows that both adults and youth are leaving agriculture, but not disproportionately relative to these countries’ level of development . In their 13-country study, after controlling for location and agricultural potential, Dolislager et al. find that youth do not spend fewer hours in on-farm work than older adults in general, and only younger adults spend less time in own farming . Youth appear to access off-farm AFS employment more easily than non-AFS jobs, especially wage work in urban and peri-urban zones. For rural youth, gaining access to opportunities both inside and outside the AFS is important, but promoting employment opportunities within the AFS is more likely to bring employment opportunities within reach of the rural poor. Fifth, sociodemographic changes, including decreasing fertility rates, rising rural schooling levels, and increasing participation of women in the rural workforce, further stimulate labor to move from farm to the non-farm AFS as well as to non-AFS jobs. Liu et al. , for example, find that, in Vietnam, the potential for agriculture to address youth unemployment is limited. However, as wages converge between rural and urban sectors, the rural economy is diversifying into non-farm activities, and access to education has become the key driver of improvements in rural household well-being. Gender differentiated preferences may affect the farm-nonfarm labor transition, as well. A field experiment in Ghana uncovered evidence that traditional gender roles lead to a division of labor that causes women to prefer investments in non-agricultural activities . This finding highlights the need to recognize women’s preference to diversify into non-farm activities in regions where gender roles preclude women from engaging in agricultural production.

By 2014, raspberries represented 33% of the county’s total value of production for all berries. In contrast, Monterey County raspberry production accounted for only 6% of the county’s total berry value. Blackberries have not been consistently reported as a separate category in archived statistical analyses, but instead were often included under the terms “bush- or miscellaneous berries”. Therefore, similar data for blackberry acreage and value of production cannot be reported here. However, between 1990 and 2010, Santa Cruz County agricultural commissioner crop reports reported an upward trend for the broad category with respect to acreage planted and value of production . In 2010, blackberries were promoted to a position of prominence in the report and shown as a separate statistic; at the same time, the miscellaneous berry category was shown to be very small indeed. Between 2010 and 2014, however, blackberry acreage and value of production leveled off and have shown only modest gains . This may be because there has been less emphasis on production and market research and promotion for blackberries than for strawberries or raspberries. No comparable data are available for Monterey County. The two counties have contributed significantly to California’s total berry sector: in 2014, area strawberry acreage represented 35% of the statewide total, 37% of the total tons produced and 38% of the total value of production . Area raspberry acreage represented 43% of the statewide total, 42% of the total tons produced and 39% of the total value of production. Comparable statewide statistics are not available for blackberries. County agricultural commissioners’ reports show that the majority of all berries produced in the two counties — up to 98% — are sold as fresh market fruit . In years with adverse production conditions or low prices, a higher percentage of the crop may be diverted to the freezer or processed products market. Fresh market fruit is handled and sold primarily through local grower-shippers; a much smaller share is sold directly to consumers through farmers markets, community supported agriculture operations,10 liter drainage collection pot farmstands and other direct and intermediated market channels such as restaurants, independent grocers and schools.

Arguably the most momentous shift in cultural practices for strawberries was the introduction of preplant soil fumigants, beginning with chloropicrin in the 1950s and methyl bromide in the 1960s. Fumigation is a soil disinfestation practice that improves plant productivity and helps with the management of arthropods, nematodes, weeds, soilborne fungi and other plant pathogens. Some of the most difficult to control pathogens include Verticillium dahliae, Fusarium spp. and Macrophomina phaesolina. Without soil fumigation, these pathogens have the potential to completely destroy strawberry plantings. Early on, when CP and MB were mixed and applied together, the synergistic effects allowed strawberries to be produced as an annual rather than a biennial crop, and to be grown continuously on the same land without rotation to another crop, resulting in an increase in annual strawberry acreage. The use of fumigants also led to higher and more predictable yields and fruit quality, and further enabled the development of more stable markets for strawberries . Yields for strawberries statewide increased from a range of 2 to 4 tons per acre prior to the introduction of soil fumigants to 16 tons per acre by 1969 . Additional cultural improvements included the development of both UC and proprietary strawberry varieties uniquely adapted to coastal production conditions. Varieties were bred, for example, for disease resistance, yield and market potential. Notable UC-bred strawberry varieties include Tufts , Pajaro, Douglas, Chandler, and Selva , Camarosa and Seascape , and Aromas, Albion and Monterey . Irrigation practices also evolved, shifting from furrow irrigation in the 1960s to drip irrigation in the 1980s, which led to further improvements in plant disease management and greater water use efficiency. These and other enhancements meant that by 2012, yields could exceed 35 tons per acre . More recently, the strawberry industry has focused on “fine-tuning” fertility and water management for more efficient resource use, along with additional yield and fruit quality improvements . The Santa Cruz–Monterey area is also recognized for its early experience with conversion of conventional strawberry production to organic management .

Organic strawberry production was shown to result in lower yields, which, when offset bypremium prices could potentially offer higher net returns to growers. The importance of crop rotation for disease management was not addressed in the initial study by Gliessman et al. but has since been the focus of additional research, as have more complete analyses of the economics of organic strawberry production .Most notably, a long-term research commitment has been made to determine organically acceptable disease management practices such as anaerobic soil disinfestation , the use of commercially available soil-applied biological organisms and the incorporation of soil amendments such as mustard seed and its derivatives. The area is now seen as a global leader in organic strawberry research, and in 2012 the first organic strawberry production manual was published by UC Agriculture and Natural Resources . Statistics documenting expansion of the organic strawberry industry over time are not available on a county-by-county basis, but statistics for California show prodigious growth in acreage and value of production: from $9.7 million in 2000 to $93.6 million in 2012, a 621% increase in real dollars . Research points to several factors that have spurred consumer demand for all berries. Berries contain bioactive compounds, including essential vitamins, minerals, fiber and antioxidants that contribute to healthy diets, and that help to reduce the risks associated with some chronic diseases and cancers . This information has been widely shared with consumers through, for example, government programs promoting healthy eating , and more generic berry promotion programs . Per capita consumption of fresh strawberries in the United States almost doubled from 1994 to 2014, increasing from 4.1 to 8.0 pounds . U.S. per capita consumption of fresh raspberries was small by comparison, at just 0.5 pounds in 2014.

Similar consumption data are not available for blackberries, but Cook notes that consumers generally view berries as complementary, and that sales for all berries have increased. Indeed, in 2014, berry sales increased 5.8% over 2013; berries were the number one produce category for U.S. grocery retailers, at $5.7 billion in annual sales . Some berry operations also benefit from their proximity to the area’s urban centers, which have sizeable cohorts of educated, high-income consumers who generally demonstrate an interest in health and wellness, local agriculture and fresh and organic products. In addition to the more traditional grower shipper and direct marketing channels, new technology-driven food marketing companies — virtual food hubs — have evolved to cater to this demographic. They promote the values of sustainable communities, local food economies and business integrity and transparency, all important attributes for new 21st century consumers . These companies form relationships with local growers, provide some technical and market support, and enhance sales and engagement with consumers. It is not yet clear what impacts these still-niche marketing businesses may have on the industry in total. However, growers have responded to the various health and market signals by ramping up production of both conventional and organic products, berries included. Specialists and farm advisors with UC Cooperative Extension have performed economic analyses for Santa Cruz and Monterey county fresh market berry crops for decades . The studies estimate production costs for a representative enterprise based on characteristics common to the area’s farms. Data are collected from established growers, input suppliers and other industry experts so that a diversity of operations and practices are taken into account. Since 1990, UCCE researchers have used a farm budget software program to analyze the data and present results in several formats detailing costs for cultural and harvest practices,hydroponic vertical garden monthly cash costs and business and investment overhead costs. The studies also include an analysis estimating net returns to growers for several yield and price scenarios. Representative costs for food safety and environmental quality programs have been incorporated into more recent studies as they have evolved to become standard business practices. The resulting production and economic information is specifically designed to assist growers, bankers, researchers and government agencies with business and policy decisions. The first economic analysis of fresh market strawberry production for Santa Cruz and Monterey counties was performed in 1969; at least one subsequent analysis has been conducted every decade since then.

Though the level of detail and data included in each study has changed over time, some interesting trends can be noted. Annual land rent climbed from $150 per acre in 1969 to $2,700 in 2014, representing 2.5% and 5.5% of total production costs, respectively. The cost of soil fumigation for conventional strawberry production increased from $350 per acre in 1969 to $3,302 in 2010, representing 5.5% and 6.9% of total production costs, respectively. Production year water use gradually decreased from 80 acre-inches per acre in 1969 to 36 acre-inches by 1996 as drip irrigation became the standard. The amount of water used to bring a crop to harvest has remained roughly the same since that time; however, growers and researchers continue to investigate methods to increase water use efficiency even further. In some areas, soil types and fields, growers have been able to reduce per acre water use by several acre-inches more . When the above costs and water usage are assessed on a per ton rather than a per acre basis, production practice cost increases are less notable, and water savings even greater. Labor-intensive practices such as hand weeding and harvest are consistently shown as costly line items relative to other operations. Representative yields for conventionally produced fresh market strawberries rose from 20 tons per acre in the 1969 study to 30 tons in 2010, an increase of 50%. Even higher yields are discussed for some varieties and production conditions; county production statistics confirm that higher yields are indeed possible . Representative yields for organic strawberries, studied over a much shorter time period, rose from 15 tons per acre in 2006 to 17 tons in 2014, an increase of 13%. As more research is directed towards organic agriculture in general and strawberries in particular, yields will likely increase even more with time. Recent efforts include improvements in cultivar breeding, cultural practices and disease management, especially soil pathogen management. The most recent economic analyses for conventional, second year conventional and organic strawberry production were performed in 2010, 2011 and 2014, respectively. Second year conventional strawberries, or those producing a crop for a second year after having produced the first without replanting, represent about 15% of the total strawberry acreage in the area. Similarities and differences in total, cultural and pest management costs for the three management approaches are shown in figures 1 to 3. Total costs for conventional strawberries were $47,882 per acre and include expenses for all practices from land preparation to harvest . For the second year conventional strawberry crop, total costs were lower at $32,798 per acre, reflecting a reduction in expenditures for land preparation and reduced harvest costs because of lower yield. For organic strawberries, total costs were $49,044 per acre, slightly higher than for conventional production, mostly due to higher soil fertility input costs. Harvest, a labor-intensive practice, clearly represents the lion’s share of total costs, at 58% in organic production, 60% in conventional production and 67% in second year conventional berries. Cultural costs represent 26% of total costs in the conventional and organic systems, but only 15% for second year strawberries because there were no associated planting costs, and because pest management costs were lower . Looking more closely at pest management, soil fumigation is the highest cost category for conventional production at $3,302 per acre, with weed control, another labor-intensive practice, the highest cost in second year and organic strawberries at $1,212 and $2,506 per acre, respectively . However, for organic strawberries the cost to control insects ran a close second at $2,488 per acre, which was dominated by control for lygus bug with a bug vacuum, and two-spotted spider mite with the release of predatory mites. By comparison, estimated costs for insect control in conventional strawberries were lower at $702 per acre and still lower at $579 in second year conventional berries. Raspberry and blackberry production were not routinely studied in years prior to 2003. Since then, several primocane-bearing raspberry and floricane-bearing blackberry cost and return analyses have been performed, with the most recent studies conducted in 2012 and 2013, respectively.

From a mechanistic perspective, tillage directly changes the mixing of soil and crop residue as well as soil structure, which then affect soil biogeochemistry and crop performance through various mechanistic pathways . As such, all other impacts on water, energy, carbon and nutrient cycles from tillage are then simulated as an emergent outcome in a coherent way. In contrast, some models represent the effect of tillage as direct modification of evaporation flux and decomposition rates based on multiplication factors derived from empirical data , which introduces excessive parametric uncertainty and strong context dependence on the empirical data used for model parameterization. Simulate as many measurable variables as we can, such that the model simulation can be thoroughly validated, and measurable constraints can be easily incorporated to further improve the model simulation. For example, as discussed in Section 2.3, GPP largely determines the carbon input to the soil , and crop yield are major carbon outputs from cropland, thus models with observational constraints from ground or satellite measured GPP and/or crop yield will unsurprisingly outperform models without such constraints. From a mass-balance perspective.GPP could serve as a particularly strong constraint for quantifying litter and root exudates, two critical carbon cycle components that have significant spatial heterogeneity but are hard to measure . Another example is the recent paradigm shift from using conceptual and non-measurable SOC pools to using measurable SOC fractions for SOC simulation in process-based models . SOM is a complex mixture of materials with heterogeneous origins, chemical compounds, microbial accessibility, and turnover rates . Physical fractionation of SOM differentiate particulate organic matter and mineral-associated organic matter ,danish trolley which all are measurable in the laboratory and have different characteristic residence times .

Beyond the change in total SOC, quantifying the changes and distributions of POM and MAOM may help address the permanence issue of soil carbon credit. However, most previous soil carbon models simulate SOM dynamics as non-measurable fluxes between conceptually defined and non-measurable soil carbon pools . Only if POM and MAOM are properly conceptualized and represented in the models can they be used to simulate the changes of those SOM fractions and can measured SOM fractionation data be used as direct constraints for models . Model-data fusion here refers to a set of techniques that reduce the uncertainty of states and parameters of process-based models or data-driven models using local information to obtain improved estimation of carbon outcomes . MDF also has the ability to evolve by incorporating new sensors/sensing data or new model developments to this framework. MDF is the core part of the “System-of-Systems” solution, with the basic rationale that available observations can only see part of a system, but a model that has the necessary processes can leverage available observations to help constrain the overall system and thus improve prediction accuracy for the processes that observations do not see. The most successful example of MDF is weather forecast – the integration of weather models with satellite observation – leading to its everyday use by different industries . MDF is not a new concept in earth science and ecological studies , as methods such as Bayesian Inference, Data Assimilation, and Emergent Constraint have been extensively used to improve various predictions at some sites, watersheds, or relatively coarse spatial grids ; however, the use of MDF for field-level carbon outcome quantification has many new requirements. We propose a new MDF approach to enable MDF being conducted at every individual field level, while also quantifying critical components of the carbon cycle to inform both science and management practices.

Essentially, for every field in a targeted region, cross-scale sensing provides high-resolution and spatially-explicit E, M, C observations, which are then used as either inputs or constraints for a model with necessary processes represented , and a set of location-specific parameters will be constrained for every field. By doing so, carbon outcome quantification allows the uncertainty quantification at every field, and model verification at every field is also made possible when extra carbon-related observations can be used as independent validation data. This MDF approach to enable high resolution and spatially-explicit model constraining represents a major advance over any of the existing quantification protocols that only require validation at the regional scale. This new MDF approach fulfills the model validation needed to test whether a model or a solution has true scalability, which was defined earlier as the ability of a model to perform robustly with accepted accuracy on all targeted fields. Only models that can reproduce the accepted ‘accuracy’ at any random fields can be used as an accepted MRV tool for agricultural carbon outcome quantification. Meanwhile, such a new MDF calls for new computational techniques, as the conventional implementation of MDF techniques would be too computationally expensive to handle the field-level MDF. Take Champaign county in Illinois alone as an example, it has ~12,000 fields in active cultivation; and the state of Illinois has ~1,000,000 fields in active cultivation; conducting intensive MDF using traditional implementation for each of these fields is infeasible. Moving to AI-based solutions and fully leveraging GPU computing to facilitate efficient and effective scale-up of the field-scale MDF over a broad region is the only path forward, which will be discussed further in Section 3.4. Scaling a System-of-Systems solution to all the individual fields with similar accuracy and at a low cost is a twofold problem: cross-scale sensing to generate rich E, M, C information for constraining various aspects of agricultural carbon cycles ; and scalable application of MDF over millions of individual fields .

To reduce the computation cost to scale up, both problems require the inclusion of AI and a transition from CPU-heavy to GPU-heavy models on super computing or cloud-computing platforms for massive deployment. Below we will specifically discuss three pathways to help realize the upscaling of MDF, spanning across a spectrum of different levels of integrating process based models with AI. Pathway 1: The most straightforward path to reduce model uncertainty is to use MDF to constrain model parameters. However, the high computational cost of parameter optimization limits the scaling of MDF. A feasible bypass without massive re-coding is to leverage deep learning algorithms and develop GPU-based surrogate models. Forward inference of deep neural network-based surrogates can be orders of magnitude faster than CPU-based process-based models, making them particularly suitable for parameter calibration . Successful applications have been reported in hydrologic and Earth system models , this strategy is also practiced in other complex systems such as agroecosystem and climate models . Traditional parameter optimization algorithms work by iteratively searching for the optimal parameter combination to minimize an objective function , but may get stuck at random local optima where multiple parameter combinations correspond to identical model outputs. If parameters are calibrated for individual pixels, this illposed issue may lead to a discrete spatial distribution of the target parameters. Recently, neural network-based parameter learning methods have demonstrated promising possibilities to address this issue without a searching procedure . For example,vertical aeroponic tower garden the differentiable parameter learning framework developed by Tsai et al. enables the inference of model parameters by an unsupervised parameter learning network, which was automatically constrained by the surrogate network to produce reasonable parameter combinations in the training phase. Compared to the traditional SCE-UA method in calibrating the Variable Infiltration Capacity model, the parameter learning network estimates physically more sensible parameter sets with continuous spatial patterns because the inputs of the parameter network are themselves spatially coherent. Although AI-based surrogate models provide a pathway for the MDF upscaling, the objectives of further research should not be limited to speeding up the parameter calibration procedure but to exploring generalized pathways for estimating interpretable and reasonable model parameters. Pathway 2: The second pathway is a hybrid modeling approach to integrate machine learning and mechanistic modeling in one integrated modeling system to achieve computational efficiency, prediction accuracy and model transferability. Knowledge Guided machine learning is one such approach that learns complex patterns from data while incorporating domain-specific knowledge, such as physical rules , causality and nature of variables , informed by process-based models .

Preliminary success has been achieved in many topics including stream flow prediction , lake phosphorus and temperature estimation , and GHG emission modeling . In particular, the KGML-ag model developed by Liu et al. incorporated knowledge from the ecosys model into a GRU model and outperformed both the ecosys model and pure GRU model in predicting the complex temporal dynamics of N2O fluxes . The expanded KGML-ag method for quantifying carbon budgets exhibited strong agreement with the NEE measurements obtained from 11 eddycovariance sites . Combining KGML with Meta-learning may increase model transferability by accelerating hyperparameter learnings that account for spatial heterogeneity . Despite this early success, efforts to develop hybrid models are still in its nascent stage. Scaling field-level KGML for carbon accounting across millions of fields would require innovative approaches to assimilate multimodal remote and insitu sensing data, possibly by assimilating these data via low dimensional embeddings to constrain neural networks. Future research should also address multi-objective learnings, because existing KGML models are mostly mono-objective and lack synergistic considerations for the coupling of soil biogeochemistry. Pathway 3: Fully upgrading existing agroecosystem models to GPUaccelerated systems necessitates intensive code redesign and rewrite, thus requiring longer coordinated efforts with dedicated funding support . Based on previous explorations for Earth System Models  and specific challenges in agricultural carbon outcome quantification , the ideal GPU-accelerated agroecosystem models should have the following characteristics: having the same or higher level of performance and interpretability as in the original model; working freely in the GPU environment and be flexible enough to adapt to hardware improvements; and enabling the assimilation of generic data ensemble from multiple sources with different scales for efficient training/validating/fine tuning and on-time correcting. Progress is faster in upgrading modules with relatively known physical rules, such as in the areas of climate and hydrology than in biogeochemistry or human disturbance . For example, previous efforts on rewriting domain-specific language to adapt the GPU-accelerated systems succeeded in weather modeling  and climate modeling . An extensive effort is currently underway to adapt DOE ESMD/E3SM with modern machine learning techniques to next-generation architectures that are capable of GPU computing and generic data assimilation . The recently proposed concept of neural earth system modeling , aiming for a deep and interpretable integration of AI into ESMs, might be the closest solution for upgrading agroecosystem models as well. One profound step for such upgrading is to replace every submodule of the process-based model with a ML surrogate, and to train those surrogates jointly with real world observations. However, proceeding in this direction needs to conquer the challenge of mapping highly non-linear processes involving partial differential equations with different coefficients at different spatial and temporal resolutions. One solution that has shown some early success in predicting global atmospheric circulations is Fourier Neural Operator , a neural network specifically designed for solving an entire family of PDEs by learning mappings between functions in infinite-dimensional spaces . However, FNO is only one kind of “black box” neutral solver for PDEs. To be adopted in agroecosystem simulations, FNO needs to combine with other machine learning models to consider the connections and heterogeneity in space and time, and needs knowledge-guided constraints to provide predictions following physical/biogeochemical rules. Model validation, a procedure to benchmark model simulation with independent, high-quality observational data, is the only way to build model fidelity. The new MDF approach of high resolution and spatially-explicit model constraining essentially proposes a more strict way to test model scalability, defined as the ability of a model to perform robustly with accepted accuracy on all targeted fields. “Scalability” of a model or a solution should not only be demonstrated by model performance at a limited number of sites with rich data, where extensive parameter calibration is allowed; a true test of model “scalability” should be also demonstrated at many random sites, where only limited measurements are available. The latter is what a real world application entails – we are required to quantify the carbon outcomes at any given field. To achieve the above goal to fully validate the model scalability, a three-tier validation approach is needed, and results from these three tiers should be reported to the community for fair and transparent comparison.