Category: hydroponic agriculture

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The effect of this cumulative dose of multiple PPCP/EDCs is unknown

For instance, the half-lives of triclosan and estrone increased from 5.9 d to 8.9 d and from 0.6 d to 1.1 d, respectively, in soils previously exposed to WWTP effluent as compared to unexposed soils . Extensive microbial transformation results in the mineralization of PPCP/EDCs in soil to CO2 and hence complete decontamination. Mineralization is exclusively mediated by microbial transformations . For example, in 3 soils, 14C-estrone showed 15 – 85% mineralization after 100 d of incubation . About 15% of 14C-bisphenol A was mineralized after aerobic incubation in 4 soils for 120 d , while only 0.49 – 0.58%of sarafloxacin was mineralized after 80 d of aerobic incubation in 3 soils . After 27 d, 50% of 14C-naproxen was mineralized . This variability shows that mineralization is compound and soil specific, similar to other microbial transformation processes. However, at present there is a general scarcity of information, making it difficult to predict the relative impact of mineralization in the overall fate and risk of PPCP/EDCs in the soil-plant-human continuum. Microbial transformations may produce many intermediate products before the compound is fully mineralized or bound in soil. The formation of transformation intermediates in soil poses unknown risks as the new products may have biological activity . Due to analytical challenges in identifying unknown products in environmental matrices, very little information on transformation intermediates is available for PPCP/EDCs . A study showed that diclofenac was transformed to 5-hydroxydiclofenac and its p-benzoquinone imine in a bioreactor with river sediment, though the levels were not quantified . While the p-benzoquinone imine was formed transiently and in small quantities, it is the known to have high hepatoxic potential . In a separate study using an activated sludge bioreactor, 7 transformation products of diclofenac were found but none were identified . Ibuprofen formed hydroxyibuprofen in a pilot sewage plant and carboxyibuprofen in an oxic biofilm reactor . Overall, knowledge of PPCP/EDC transformation intermediates in the soil is extremely limited and warrants further investigation. The application of treated wastewater, bio-solids, or manure to land creates a potential for plants to take up PPCP/EDCs ,vertical hydroponics which may be beneficial in areas of phytoremediation, but in agricultural areas may contaminate food crops and create a possible route of human exposure through ingestion .

The few studies that have examined PPCP/EDC uptake by plants have reported accumulation by a variety of edible and non-edible plants, with accumulation varying among compounds, plant species, plant tissues, exposure concentrations, and exposure durations . While potential for plant uptake has been shown in laboratory settings, many of these experiments used artificially high concentrations that are not representative of environmental levels of PPCP/EDCs. The extent of plant accumulation in the environment has been scarcely studied. Calderón-Preciado et al. analyzed alfalfa and apple trees irrigated with water impacted by WWTP effluentand identified PPCP/EDCs in plant tissues at ng/kg – µg/kg levels, verifying that PPCP/EDCs are susceptible to plant accumulation under realistic agronomic conditions. Due to the extensive suite of PPCP/EDCs, it is not feasible to empirically measure plant uptake of each compound. Therefore, it is crucial to develop a mechanistic understanding of their accumulation to inform risk assessment. Many factors affect plant uptake of organic compounds, including compound hydrophobicity, ionization behavior, soil pH, soil organic matter, and plant transpiration . Uptake is generally a passive process, occurring by diffusion that is driven by water potential gradients . Due to transpiration driving the translocation of water through the plant, compounds which are neutral, polar, persistent, and non-volatile have the potential to concentrate in plants up to 100 times the concentration in soil . Most PPCP/EDCs are non-volatile , making this accumulation pathway relevant for some PPCP/EDCs. Ionic compounds, like phenoxy acid herbicides, have the possibility to be taken up by active transport, perhaps through processes designed for uptake of essential nutrients , and may reach higher concentrations than would be expected through passive diffusion . Since some PPCP/EDCs exist primarily in an ionic state , these compounds may potentially accumulate to high levels in plant tissues. The pH of the soil-water or hydroponic solution affects the fraction of ionizable compounds that is in the ionic form. For compounds that are partly ionized at environmental pH levels, basic compounds have increased uptake and acidic compounds have reduced uptake as pH increases , due to changes in the prevalence of the neutral fraction and ion-trapping effects as discussed below.

Accumulation in plant tissues is also related to the tissue composition. Hydrophobic compounds may partition to lipids, where they have the potential to accumulate. Therefore, plants with higher lipid contents may accumulate a compound to a greater degree . The partitioning of a compound to plant lipids is related to its Kow, as discussed below. In comparison, polar compounds are expected to reach equilibrium with the water present in plants and with relatively polar carbohydrates and proteins, which suggests accumulation of these compounds will likely be less extensive . No single model is currently available that accurately accounts for all of these factors , and very little validation of plant uptake models has been done for PPCP/EDCs.Compounds may be taken up by plants when plant roots reach contaminated areas and by mass flow or diffusion of dissolved compounds to roots . Entry is typically by diffusion of neutral compounds across the root membrane, and for ionizable compounds by a combination of diffusion of the neutral fraction and electrostatic interactions by the ionic fraction . A positive relationship has been shown between hydrophobicity and root uptake of neutral pesticides and other neutral compounds . The partitioning of neutral compounds to plant lipids is very similar to the partitioning to octanol, and thus uptake models use log Kow values with adjustments for other factors, including the amount of lipids in the tissue . Based on the partitioning behaviors of neutral compounds and that 1% of barley roots were lipids, Chiou et al. predicted that accumulation into root lipids compared to the rest of the root tissue accounted for 15% of uptake for compounds with log Kow ≤ 1, but ~100% of compounds with log Kow of > 3,hydroponic vertical farming systems showing that while lipids make up a very small part of plant tissue, they greatly affect accumulation behavior and may explain some uptake differences among plant species . For neutral compounds, root uptake is expected to be the greatest for compounds with high hydrophobicity and for plants with high lipid content . Models developed for neutral compounds may be inappropriate to describe the behavior of ionizable compounds, which includes many PPCP/EDCs. The accumulation of compounds in aerial tissue can occur via deposition from volatilized compounds, direct contact with irrigation or amendment materials, and translocation from root tissues . Since most PPCP/EDCs are polar and nonvolatile, volatilization and deposition is expected to be a very minor input for aerial tissue uptake . The extent of organic compound uptake by direct contact is not very well characterized and warrants further investigation. In general, it is expected to proceed by diffusion similar to root uptake. Most studies have focused on the translocation of PPCP/EDCs from roots, which is likely to become more important than direct contact with increased use of drip and other water-conserving irrigation methods that reduce the likelihood of direct contact between plant leaves and irrigation water. Aerial accumulation of neutral organic contaminants from root tissue involves movement of compounds into xylem and then translocation to aerial parts.

Concentrations in xylem are lower than root concentrations due to hydrophobic partitioning to root tissues, suggesting that hydrophobic compounds will be predominantly retained by roots while a greater portion of hydrophilic compounds will move to xylem and be translocated to aerial tissues . Accumulation in aerial tissue competes with compound return to roots tissues via phloem, and occurs by hydrophobic partitioning of compounds to lignin, which usually has much greater affinity for organic chemicals than carbohydrates or cellulose do . Overall, studies suggest that the maximum leaf uptake of neutral compounds may occur at log Kow values in the range of 1.8 – 3.08 . However, many of these studies utilized hydroponic systems, and it has been suggested that in a soil-plant system where uptake is in competition with soil sorption, that the optimal log Kow value would be closer to 0.75 for soil with 6% organic matter,1.25 for 1.25%, and 2 for 0.25% . Similar to root uptake, aerial uptake of ionizable PPCP/EDCs is a combination of neutral fraction uptake, which can be described with log Dow values, and ionic fraction uptake, which is controlled by electrical interactions. Anions are repulsed from all cell membranes except the tonoplast of vacuoles in root cells, so uptake of anionic PPCP/EDCs by xylem and aerial tissue is predicted to be small, except in cases of ion trapping . Cations are electrically attracted to most cell membranes, enhancing diffusion to many plant parts and resulting in generally moderate uptake ability, which may be further enhanced in alkaline soils by ion trap effect . After PPCP/EDCs have been taken up into plant tissues, a number of biological processes may occur that will reduce the bio-available fraction of the parent PPCP/EDC. Xenobiotics in general are quickly modified in a plant cell by enzymes, such as hydrolases or cytochrome p450, to enable conjugation with glutathione or glucose . The conjugated compounds may then be catabolized, creating a variety of transformation products, which are eventually mineralized or incorporated into the plant tissue . The pathways andrates of these metabolic processes are likely specific to each compound and plant species . As organic contaminants, PPCP/EDCs may be metabolized in plants to form transformation products and non-extractable residue, but this area needs further research. In one of the few studies available, Bokern and Harms used cell suspension cultures to identify toxicity and metabolism of 14C-nonylphenol. Plant species which were resistant to toxicity were most efficient at incorporating the compound into cell walls, primarily associated with lignin but also with pectin and hemicellulose. Extractable polar metabolites were also detected, showing that plant cells metabolized the nonylphenol into transformation products and non-extractable residue. In another study, Macherius et al. incubated carrot cell cultures and whole carrots with triclosan, methyl triclosan, and triclocarban. Triclosan was taken up and converted to 8 different conjugated compounds in cells due to bonding at its phenol moiety, but triclocarban and methyl triclosan were found to be taken up and not metabolized. These results suggest that metabolism of PPCP/EDCs in plant may vary widely with the compound, and some compounds may exist principally in their original form in plant tissue. This area needs more research due to its human health implications. Due to the scarcity of information about PPCP/EDC accumulation in edible plants, especially for real environmental situations, the potential of PPCP/EDC residue to have a biological effect in humans is unknown. Matamoros et al. predicted that human consumption of vegetable crops irrigated with water containing PPCP/EDCs would cause an exposure of 500 ng/d of each compound, a level well below the therapeutic dose for individual pharmaceuticals but in an active range for EDCs. Based on the accumulation in radish and ryegrass grown in soil with 0.4 – 19 µg/kg of carbamazepine, diclofenac, fluoxetine, propranolol, and triclosan, Carter et al. calculated that humans might consume 0.01 – 0.21% of an acceptable daily intake for each compound in root vegetables and 0.09 – 3.81% for leaf vegetables. The major exception in the study was the high accumulation of triclosan, which was predicted to reach 83.8% of ADI in leaf tissues, nearing the acceptable limit. These studies focused on the extractable parent compound measured in laboratory uptake studies. As discussed above, it is likely that a large portion of the accumulated PPCP/EDC may be in the form of transformation products, conjugated compounds, and non-extractable residue. While non-extractable residues of xenobiotics have significantly reduced biological activity in plants and appear to be primarily not bio-available to animal metabolism, conjugated compounds may be cleaved during animal metabolism andpotentially exert toxic effects . The presence of conjugated and transformed PPCP/EDCs in plant tissue is poorly understood and the health risks from them are far from clear. Pharmaceuticals and other anthropogenic chemicals are increasingly used around the world .

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The rate of methylation or demethylation appeared to be molecule -specific

Biologically mediated transformations such as methylation and demethylation may also occur in organisms such as D. magna after their uptake of CECs, which may further influence their toxicity. Methylation and demethylation in D. magna were investigated after exposing D. magna to the individual compounds. Methylation of the selected demethylated CECs was negligible, as no methylated product was detected in D. magna after its exposure to the corresponding demethylated counterpart. However, demethylation of diazepam, methylparaben and naproxen in D. magna was evident , while acetaminophen was not detected in D. magna exposed to Macetaminophen. The demethylation of methylparaben was limited, with a peak concentration of DM-methylparaben at 0.5 ± 0.0 nmol g -1 in D. magna after 12 h of exposure to 1 mg L-1 methylparaben. This represented only about 2.0% of the molar equivalent of methylparaben in D. magna. The demethylation of diazepam was found at similar levels, with DM-diazepam at 4.4% molar equivalent of diazepam. Interestingly, the molar equivalents of the demethylated derivatives increased over time during the depuration phase, even though the overall concentrations generally decreased over time. For example, the molar equivalents of DM-diazepam and DM-methylparaben reached 33.5% and 54.8% at the end of depuration, respectively. This may be attributed to the fact that demethylation continued during the depuration phase, which may have influenced the apparent depuration of these compounds .The demethylation of naproxen in D. magna was the most pronounced among the four methylated compounds,vertical garden growing with DM-naproxen generally detected at levels higher than naproxen itself during both the uptake and depuration phases . DM-naproxen was formed quickly in D. magna after exposure to naproxen, with 21.5 ± 2.7 nmol g-1 after 12 h into the uptake phase, which was significantly higher than that of the parent naproxen .

Similar to DM-diazepam and DMmethylparaben, the molar equivalent of DM-naproxen also continued to increase during the depuration phase. At the end of depuration, DM-naproxen accounted for approximately 88.9% of the total naproxen and DM-naproxen residues in D. magna. The high proportion of DM-naproxen in D. magna also suggested that demethylation was the primary metabolism pathway of naproxen in D. magna. To better understand the demethylation of CECs in D. magna, the formation rates of DM-diazepam, DM-methylparaben and DM-naproxen were estimated by simulating their formation over the initial 12-h period, during which good linear relationships between their formation and time were present . Formation rates showed no significant differences between DM-diazepam and DM-methylparaben. However, the formation rate of DM-naproxen was significantly greater than DM-diazepam or DM-methylparaben. Based on their respective chemical structure , the demethylation of diazepam and naproxen appears to differ slightly from that of methylparaben. While the demethylation of methylparaben involves the removal of a methyl group from a carboxyl group, which may be catalyzed bycarboxylesterases,CYP450s,or through non-enzymatic hydrolysis,the demethylation of M-acetaminophen, diazepam and naproxen reflects the removal of a methyl group from an amide or hydroxyl group, which likely is catalyzed mainly by CYP450s.Previous studies showed that carboxylesterases play a more important role in drug metabolism in invertebrates due to the lower activity of CYP450s.The more significant demethylation observed for naproxen in comparison to methylparaben suggests that CYP450s may also play an important role in the metabolism of such substrates in aquatic invertebrates. The observed significant differences in the demethylation rates of diazepam and naproxen imply that CYP450s in aquatic invertebrates like D. magna may exhibit different levels of activity towards different CECs.In vivo half-lives of the test compounds were derived from the depuration rate during the 24-h depuration phase in D. magna. The in silico half-life was estimated from the primary bio-transformation rate in fish and normalized to a 10 g fish at 15 °C based on the inherent characteristics of the QSAR model.

Similar to BCF values, in vivo and in silico half-lives could not be compared directly between the different organisms. Hence, the relative persistence of test compounds was calculated for evaluation. As shown in Table 2, in silico predictions suggest that methylation may prolong the persistence of CECs in fish. This was in contrast to the in vivo results in D. magna, which showed that methylation generally shortened the persistence of CECs. As mentioned above, methylated CECs generally accumulated faster with a larger ku value during the uptake phase, but dissipated rapidly during the depuration process. Considering that biota residing in wastewater effluent-dominated streams often experience pseudo-persistent exposure to CECs due to the constant discharge of effluents from WWTPs, uptake rates may be more important in regulating the accumulation of CECs in aquatic organisms dwelling in the impacted system. The prolonged bio-transformation half-lives of methylated CECs should be validated under field conditions. Overall, in silico predictions and experimental measurements were in agreement for the influences introduced by methylation or demethylation. This highlights the feasibility of incorporating QSAR models to evaluate the potential influence of common transformations such as methylation and demethylation on the environmental risks of CECs to non-target organisms in impacted ecosystems. Simple transformations such as methylation and demethylation contribute to the proliferation of the numbers of CECs and diverse structures in environmental compartments impacted by e.g., wastewater effluent.This study showed that these transformations can alter the physicochemical properties of CECs, resulting in changes in their environmental processes such as bio-accumulation and acute toxicity in aquatic organisms. These transformations of man-made chemicals may also take place within a non-target organism after their accumulation from the ambient environment.

Certain transformations, like methylation, likely result in enhanced bio-accumulation and increased toxicity in non-target organisms. Although not considered in this study, halogenation of man-made chemicals, such as gemfibrozil, 4-nonylphenol and naproxen, during the disinfection process in WWTPs, has also been reported, and the halogenated products generally exhibited increased bio-accumulation and toxicity to aquatic invertebrates.Due to the presence of numerous CECs in sources such as wastewater effluents and sediments, the co-existence of various TPs presents an additional challenge in addressing the overall environmental risks of man-made chemicals. It is important to note that high concentrations of test CECs and their corresponding methylated or demethylated TPs were used in this study in order to derive the LC50values and examine conversions in D. magna; these concentrations were above environmentally relevant levels. However, previous studies suggested that BCFs may be greater at lower exposure concentrations.Therefore, the effect of methylation or demethylation on bio-accumulation of CECs may be more pronounced than what was observed in this study. The environmental occurrence and concentrations of methylated or demethylated TPs are largely unknown for most CECs. Further research into the occurrence of TPs in different environmental compartments is needed to gain knowledge about the realistic exposure levels and to refine risk assessment. A major challenge in comprehensively assessing environmental risks is the sheer number of CECs and their TPs. It is unrealistic to experimentally evaluate transformation-induced changes in their environmental behaviors and toxicological profiles for all CECs.The incorporation of well-established QSAR models to predict essential chemical properties and environmental risk markers, such as hydrophobicity and lipophilicity, bio-accumulation potential, and acute toxicity, may help prioritize TPs with enhanced biological activities.This approach can be used to more effectively direct future research efforts to better understand the environmental significance of common transformation reactions for CECs. Four CECs and their methylated or demethylated TPs were comparatively evaluated for their uptake into A. thaliana cells or by wheat seedlings. The methylated compounds, generally more hydrophobic with a greater log Kow and log Dow,equipment for vertical farming often displayed a greater accumulation potential in both plant models as compared to their demethylated counterparts, with the exception of acetaminophen/M-acetaminophen in A. thaliana cells. The influence of methylation and demethylation on the translocation of CECs in wheat plants was molecular-specific. Methylation caused a significant increase in the translocation of acetaminophen, but a significant decrease for DM-diazepam. Methylation also generally prolonged the persistence of CECs in both A. thaliana cell culture media and wheat seedling hydroponic solution. A significant linear relationship was observed between log Dow and log BCF, indicating that the generally increased accumulation of methylated compounds may be attributed to their higher hydrophobicity. Results from this study suggested that common transformations such as methylation and demethylation may affect the persistence and accumulation of CECs in plants, and their role should be considered to obtain a more comprehensive understanding of the risks of CECs in the terrestrial environment including agro-food systems.

The interconversions between CECs and their methylated or demethylated TPs were evaluated in A. thaliana cells and wheat seedlings after their uptake. The methylation demethylation cycle was observed in both plant models, with demethylation generally taking place at a greater degree than methylation. Computation results showed that the chemical bond strength between the methyl group and the major molecular fragment in the methylated CECs followed a general order of methylparaben < diazepam < naproxen < M-acetaminophen, a pattern reflective of experimental observations for demethylation in A. thaliana cells. Future studies considering more chemical structures would help strengthen such QSAR models so that the potential for simple transformations such as methylation and demethylation may be predicted in the absence of experimental data. The acute toxicity of selected CECs and their methylated or demethylated TPs was further assessed by exposing D. magna to individually compounds. Methylation or demethylation resulted in changes in the acute toxicity for most CECs, and the influence was compound-specific. Methylation led to a significant increase in the acute toxicity of DM-methylparaben and DM-naproxen, but a decrease for acetaminophen. A significant negative linear relationship was observed between log LC50 values and log Dlipw values, indicating that as log Dlipw increased, the acute toxicity generally increased. Methylation increased the bio-accumulation in D. magna for acetaminophen, DM-methylparaben and DM-naproxen, and the increased bio-accumulation likely underlined the increases in acute toxicity for methylated compounds. In D. magna, active demethylation of diazepam, methylparaben and naproxen was observed, with the demethylation of naproxen especially pronounced, suggesting that enzymes in D. magna exhibited different levels of activity towards different substrates. QSAR models were used to predict changes in acute toxicity and bio-accumulation as a result of methylation, and the predicted values were in good agreement with experimental observations. The exploratory research presented in this dissertation clearly showed that simple transformations such as methylation and demethylation can significantly change the physico chemical properties of CECs and subsequently cause changes in their environmental behaviors such as accumulation by plants and aquatic organisms, toxicity and persistence. Methylation generally leads to increased hydrophobicity and further greater bio-accumulation and acute toxicity. However, exceptions were also observed in this study, suggesting that specific molecular structures may respond differently to the impact of simple transformations. QSAR models using molecular descriptors have the capability to predict the easiness of transformation reactions such as methylation and demethylation, the subsequent changes in physicochemical properties from such transformations, and further, the ensuing changes in bio-accumulation, translocation, and toxicity. Such models should be calibrated with more experimental observations and by the inclusion of more diverse structures. Such predictive tools are extremely valuable, given the enormous number of CECs and their transformation products, which renders experimentation-based approaches largely infeasible. This dissertation research highlights the prevalence of simple transformations such as methylation and demethylation in the environment, and the need to consider such transformations in achieving a more comprehensive understanding of the environmental fate and risks of CECs.Results from this dissertation research and a few other studies showed that simple transformations can effectively influence the environmental behaviors of CECs, and the effect is specific to molecular structures. Changes in bio-accumulation and toxicity due to transformations should be further evaluated under environmentally relevant conditions. The greatest challenge to understanding the environmental risks of CECs is the sheer number of CECs and their metabolites. In the absence of experimental data, predictive tools such as QSAR models and computational chemistry should be used to predict the possibility for the occurrence of transformations as well as the changes in physicochemical properties accompanying these transformations. Likewise, modeling may be also used to estimate changes in environmental behaviors and risks for CECs that are susceptible to transformations. It must be noted that only methylation and demethylation were considered in this research.

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These chemicals are known to have adverse effects on non-target aquatic organisms

Glycosylation was observed for diclofenac, sulfamethoxazole and di-n-butyl phthalate in A. thaliana, triclosan, naproxen, diclofenac, ibuprofen and gemfibrozil in carrot cell cultures,bisphenol A and carbamazepine in lettuce,and TBBPA in pumpkin seedlings.Acetaminophen and chlortetracycline were conjugated with glutathione in cucumber seedlings and maize seedlings, respectively.Conjugation with other biomolecules, such as saccharides, malonic acid, and sulfate, was also occasionally reported for some CECs, such as triclosan in carrot cells and diclofenac in A. thaliana cells.Other than conjugation, methylation is also a type of phase II metabolism and has been reported for TBBPA in pumpkin seedlings. The investigation of phase III metabolism of CECs in plants is relatively limited, as quantitative evaluation of phase III products would require the use of isotope labeling to account for the nonextractable or bound residues, although phase III is expected to be dominant in determining the final destination of xenobiotics in plants. For instance, nearly all 14C- labeled naproxen, diclofenac, bisphenol A and nonylphenol were found in nonextractable bound residues in lettuce and collards.Numerous studies have documented the occurrence of CECs in aquatic environments in many countries and regions. CECs are introduced into aquatic environments via discharge of TWW from WWTPs, agricultural activity, landfill leachates, and surface runoff . Concentrations of CECs ranges from ng L-1 to μg L-1 in impacted surface water and from μg kg-1 to mg kg-1 in the sediment. The concentration of CECs in surface water is largely influenced by the population density, environmental conditions and terrigenous supply, displaying temporal and spatial variations. Studies conducted in the U.S. in recent decades have shown the occurrence of hundreds of CECs in various watersheds, with the maximum concentration at 35 μg L-1 for sucralose in water. A comprehensive review of the occurrence of CECs in Latin America, including studies performed in 11 different countries between 1999 and 2018,vertical planters for vegetables has shown the common detection of 17β-estradiol, bisphenol A and estrone.

The highest concentration of CECs detected reached 1100 μg L-1 for clindamycin in Costa Rica.Many survey studies have also been reported for countries in Europe, such as the Sava River in Slovenian and Croatian,rivers receiving TWW in Ireland,and impacted rivers and lakes inSweden.An EU-wide survey of CECs in European river waters indicated that benzotriazole, caffeine, carbamazepine, tolyltriazole, and nonylphenoxy acetic acid were among the most frequently detected and/or at the highest concentrations.In addition, levels of CECs in some developing countries may be higher than those in developed countries, likely due to less rigorous treatment at WWTPs. For example, naproxen as high as 140 μg L-1 was reported in a study originated in India and up to 167 μg L-1 for lamivudine in Kenya.Research focusing on the occurrence of CECs in sediment is less prevalent and typically involves fewer CECs. Furthermore, the concentration of CECs in sediments usually exhibits less seasonal variations, which suggests that sediment samples may serve as a more stable marker for CEC monitoring in aquatic environments.High detection frequencies and concentrations of estrone and 17β-estradiol have been reported in sediments from the mouth of the Manokin River in the U.S., with the highest concentration at 58.4 μg kg-1 and 11.5 μg kg-1 , respectively.In the Southern California Bight, triclosan, 4-nonylphenol and bis have been detected in all sediments at median concentrations of 5.1 , 30 , and 121 μg kg-1 , respectively.The highest total concentration of antibiotics in sediments from the intertidal zones of the Yellow River Delta, China was measured to be 178.77 μg kg-1 . 124 Studies conducted in African countries such as Morocco showed even greater CEC concentrations in the sediment . Interestingly, several surveys have shown the presence of some hydrophilic CECs in sediments, such as acetaminophen and caffeine, which were previously thought to have limited sorption to solids due to their low hydrophobicity.Aquatic organisms living in impacted aquatic environments have been sampled for detection of CECs. CECs have been found in the tissues of fish, mussels and oysters collected from the impacted water systems in the U.S., with the maximum concentration detected at 3000 ng g-1 for 4-nonylphenol in mussels, suggesting potential bioaccumulation of CECs in aquatic organisms.

Diazepam was detected in all collected flatfish liver samples in Southern California but was infrequently detected in sediments, highlighting the bio-magnification potential of certain CECs.The accumulation of PBDEs in fish livers was comparable to that of legacy organochlorines.Water snakes and small common carps living in an e-waste contaminated water pond were reported to accumulate plasticizers and organophosphorus flame retardants in their tissues.Therefore, aquatic organisms may be exposed to low levels of CEC mixtures in the environment, and bio-accumulation in aquatic organisms is possible for some CECs. As CECs are continuously introduced into aquatic environments via various pathways, they may be considered as pseudo-persistent contaminants, causing long-term, mixed, low-dose exposure to aquatic organisms. A comprehensive review of the ecotoxicity of human pharmaceuticals concluded that for all human medicines tested, acute effects to aquatic organisms were unlikely, unless spill incidents occurred, due to their trace level occurrence in aquatic environment.The chronic lowest observed effect concentrations of most tested pharmaceuticals in standard laboratory organisms are about two orders of magnitude higher than what detected in the effluents.However, recent studies showed that some CECs may exert unintended adverse effects to organisms, such as endocrine disruption and developmental toxicity, at environmentally relevant levels.The investigation on toxic effects of CECs on aquatic organisms includes two main types of exposure: direct exposure to the real environment, such as TWW and impacted water, and exposure to water spiked with CECs under controlled conditions. Chronic effects, including sublethal effects, have been often observed in aquatic organisms exposed to affected water bodies or TWW. For example, fathead minnows and freshwater mussels were caged for 4 weeks upstream and downstream of the discharge from WWTPs, and were found to develop multiple biomarker responses, such as oxidative stress, enzyme induction, shifts in gene expression and alteration of immune functions.

The growth and yield of green algae and reproduction of daphnia were inhibited by TWW and exhibited dose-response effects. Juvenile rainbow trout exposed to TWW showed significantly different plasma cortisol and glucose response to the secondary stressor.However, it is often difficult to interpret the impact of CECs in this type of experiments, as various other stressors, such as water temperature and bacteria in the real environment, may also induce such biomarker responses.The low concentrations of CECs in TWW also could not explain the sublethal effects observed on algae and daphnia.In addition, interactions of compounds in CEC mixtures should be further considered. The exposure of aquatic organisms to artificially spiked CECs, on the other hand, provides comparable toxicological data under controlled conditions. Most research has been devoted to the toxic effects of CECs at the individual level, while in realistic situations, CECs are always present as a mixture. Exposure to CECs at environmental relevant levels cause multiple adverse effects. For example, marine mussels exposed to atorvastatin at around 1.2 μg L-1 exhibited key fatty acid metabolism disruption and suppression of xenobiotics efflux through P-glycoprotein and membrane diffusion.Gemfibrozil was shown to reduce plasma androgens in goldfish after exposure to 1.5 μg L -1 for 4 and 14 days;while the concentration of gemfibrozil in WWTP effluent was found to be in the range of 10-3830 ng L-1 . The adverse effects of CECs have also been shown at the population level. For example, a 7-year, whole lake experiment conducted in northwestern Ontario, Canada, showed that chronic exposure of fathead minnow to 5-6 ng L-1 of the synthetic estrogen, 17 α- ethynylestradiol,vertical farming technology led to the near extinction of this species.Aquatic invertebrates have been widely adopted to derive acute toxicity end-points, e.g., LC50 values, for target CECs. The acute toxicity of CECs varied greatly, even for compounds belonging to the same chemical class and displayed species-specific effects. For example, the EC50 and LC50 values varied largely among the 12 tested polychlorinated diphenyl ethers for S. obliquus, D. magna, and D. rerio, respectively.Exposure to 17α-ethinylestradiol, acetylsalicylic acid, and bisphenol A significantly affected the embryonic development of sea urchins, with different LC50 values for Mysidopsis juniae and Artemia sp. The concentrations that induced 50% growth inhibition in algae of metolachlor, erythromycin, and triclosan also showed multiple-fold differences between freshwater and marine algae, reflecting the species-specific sensitivity.Mixed exposure of silver nanoparticles, polystyrene nanoplastics and 5-fluorouracil displayed interaction toxicity to marine mussels, with exponentially increased oxidative damage compared to individual contaminants,highlighting the importance to consider chemical interactions when investigating the toxic effects of CECs in the real environment. Some government agencies in the U.S., such as EPA and California State Water Resources Control Board, have tried to put some regulations to control CECs in aquatic environments. For example, the Science Advisory Panel for CECs in California’s aquatic ecosystems has developed strategies to identify the monitoring trigger levels of CECs in aquatic environments based on their lowest effect values available from established databases, such as the Computational Toxicology database and the NORMAN database , for aquatic organisms.However, for the TPs of CECs, such data are usually not experimentally available. Due to the huge and continually increasing number of CECs in aquatic environments, it is unrealistic to examine the toxicity effects of all CECs. Several studies have attempted to develop a prioritization process to select CECs that require the most attention for aquatic organisms based on their monitoring data, production volume, persistence and prevalence in the environment, bio-accumulation potential, and biological effects.

Several modeling tools, like machine learning and quantitative structure-activity relationships , have also been developed to predict the bio-accumulation, bio-transformation and toxicological effects of CECs.For example, Sequence Alignment to Predict Across Species Susceptibility was adopted by the Science Advisory Panel for CECs in California’s aquatic ecosystems to predict the behaviors of CECs across species without available toxicological data from the existing database.The incorporation of such tools is of vital importance to improve risk assessment of CECs due to the limited experimental resources. Studies have often revealed that TPs of CECs occur simultaneously in the tissues of aquatic organisms with their parent compounds, sometimes at even higher concentrations. For instance, metabolites of organophosphorus flame retardants were found in the same order of magnitude as their parent compounds in water snake and small common carps collected from an e-waste-affected site. Norsertraline, the demethylated TP of sertraline, was found to be bio-accumulated at a greater degree than sertraline in the liver of rudd collected from the TWW-impacted Niagara River.Nordiazepam, the demethylated TP of diazepam, was also frequently detected in aquatic organisms along with diazepam.127 Therefore, TPs of CECs in aquatic organisms could originate from two sources – uptake from the ambient environment, and transformation taking place in the organism upon the uptake of the parent compound. Research focusing on the biotransformation of CECs in aquatic organisms, including aquatic plants such as algae, invertebrates such as daphnia, and vertebrates such as fish, is limited. However, the identification of CEC metabolites in aquatic organisms is crucial for evaluating the ecological risks of CECs. Prevalent phase I and phase II enzyme activities were frequently induced in aquatic organisms after CEC exposure, such as cytochrome P450 enzymes and glutathione transferases.Although some common metabolites might be expected, the pattern of CEC metabolism could also vary between different species. For example, three phase I metabolites and 10 phase II metabolites were identified in marine mussels exposed to diclofenac,while 7 phase I metabolites and 3 phase II metabolites were found in H. Azteca and G. pulex. Significant differences in biotransformation rates were observed for different species or between opposite sexes of fishes exposed to CECs.157 Certain aquatic species, such as glass eels, displayed low metabolic activity, with few metabolites detected after CEC exposure,155 while the absence of bio-magnification effects of PFRs in water snakes was attributed to the active bio-transformation. Bio-transformation of CECs in algae shared some similar pathways as that in terrestrial plants, as in the case of hydroxylation, demethylation and glycosylation of bisphenols.Methylation and demethylation are common transformation pathways for chemicals in the environment, especially for compounds with -OCH3, -NCH3-, -SCH3, and/or the corresponding -OH, -NH-, and -SH groups in their chemical structures.

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Flag leaves were harvested from plants four days after panicle emergence

To use the SPQV, all QTL with a sufficiently high logarithm of the odds score should be assessed. The function provides the higher and lower confidence limits, as well as the combined CI for each mathematically related set of QTL. If any QTL within a set exceeds the combined CI for that set, then the whole set can be deemed successful. QTL with observed gene counts that exceed the upper confidence limits should of course be considered to have attained significance. If at least one QTL in an experiment is determined to be significant by the SPQV, the mapping experiment on a whole can be deemed a success. The QTL that contain lower numbers of genes might be identifying previously unknown genes, and therefore their non-significance does not detract from the significance of other QTL. Because of the potential for the identification of new phenotype associated genes in QTL mapping, QTL containing significantly low numbers of identified genes might also be of interest, as they may have been placed on ’empty’ regions due to previously unidentified genes that have a large impact on the phenotype. The QTL containing significantly low numbers of genes should only be considered interesting when at least one other QTL in the same mapping experiment has been proven to be significant so as to avoid the suggestion that all QTL in a faulty mapping study were significant. The intake, transportation, and storage of elements within a plant is vital to its appropriate growth and development. Plants must maintain balance between excessive uptake, which can cause toxicity and necrosis through the production of free radicals or the exclusion of other nutrients, and sufficient uptake of essential inorganic nutrients . Tight control of ion homeostasis allows plants to respond appropriately to environmental conditions such as temperature, soil pH, and water availability . This regulatory control must be responsive to both the concentration of ions within the soil and to those concentrations within the plant. Understanding the genetic control of elemental uptake and transportation will contribute to efforts to improve crops,vertical farming tower for sale and to the ultimate sustenance of the growing global population.

Examining the concentration of elements within a plant sample allows for exploration of the genetic and physiological processes involved in adaptation to a particular environment. To this end, we and others have developed a pipeline that is designed to cheaply and efficiently measure the concentrations of 20 different elements via inductively coupled mass spectrometry . This process is known as ionomics, which is defined as the quantitative study of the mineral nutrient and trace elemental content of an organism; that is to say, its ionome . In cereal crops such as Setaria italica, the flag leaf emerges just before the panicle, and therefore marks the specific developmental time point at which the plant has taken up the majority of its total mass. This tissue is therefore ideal for ionomic inquiry. Additionally, nutrient loading of the grain is accomplished through the remobilization of organic and inorganic materials from the leaves . The flag leaf specifically is instrumental in the loading of photo assimilates and other micro-nutrients , and is therefore commonly used as representative tissue to assess the composition of a grass . The species Setaria viridis, or green foxtail millet, is a member of the Panicoideae that utilizes C4 photosynthesis . It is therefore a good model system for several related, economically important crops, including sorghum and maize. The compact stature, short life span, and sequenced genome of S. viridis have also contributed to its status as an emerging model organism . In addition, S. viridis is the wild ancestor of the crop species Setaria italica ; these two species have a semi-permeable boundary between them, as their primary difference is phenotypic and they are still readily crossed . Foxtail millet is a member of the small millet species, a group of ancient grains that are relatively nutritionally dense when compared to rice and wheat and which are often cultivated as subsistence crops . Because of the combination of the high nutritional value of the S. italica grain and its resistance to abiotic and biotic stressors, breeding elite cultivars of S. italica for increased nutritional content is an attractive prospect. Understanding the contribution that different regions of the S. italica genome make to total nutrient content is an important first step for breeding purposes.

Structured populations are useful tools for dissecting the relationship between elemental accumulation and the genetic content of a species. Recombinant inbred lines have various advantages when it comes to quantitative trait loci analyses. Repetitive selfing allows for the break up of large linkage blocks, which in turn allows for finer mapping . Additionally, once established, RILs may be continuously maintained in a fixed homozygous state. This makes it possible to assay the same combinations of alleles in multiple different environments . The resulting phenotypic and genotypic data can then be compared through various statistical means in order to identify QTL. Here, we use elemental profiling of a RIL population resulting from a wide cross between S. italica and S. viridis grown in multiple environmental conditions to identify QTL associated with the ionic content of leaf material. Overall, we identified 251 QTL, 171 of which were associated with a single element and 80 of which were associated with a principal components analysis of the ionome. The use of traits defined by the differences between treatments in an experiment allowed for the quantification of the influence of the environment on Setaria’s ionome. Experiments were conducted in the summers of 2013 and 2014. Experiments assaying the effect of density of planting on ionic content were conducted in 2013 and 2014. A single drought experiment was conducted in 2014. A total of 189 F7 RILs resulting from a wide cross between the B100 cultivar of S. italica and the A10 line of S. viridis, together with their parent lines, were used as the study material . In every experiment, lines were planted in triplicate in a block design in the field in Creve Coeur, MO. Treatments in the density experiment consisted of either five centimeter spacing between neighboring plants, or twenty centimeter spacing between neighbors. Plants in the drought experiment were either well watered until the time of sample collection, or were subjected to drought stress from eight weeks post planting. The data used for this work included measurements for 20 different elements in flag leaf tissue collected from a recombinant inbred line population resulting from the cross of the B100 cultivar of Setaria italica and the A10 line of Setaria viridis. A drought experiment was conducted in Creve Coeur, Missouri in 2013 ; both a drought and a density experiment were conducted in the same location 2014 . 179 of the RILs were planted in at least two of the three experiments, while 116 were grown in all three.

The leaf samples from all experiments were treated in an identical fashion; samples were dried and stored in temperature and humidity controlled rooms before ionomic analysis. Each sample was profiled for the quantity of 20 elements using ICP–MS . The resultant measurements were normalized to the sample weight and technical sources of variation using a linear model. Experiment level analytical outliers were removed as in Davies and Gather 1993. Pursuant to this, the measured values for each element were transformed to normality using the Box Cox family of transformations, and Studentized deleted residuals were used to identify and eliminate further outliers within the measurements for each element. After outlier removal,hydroponic vertical farm phenotypes were derived by averaging the values for each line within an experiment and treatment. Both environment and genotype impacted the variation present in these data. Repeatability was generally lower than within experiment heritability , indicating that there was less variation in genotypic replicates within an individual experiment than across experiments. The broad sense heritability of 9 elements in the DN13 experiment, 14 elements in the DN14 experiment and 16 elements in the DR14 experiment exceeded 0.4. Certain elements including selenium, sulfur, and boron showed low repeatability; this is likely due to the fact that these elements tend towards analytical artifacts, as they accumulate to levels that are near the limits of detection of the methods described in this paper. The heritability of individual elements varied by up to 0.533 between different experiments. The function stepwiseqtl from the R package was used in order to identify a multiple QTL model for each of the elemental phenotypes. This function moves iteratively through the genome to test for significant allelic effects of each marker on the phenotype in question. When a significant locus has been identified, this is added to the model. A combination of forward and backward regression ultimately produces a genome wide QTL model for each trait. Each element was considered individually, as well as in combination with the others as a contributor to a principal components analysis that was run for each experiment . Five different metrics were used as the phenotype for each element in each experiment. These phenotypes include the ‘raw’ values for each treatment and the differential values for that trait .

The significance of a QTL was computed using the 95th percentile threshold resulting from 1000 iterations of the scanone function as a penalty for adding the QTL to the model. When all experiments are considered, a total of 251 QTL were identified . As expected from the heritability measurements, the majority of these were identified in the 2014 drought experiment . The 2013 and 2014 density experiments allowed for the identification of 75 and 71 QTL, respectively. Approximately a third of the QTL were identified within treatments; the remainder were identified using either the difference, relative difference, or ratio of the phenotypic values measured within the different treatments in a single experiment . Of the 251 QTL, 80 were identified for the mapping based on the principal components analysis; 39 of these resulted from the drought experiment, 21 from the 2013 density experiment, and the remaining 20 from the 2014 density experiment. Of the 251 QTL, 55 were located on chromosome 2 . The locations of the QTL were assessed for overlap with the locations of known ionic genes . Forty five of the QTL contained at least one gene within their 95% confidence intervals. Of the QTL that coincided with genes, 35 were identified using PC as the mapped trait. The QTL were assessed for significance using the Scanning Probabilistic QTL Validator. The QTL were divided into mathematically related sets based on experiment and the phenotypic metric that was used when they were mapped. The results of this assessment are reported in Table 3-3; each phenotypic metric that was used for mapping was associated with at least one set of QTL that identified a significant number of genes, indicating that the data curation was done effectively. There were several regions in which QTL were remarkably concentrated. Fifteen QTL were identified on chromosome 2 between 89.4 and 95.9 cM ; these QTL were discovered in both the 2013 density experiment and the 2014 drought experiment. The traits associated with these QTL included As, Al, Co, Cu, Mo, P, Rb, and Sr, as well as PC2 for the 2013 density experiment. In the context of the ionome, principal components analysis allows for the identification of regions of the genome that would not otherwise be found. While some of the PC QTL identified regions that overlapped with those identified by ion specific QTL, the majority of them, including many QTL identified for PCs which explained a large amount of the variance present in the data, did not. It is possible that for a single ion, the signal associated with the PC QTL regions is not sufficient for their identification, while the additive signal that is inherent in a principal component suffices. Moreover, many of the first few principal components overlay regions associated with water use efficiency , with concentrated regions of QTL identified on chromosomes 2, 5, 7, and 9 at positions 94, 111.9, 99.9, and 123.7, respectively.

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An additional experiment was carried out in an airtight container without aeration

Briefly, 400 mg sub-samples of the dried, ground plant matter were freeze-dried for 12 h, weighed, and extracted in polypropylene tubes by sequential sonication and centrifugation with 20 mL methyl tert-butyl ether and then again with 20 mL acetonitrile. The combined extracts were evaporated under nitrogen to less than 1 mL, and mixed with 5 mL methanol and 20 mL water. A 6 mL aliquot of the extract was taken for analysis by LSC to determine the fraction of activity as extractable residue. Selected 150 mg sub-samples of the solventextracted plant matter were combusted on the Biological Oxidizer as described above to determine the fraction of 14C present as non-extractable residue. When nutrient solution and jars were exchanged, the volume of remaining nutrient solution in each jar was gravimetrically determined. A 9 mL aliquot of the solution was mixed with 13 mL Ultima Gold scintillation cocktail and the 14C was quantified by LSC. Water loss from evaporation during each 3 d period was found to be negligible in the no-plant control containers. It is likely that microbial activity in the nutrient solution may have resulted in transformation of the spiked 14C-compounds and that plants may have accumulated both parent PPCP/EDCs and transformation products. To discern the contribution of transformation products to plant accumulation, the used nutrient solution from day 21 was preserved with 2 g sodium azide and 100 mg ascorbic acid, extracted, and fractionated using high performance liquid chromatography . Solutions from 14C-BPA, DCL, or NPX treatments were first filtered through a What man #4 filter paper and then passed through a HLB solid phase extraction cartridge . Before use, the cartridges were sequentially conditioned with 5 mL each of MTBE, methanol , and water. The filtered solution was drawn through the conditioned HLB cartridges under vacuum and followed by 50 mL deionized water. A sub-sample of the filtrate that passed through the cartridge was collected for analysis by LSC to quantify 14C that was not retained by the cartridge.

The cartridges were dried with nitrogen gas,hydroponic bucket and then sequentially eluted with 5 mL of MeOH:MTBE and 5 mL MeOH. The collected eluent was dried under nitrogen to 100 μL. The concentrated eluent was transferred to an HPLC vial equipped with a 250 μL insert. The condensing vial was rinsed with 130 μL of methanol, and the rinsate and 20 μL of non-labeled parent standard were added to the HPLC vial. Preliminary experiments showed that the recovery of this extraction procedure from the initial solution to HPLC analysis was 81.5 ± 7.1% for BPA, 85.8 ± 2.5% for DCL, and 74.0 ± 1.9% for NPX. Nutrient solutions from the 14C-NP treatment were extracted by a simple liquid-liquid extraction method. Each nutrient solution sample was shaken with 50 mL hexane for 30 min, and then the upper layer of the sample was transferred to a centrifuge tube and centrifuged at 3500 rpm for 30 min to reduce emulsification. The hexane phase was transferred to a 15 mL glass tube, concentrated under nitrogen to 300 μL, and transferred to an HPLC vial. The condensing vial was rinsed with 180 μL of methanol, and the rinsate and 20 μL of non-labeled NP standard were added to the HPLC vial. The recovery of this extraction method from the initial solution to HPLC analysis for NP was determined to be 66.8 ± 12.0%. Young plants of lettuce and collards were grown for 21 d in nutrient solution containing one of the four 14C-labeled PPCP/EDCs. No significant differences in plant mass were observed between treatments at the end of the experiment. During the experiment, three plants died . Figure 2 shows the mean mass balance for the systems at the end of the experiment, depicting the fractions of the spiked 14C present in plant tissues, in the used nutrient solution, and as unaccounted activity. The unaccounted activity reflected the 14C that was not found in the nutrient solution at the time of solution renewal or in the plant tissues after harvest and may include losses via unidentified processes, such as volatilization, microbial mineralization in the nutrient solution , or stomatal release. Activity in each fraction varied across compounds and to a lesser degree across plant species, suggesting specificity to uptake. Figure 3 shows the cumulative 14C dissipation from the nutrient solution as calculated from the difference in activity in the solution at the beginning and end of each 3 d interval of solution renewal, representing 14C loss from plant uptake and other processes. Dissipation followed the decreasing order of BPA > NP > DCL > NPX for all treatments and occurred at a similar rate throughout the 21 d cultivation. The presence of plants significantly increased the dissipation of PPCP/EDCs from the nutrient solution, except for NP.

For example, the initial concentration of 14C-DCL in the nutrient solution was 105.3 ± 0.3 dpm/ mL, but it decreased to only 32.8 ± 1.9 dpm/mL after 3 d in the presence of lettuce, while 91.2 ± 3.2 dpm/mL remained in the no-plant control. Lettuce and collards treatments had different levels of chemical dissipation in the nutrient solution. For example, the overall dissipation of BPA in the lettuce treatment was 69.1 ± 8.7%, as compared to 88.4 ± 5.3% in the collards treatment . Different compounds also dissipated at different rates. For instance, in the presence of collards, the cumulative loss was 88.4 ± 5% for BPA, 55.6 ± 11.8% for DCL, and 45.5 ± 4.3% for NPX.The dissipation of NP in the solutions with plants was found to be similar to that in the noplant control, especially for the lettuce treatment . The loss of NP from the noplant control was likely associated with volatilization, as continuous aeration was used to maintain the oxygen level in the nutrient solution throughout the experiment. The Henry’s Law constant for NP is 1.09 ×10−4 atm m3 mol−1 , suggesting a tendency for volatilization. The loss of NP in the solution was found to be insignificant, as all of the spiked 14C was found in the solution , and a solvent rinse of the system showed little sorption of 14C-NP on the container wall . Doucette et al. found that in a hydroponic set up, about 13% of the spiked NP was lost to volatilization in the absence of plants. The increased volatilization losses in the current study were likely due to specific aeration and temperature conditions used. Despite volatilization losses, significant amounts of 14C were detected in plant tissues, suggesting that both collards and lettuce accumulated NP . Noureddin et al. studied the uptake of 5 mg/L BPA from hydroponic solution by water convolvulus and found that approximately 75% of the spiked BPA was removed after 3 d. This removal was comparable to that observed for BPA with lettuce in this study, but was smaller than that with collards . Calderón-Preciado et al. evaluated hydroponic uptake of triclosan, hydrocinnamic acid, tonalide, ibuprofen, naproxen, and clofibric acid by lettuce and spath and showed that the removal of NPX from solution was about 70% for lettuce and 10% for spath after 3 d. In comparison, Matamoros et al. observed less than 10% removal of NPX after 3 d of hydroponic growth with wetland plants , while 46% removal of NPX was measured in the collards treatment in the present study.

Matamoros et al. also showed that DCL did not dissipate appreciably in treatments with wetland plants, which was in contrast to the high removal of DCL by leafy vegetables observed in this study . It is likely that the smaller plant mass and the use of non-aerated nutrient solution in the earlier study contributed to the limited plant uptake. The range of variation suggests that plant species,stackable planters along with other factors such as plant mass and environmental conditions, affect the actual accumulation of PPCP/ EDCs into plant tissues. Plant tissues were collected after 21 d of cultivation, rinsed with deionized water, and separated into roots, stems, new leaves, and original leaves for analysis of both extractable and non-extractable 14C. Table 1 shows concentrations of 14C in plant tissues, expressed as parent-equivalents. In agreement with the dissipation trends in solution, plant accumulation followed the decreasing order of BPA > NP > DCL > NPX. Concentrations based on dry plant mass ranged from 0.22 ± 0.03 to 12.12 ± 1.91 ng/g in leaves and stems. Statistical analysis showed that the accumulation in leaves and stems was not significantly different between lettuce and collards, or among the different compounds. In contrast, roots accumulated significantly more 14C than all the other plant tissues, with concentrations that ranged from 71.08 ± 12.12 to 926.89 ±212.89 ng/g. Accumulation of 14C in plant tissues exhibited several apparent trends. In whole collards plants, significantly greater accumulation was found for the neutral compounds BPA and NP than the anionic compounds DCL and NPX , suggesting that the charge state of PPCP/EDCs may greatly influence plant uptake . Similar effects have been frequently observed for anionic herbicides, and are attributed to exclusion of negatively charged molecules by cell membranes . Between lettuce and collards, lettuce significantly accumulated less PPCP/EDC when all test compounds were pooled , although the interaction effect for individual compounds was not significant . Accumulation of BPA or NP in plant roots was significantly higher for collards than lettuce , while portion of DCL accumulated into lettuce and collards roots was not significantly different. Analysis of tissue extracted with solvent showed that essentially all of the 14C was non-extractable; only the root samples from NP-collards treatment contained a detectable fraction of 14C in extracts .

Combustion of extracted plant tissues confirmed that almost all 14C remained as non-extractable residue, one possible endpoint for xenobiotics taken up by plants . Only a few studies have examined the plant uptake of some of the same PPCP/EDCs considered in this study. Wu et al. grew iceberg lettuce and spinach for 21 d in hydroponic solution initially spiked with a suite of 19 PPCPs, including DCL and NPX, each at 500 ng/L and found no detectable residues of DCL or NPX, except for NPX in spinach at 0.04 ng/g. Calderón-Preciado et al. analyzed apple tree leaves and alfalfa from fields irrigated with water containing BPA, DCL, and NPX. DCL was detected at 0.354 ng/g in apple leaves and 0.198 ng/g in alfalfa; NPX was detected at 0.043 ng/g and 0.04 ng/g, respectively. The low concentrations found in these studies generally agree with the findings of this study, but there is some variation in the tendency for specific compounds to accumulate. This variation may be partly attributed to the different analytical approaches. In other studies, uptake of PPCP/EDCs by plants was evaluated using non-labeled compounds, and accumulation was measured by targeted chromatographic analysis for the extractable parent compound. The use of 14C-labeled compounds in the current study should have provided “worst-case” estimates of human exposure, as the concentrations included non-extractable residue and likely also included transformation products. Transformation products may be an important component of potential risk since the metabolites of some PPCP/EDCs have higher biological activity than their parents and studies have shown that a large portion of PPCP/EDCs that are taken up by plants may be transformed in vivo . A translocation factor , which was the total 14C in stems, new leaves, and original leaves divided by the 14C in roots, was calculated . The derived TFs were consistently very small, demonstrating the poor translocation of these PPCP/EDCs from roots to upper tissues after uptake. The TF values followed the decreasing order of NPX > DCL > NP > BPA, the opposite observed for plant accumulation. Lettuce displayed lower TFs than collards for the same PPCP/EDCs. For example, the mean TF for BPA was only 0.010 ± 0.003 for lettuce, but was 0.051 ± 0.008 for collards. The much greater accumulation of PPCP/EDCs in roots, as compared to leaves, has been observed in previous studies. For instance, Herklotz et al. found that the levels in leaves were 0.00952 – 0.00503 of those in roots for cabbage grown in nutrient solution spiked with carbamazepine, salbutamol, sulfamethoxazole, and trimethoprim. Doucette et al. reported that the accumulation of NP in leaves was 0.0233 – 0.0167 of that in the roots of crested wheatgrass grown in solution.

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A two compartment model for SA dynamics on the human skin was developed and fitted to data

Some parameters are identifiable to a reasonable degree through model fitting, but there is a large degree of uncertainty in the viral transport efficiencies and the AD kinetic parameters. While this could be a consequence of fitting a limited number of data points with several parameters, the viral load at harvest and risk estimates were well constrained. This large variation in parameters and ‘usefully tight quantitative predictions’ is termed the sloppiness of parameter sensitivities, and has been observed in physics and systems biology. Well-designed experiments may simultaneously reduce uncertainty in the parameters as well as predictions and therefore increasing confidence in predictions. One possible experiment to reduce parameter uncertainty is recording the transpiration and growth rate to fit eq. independently to get at and bt . An interesting outcome of my analysis is the strong association of risk with plant growth conditions. The health risks from consuming lettuce irrigated with recycled wastewater are highest in hydroponic grown lettuce, followed by soil grown lettuce under Sc2 and the least in soil grown lettuce under Sc1 . This difference in risk estimates stems to a large degree from the difference in AD kinetic constants . Increasing katt,s will decrease risk as more viruses will get attached to the growth medium, while increasing kdet,s will have the opposite effect , as more detached viruses are available for uptake by the plant. The combined effect of the AD parameters depends on their magnitudes and is portrayed in Fig. A.4. This result indicates that a better understanding of the virus interaction with the growth environment can lead to an improved understanding of risk. More importantly,livestock fodder system this outcome indicates that soil plays a vital role in the removal of viruses from irrigation water through the adsorption of viral particles. An investigation focused on understanding the influence of soil composition on viral attachment will help refine the transport model.

The risk predicted by this dynamic transport model is higher than the EPA annual infection risk as well as the WHO annual disease burden benchmark. The reasons for this outcome are many-fold. First, there is a significant variability in the reported internalization of viruses in plants. In research of data for modeling NoV transport in plant, I filtered the existing data using the following criteria: 1) human NoV used as the seed agent, 2) presence of quantitative viral results in the growth medium and different locations of the plant. Based on these criteria, the data from represent the best available data on viral internalization and transport in lettuce. However, it is also important to note that a similar study by did not observe human NoV internalization in lettuce. This discrepancy could be due to the specific subspecies of the plant and growth conditions used in the studies. Besides, minor changes such as damages in roots or decrease in humidity of the growing environment can promote pathogen internalization. Alternatively, tracking viral transport through the growth medium and the plant is challenging, which may yield false results due to reaction inhibitions in genome amplification and inferior detection limit. The risk outcome of this study is conservative because it assumes an individual consumes the wastewater irrigated lettuce daily for an entire year. This assumption and the corresponding higher risk estimates are only applicable to a small portion of consumers, while most consumers in the U.S. are likely to have a more diverse diet. While the model outcomes presented here represent the best attempt given the available data, it is also possible that the internalization observed by is an extreme case and and typically internalization occurs to a lesser extent.As previously discussed by others , risk estimates by different NoV dose-response models differed by orders of magnitude. This study primarily aims to introduce a viral transport model without advocating any one dose-response model. The future refinement of pathogen dose-response models will reduce variability in risk estimates.

The risk of consuming lettuce grown in soil as predicted by is higher than my predictions, although I used the results of in both studies. This is a consequence of considering the greater adsorption capability of soil, which is not reflected when assuming a simple input:output ratio. Using different inoculating concentrations of NoV, body weight and consumption rate distributions also contributed to the difference in the outcomes but to a lesser extent. In addition to a transport model predicting the NoV load in lettuce, I explored strategies to reduce the risk of NoV gastroenteritis by increasing holding time of the produce after harvesting or using larger hydroponic culture volumes. Although neither strategy could significantly alleviate the risks, the process highlights two strengths of modeling: 1)It provides analytical support for arguments that would otherwise be less convincing; 2) It predicts outcomes of experiments without the physical resources required to perform them. For instance, the model can be used to explore alternate irrigation schedules to reduce the NoV internalization risk. Modeling also helps encapsulate our understanding of the system and generate hypotheses. For example, simple first-order decay did not produce the trend observed in the water, which suggests that additional mechanisms are at play. I postulated the attachment of virus particles on the walls of the hydroponic system as one possible mechanism and examined the fit of the model. Although viral attachment to glass or other materials has been observed before, here it stands as a hypothesis that can be tested. In addition to generating and testing hypotheses, some of my model assumptions raise broader questions for future research. For example, I assumed that viruses are transported at the transpiration rate from the growth medium to the roots. However, not much is known regarding the role of roots in the internalization of viruses. Investigating the defense mechanisms of plants’ roots to passive viral transport, i.e., through rhizosphere microbiome interactions, may shed light on the broad understanding of plant and microbe interactions. The question of extending this model to other pathogen and plant systems draws attention to the dearth of data in enabling such efforts. While modeling another virus may not require changes to the model, understanding transport in other plants can be challenging.

Data required includes models for growth rate and transpiration, plant growth characteristics including density, water content, as well as internalization studies to determine transport efficiencies. However, from the perspective of risk management, lettuce may be used as the worst-case scenario estimate of risk in water reuse owing to its high consumption with minimal pathogen inactivation by cooking. This worst-case scenario can be used to set water quality standards for irrigation water for the production of fresh produce eaten raw. The models can also be extended to include pathogen transport to the plant tissue from manure/biosolids that are used as organic fertilizer. By assuming that SA transitions from an un-adapted state to an adapted state, the model is grounded in first principles. The stochastic aspect of dose-response emerges naturally from a stochastic simulation of the growth kinetics. In addition, the model predicts carrier outcomes without additional data. Armitage et al. interpret results from several studies to posit that pathogens, including bacteria,hydroponic nft gully show an initial exponential increase in all individuals. We argue that this is not inconsistent with the initial decrease assumption for three reasons. Firstly, the exponential increase is observed in organs like the liver or spleen, and not the whole body or site of inoculation . This does not refute the possibility of an initial decrease at the inoculation site or the whole body. Secondly, the posited decrease is transient, and samples may not have been collected during this window. Thirdly, the magnitude of decrease is low at higher inocula and consequently less detectable. Further, compared to the initial decrease observed when all bacteria are in the S1 state, one would expect 1) no initial decrease if seeding with bacteria all in the S2 state, and 2) a smaller initial decrease if seeding with a mixture of bacteria in the S1 and S2 state. These trends have been observed when pathogens from in-vivo cultures were used for infecting the host . We note that the transition from S1 to S2 is perhaps not instantaneous, and the pathogen population may constitute a continuum of states between S1-S2. When loads were measured in the whole body, a transient decrease was observed in some cases . Clumping of bacteria was offered as a possible explanation, but this does not rule out an actual reduction in viable counts observed in other systems . Armitage et al. also note that non-responders show a subsequent decrease after the initial exponential increase. These were substantiated by measurements from survivors who were killed at later time points. This decrease is probably due to the activation of the adaptive immune response inside the host, which could be incorporated in a within-host variant of the 2C model. Using the concept of IED to evluate reponse, I am able to explain the data with a single IED. It has been observed that the toxic dose of a chemical can vary between individual subjects or with the season.

A similar stochasticity may be expected in IED between individuals which can be attributed to differences in covariates such as body weight, sex, immune history and biological noise. However, assuming this was not necessary to produce an acceptable fit. The model was fit to data by following a two step optimization procedure. Direct multi-objective optimization was not pursued since the objective functions were very different from each other. The deterministic ODE model was easy to evaluate and a global optimization algorithm was employed to guard against local minima while fitting the growth data. Fitting the dose-response data was computationally challenging for 3 reasons: a non-smooth objective function, stochastic simulations have to be repeated many times, and the number of stochastic entities being modeled is not small. Hence, a simple brute-force optimization was adopted. The RH model exhibits a sharp initial decline in SA density and predicts values lower than the observed minimum for each initial load . The 2C model only goes as low as the lowest load observed on the skin. Experiments similar to that of with greater time resolution are necessary to ascertain the time of true minimal SA density. The 2C model stochastic model does not perform as well as the RH model . However, the 2C model fit to dose-response data improves along the Pareto front . It is possible that exploring solutions with a higher growth objective may yield a solution that fits as well as, if not better than, the RH fit to the dose-response data. Moreover, the proposed approach offers advantages over the existing approach in that 1) it is fully mechanistic, and hence is more applicable in other scenarios , and 2) in addition to response outcomes, the proposed approach also accounts for carrier outcomes. Perhaps the most interesting outcome of this study is the incorporation of quorum sensing in dose-response modeling. The rejection of the absence of cooperativity in SA pathogenesis and the adequate fit of cooperativity make a strong case for the cooperativity in action hypothesis. Experimental support for this hypothesis include the well studied Agr system of quorum sensing . In the words of Le. et al, the Agr system “generally enhances pathogenesis by increasing expression of aggressive virulence determinants such as toxins and degradative enzymes”.This system is activated when bacteria reach a certain density, which results in a disease response such as a murine abscess. However, the 2C model posits that quorum sensing enhances bacterial growth rate, for which I propose two possible explanations. The direct explanation is the existence of an as yet undiscovered signaling mechanism responsible for density dependent growth enhancement. A second explanation relates to the events initiating response in a host, which is the interaction of the toxins/enzymes produced by SA with the host tissue. The 2C model captures these dynamics at a higher level of abstraction, with the mathematical variable i representing the amount QS signals and toxins. We can interpret b2 as the rate of enhanced production of these factors.

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Credible and prediction intervals in the shoot at harvest were similar for both models

However, Southern California, a region that suffers from a similar degree of water shortage, currently uses less than ~3% of municipal wastewater in agriculture, while discharging ~1.5 million acre-feet effluent per year into the Pacific Ocean . Secondary municipal wastewater effluent for ocean discharge is often sufficient to support both the nutrient and water needs for food production. Water reuse in agriculture can bring municipal water reclamation effluent to nearby farms within the city limit, thus promoting local agriculture and also reducing the rate of farmland loss to urban development. While the use of reclaimed water in agriculture offers a multitude of societal and agronomical benefits, broader adoption faces great challenges. One of the important challenges is ensuring the safety of food products in light of a plethora of human pathogens that may be present in recycled wastewater. Past studies have identified risks associated with irrigating food with recycled wastewater through the retention of the irrigation water on edible plant surfaces during overhead irrigation . With the emphasis on water conservation and reduction of evapotranspiration, subsurface drip irrigation is gaining popularity . Since there is lesser contact between water and the plant surface, the chance of surface contamination of pathogens is reduced. However, this new practice presents risk of uptake of microbial pathogens into plants. Such internalized pathogens are of greater concerns as washing, even with disinfectants, may not affect pathogens sheltered in the vasculature. Although pathogen transport through root uptake and subsequent internalization into the plant has been a growing research area, results vary due to differences in experimental design, systems tested, and pathogens and crops examined . Among the array of pathogens causing food borne illness that may be carried by treated wastewater, viruses are of the greatest concern but least studied. According to the CDC, 60% of U.S. food borne outbreaks associated with eating leafy greens were caused by noroviruses ,stacking pots while Salmonella and E. coli only accounted for 10% of the outbreaks . Estimates of global food borne illness prevalence associated with NoV surpass all other pathogens considered.

Viruses are also of concern because they persist in secondary wastewater effluents in high concentrations . They do not settle well in sedimentation basins and are also more resistant to degradation than bacteria . Therefore, in the absence of solid scientific understanding of the risks involved, the public are likely less receptive to adopting treated wastewater for agricultural irrigation. NoV internalization in hydroponic systems has been quantified by DiCaprio et al. . Internalization in crops grown in soil is considered lesser but nevertheless occurs. However, the only risk assessment that considered the possibility of NoV internalization in plants assumed a simple ratio of viruses in the feed water over viruses in produce at harvest to account for internalization. The time dependence of viral loads in lettuce was not explored and such an approach did not permit insights into the key factors influencing viral uptake in plants. In this study, we introduce a viral transport model to predict the viral load in crisp head lettuce at harvest given the viral load in the feed water. It is parameterized for both hydroponic and soil systems. We demonstrate its utility by performing a quantitative microbial risk assessment . Strategies to reduce risk enabled by such a model are explored, and a sensitivity analysis highlights possible factors affecting risk.The plant transpiration rate was adopted as the viral transport rate ) based on: 1) previous reports of passive bacterial transport in plants , 2) the significantly smaller size of viruses compared to bacteria, and 3) the lack of known specific interactions between human viruses and plant hosts . Accordingly, viral transport rate in hydroponically grown lettuce was determined from the previously reported transpiration model , in which the transpiration rate is proportional to the lettuce growth rate and is influenced by cultivar specific factors . These cultivar specific factors used in our model were predicted using the hydroponic crisp head lettuce growth experiment carried out by DiCaprio et al. described in Section 2.3 . Since the transpiration rate in soil grown lettuce is significantly higher than that in the hydroponic system, viral transport rate in soil grown lettuce was obtained directly from the graphs published by Gallardo et al. using WebPlotDigitizer . The shoot growth rate for soil grown lettuce was determined using Eq. 9 . In the absence of a published root growth model for lettuce in soil, a fixed root volume of 100 cm3 was used. In the viral transport model, viral transfer efficiency was used to account for the potential “barrier” between each compartment .

The existence of such a “barrier” is evident from field experiments where some microbial pathogens were internalized in the root but not in the shoot of plants . In addition, viral transfer efficiencies also account for differing observations in pathogen internalization due to the type of pathogen or lettuce. For example, DiCaprio et al. reported the internalization of NoV into lettuce, while Urbanucci et al. did not detect any NoV in another type of lettuce grown in feed water seeded with viruses. The values of ηgr and ηrs were determined by fitting the model to experimental data reported by DiCaprio et al. and is detailed in Section 2.3. The viral removal in the growth medium includes both die-off and AD, while only natural die-off was considered in the lettuce root and shoot. AD kinetic constants as well as the growth medium viral decay constant in the hydroponic case were obtained by fitting the model to the data from DiCaprio et al. . Viral AD in soil has been investigated in both lab scale soil columns and field studies . In our model, viral AD constants in soil were obtained from the experiments of Schijven et al. , who investigated MS2 phage kinetics in sandy soil in field experiments. As the MS2 phage was transported with the water in soil, the AD rates changed with the distance from the source of viruses. To capture the range of AD rates, two scenarios of viral behavior in soils were investigated. Scenario 1 used the AD rates estimated at the site closest to the viral source , while scenario 2 used data from the farthest site . In contrast to lab scale soil column studies, field studies provided more realistic viral removal rates . Using surrogate MS2 phage for NoV provided conservative risk estimates since MS2 attached to a lesser extent than NoV in several soil types . The viral decay rate in the soil determined by Roberts et al. was adopted because the experimental temperature and soil type are more relevant to lettuce growing conditions compared to the other decay study . Decay rates in the root and shoot were used from the hydroponic system predictions.The transport model was fitted to log10 viral concentration data from DiCaprio et al. , extracted from graphs therein using WebPlotDigitizer . In these experiments, NoV of a known concentration was spiked in the feed water of hydroponic lettuce and was monitored in the feed water, the root and shoot over time.

While fitting the model, an initial feed volume of 800 mL was adopted and parameters producing final volumes of b200 mL were rejected. To fit the model while accounting for uncertainty in the data, a Bayesian approach was used to maximize the likelihood of the data given the parameters. A posterior distribution of the parameters was obtained by the differential evolution Markov chain  algorithm,strawberry gutter system which can be parallelized and can handle multi-modality of the posteriors distribution without fine tuning the jumping distribution. Computation was carried out on MATLAB R2016a and its ParCompTool running on the High Performance Computing facility at UC Irvine.Table 3 lists the parameters estimated by model fitting and their search bounds. Fitting data from DiCaprio et al. without including viral AD to the tank walls was attempted but the results were not used in the risk estimates due to the poor fit of model to the data. The rationale behind the model fitting procedure and diagnostics are discussed in Supplementary section S1H.A summary of the model fitting exercise for viral transport in hydroponic grown lettuce is presented in Fig. 2. Under the assumption of first order viral decay, NoV loads in water at two time points did not fall in the credible region of model predictions, indicating that mere first order decay was unsuitable to capture the observed viral concentration data. The addition of the AD factor into the model addressed this inadequacy and importantly supported the curvature observed in the experimental data. This result indicates the AD of viruses to hydroponic tank wall is an important factor to include in predicting viral concentration in all three compartments .The adequacy of model fit was also revealed by the credible intervals of the predicted parameters for the model with AD . Four of the predicted parameters: at, bt, kdec, s and kp, were restricted to a smaller subset of the search bounds, indicating that they were identifiable. In contrast, the viral transfer efficiency η and the kinetic parameters spanned the entirety of their search space and were poorly identifiable. However, this does not suggest that each parameter can independently take any value in its range because the joint distributions of the parameters indicate how fixing one parameter influences the likelihood of another parameter . Hence, despite the large range of an individual parameter, the coordination between the parameters constrained the model predictions to produce reliable outcomes . Therefore, the performance of the model with AD was considered adequate for estimating parameters used for risk prediction.Risk estimates for lettuce grown in the hydroponic tank or soil are presented in Fig. 4. Across these systems, the FP model predicted the highest risk while the 1F1 model predicted the lowest risk. For a given risk model, higher risk was predicted in the hydroponic system than in the soil. This is a consequence of the very low detachment rates in soil compared to the attachment rates. Comparison of results from Sc1 and Sc2 of soil grown lettuce indicated lower risks and disease burdens under Sc1 . Comparing with the safety guidelines, the lowest risk predicted in the hydroponic system is higher than the U.S. EPA defined acceptable annual drinking water risk of 10−4 for each risk model. The annual burdens are also above the 10−6 benchmark recommended by the WHO . In the case of soil grown lettuce, neither Sc1 nor Sc2 met the U.S. EPA safety benchmark. Two risk models predicted borderline disease burden according to the WHO benchmark, for soil grown lettuce in Sc1, but under Sc2 the risk still did not meet the safety guideline. Neither increasing holding time of the lettuce to two days after harvesting nor using bigger tanks significantly altered the predicted risk . In comparison, the risk estimates of Sales-Ortells et al. are higher than range of soil grown lettuce outcomes presented here for 2 of 3 models. The SCSA sensitivity indices are presented in Fig. 5. For hydroponically grown lettuce, the top 3 factors influencing daily risk are amount of lettuce consumed, time since last irrigation and the term involving consumption and ρshoot. Also, the risk estimates are robust to the fitted parameters despite low identifiability of some model parameters . For soil grown lettuce, kp appears to be the major influential parameter, followed by the input viral concentration in irrigation water and the lettuce harvest time. Scorr is near zero, suggesting lesser influence of correlation in the input parameters.In this study, we modeled the internalization and transport of NoV from irrigation water to the lettuce using ordinary differential equations to capture the dynamic processes of viral transport in lettuce. This first attempt is aimed at underscoring the importance of the effect of time in determining the final risk outcome. The modeling approach from this study may be customized for other scenarios for the management of water reuse practices and for developing new guidelines for food safety. Moreover, this study identifies critical gaps in the current knowledge of pathogen transport in plants and calls for further lab and field studies to better understand risk of water reuse.

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Carbon captured in soil already exists and is merely captured rather than permanently destroyed

This proposed bill did not pass, but CARB is in the process of constructing a webpage that will provide information on proposed offset programs, and the agency affirmatively notes that it will be considering additional offset programs as a part of future rule making activities.Thus, an agricultural soil carbon sequestration offset program may not be far from being proposed and considered as a possible offset program under California’s cap and trade program.Ensuring that emission reductions are quantifiable, permanent, and additional are important considerations for any type of offset program but are particularly difficult in relation to agricultural soil carbon sequestration offset programs. Quantifiability and permanency are especially central concerns about offset programs, and it has been suggested that agricultural soil carbon sequestration plays such a minimal role in major carbon markets because soil carbon is considered difficult to measure, verify, and track.Deciding how to allocate offset credits can be challenging in any carbon sequestration program because it is difficult to accurately quantify how much carbon has really been sequestered.Soil carbon sequestration depends on a complicated living system that is constantly changing and not easy to directly quantify. A variety of factors determine how much carbon a unit of soil can sequester, including seasonal variations, weather, precipitation, plant species present, and the variation in soil type and quality.This problem does not arise in offset programs that decrease emissions from point sources, such as smokestacks or manure lagoons,rolling benches for greenhouse where measurement is more concentrated and accurate methods of measurement are established and verifiable. For example, a manure lagoon equipped with a BCS can use a meter to determine methane emissions from the entire lagoon. Adding to the complication of quantifiability, some studies dispute whether conservation tillage practices actually sequester carbon at all.

The goal of permanency is problematic in agricultural soil carbon sequestration programs because the carbon reduction is easily reversible.When carbon is sequestered in soil, it can be re-released into the atmosphere from a disturbance such as increased intensity of tilling, wind or water erosion, or a natural disaster such as an earthquake, fire, or disease outbreak.One agricultural soil carbon sequestration offset program incorporates a 60% discount into its program to account for the uncertainty of permanency.Compare this to destroying a unit of methane with a BCS or reducing a unit of carbon emissions from a smokestack by installing new technology. When that methane or carbon unit is destroyed or never created, that reduction is not reversible because it never existed.Simple disturbances can cause the loss of some or all of the carbon that was stored in the soil and essentially negate any climate change benefit.Additionality is also at issue with agricultural soil carbon sequestration offset programs because cropland conservation practices such as no till and conservation tillage are already widely used in at least some parts of the United States due to incentives programs set up by the USDA starting in the 1960s and 1970s.The USDA study on cropland conservation practices in the Missouri River Basin indicates that within the 95 million acres of cropland studied between 2003 and 2006, 46% of the cropland met no-till criteria and 97% of the cropland “had evidence of some kind of reduced tillage on at least one crop in rotation.”Considering that cropland conservation practices seem to be common in at least some parts of the country, it may be difficult to tell if any given offset project under an agricultural soil carbon sequestration offset program would have occurred anyway in a business-as-usual scenario for purposes of determining additionality.The checkpoints for offset programs—that the offset credits generated are quantifiable, real, permanent, and additional—do not explicitly include an analysis of the trade offs or incidental effects of an offset program. However, in agricultural soil carbon sequestration offset programs, the considerations of incidental effects caused by the offset program should be a critical checkpoint to consider.

Some of the conservation practices that most effectively sequester carbon in agricultural soil can present trade offs that bring new problems for farmers that must be fixed through alternative means. Primarily, tilling decreases weed growth, so farmers who infrequently or never till typically use more herbicide to keep weeds out of their field.Farmers using the other sequestration practices encouraged under soil carbon sequestration programs besides no-till and conservation tillage are also reported as using much larger quantities of herbicide. For example, the Kenya Agricultural Carbon Project does not address the use of herbicides, and the World Bank reported that herbicides are heavily used on farms that are involved with the project.An environmental and social monitor for a soil carbon sequestration program reported that “the herbicides are applied . . . without due regard to environmental consequences.”At least one assessment reported contrary findings, that less herbicide was used when conservation practices were employed.However, this assessment utilized many types of conservation practices including improved pesticide management practices, which could explain the decrease in herbicide use in this study. Increased herbicide use can be detrimental for reasons including environmental harm, pollutant emissions, and human health. Herbicides can migrate into the surrounding environment through soil, air, and waterways.The resulting chemical residues can negatively affect the natural surroundings, as any chemical might.The effects would depend largely on the toxicity of the chemicals used in the herbicide, the quantity used and leached, and the sensitivity of the surrounding environment. Additionally, harmful air pollutants, including greenhouse gases, are released when using herbicides.Herbicides release a large amount of nitrous oxide, a powerful pollutant with an estimated 298 times the global warming potential of carbon dioxide.This can be seen as similar to the problem of copollutants. Co-pollutants are pollutants that are released simultaneously and from the same source as the greenhouse gas or pollutant at issue. Increasing emissions of the pollutant at issue will often increase co-pollutant emissions, which can be more localized and harmful in smaller quantities. Similarly, increasing herbicide use will increase nitrous oxide emissions that would not have otherwise occurred if not for increased herbicide use. Thus, even if an agricultural soil carbon sequestration offset program is measured to be carbon neutral, it may unintentionally provide an avenue for increased nitrous oxide emissions and harm to the environment. Another incidental effect of increased herbicide use is that more chemicals will be put onto our food products and affect human uses of soil, water, and air.Herbicides have been linked to serious diseases, such as non-Hodgkin’s lymphoma, soft-tissue sarcoma, and Parkinson’s disease.Due to these health risks, some countries have started to mandate that farmers reduce the amount of herbicide used on their crops due to the harmful human health effects of herbicides.

These circumstances have led some to sharply oppose the increased use of herbicide. If CARB considers including an agricultural soil carbon sequestration offset program in AB 32’s repertoire of offset programs,cannabis grow systems the issues of quantifiability, permanency, additionality, and incidental effects of the offset projects should be addressed. Implementing a new agricultural soil carbon sequestration offset protocol under AB 32 without considering and compensating for these issues would jeopardize the purpose of AB 32’s cap and trade program and likely inflate AB 32’s carbon market with credits that do not actually represent the additional sequestration of one ton of carbon dioxide or its equivalent.These issues may be most completely and accurately addressed by using an ecosystem approach to design the offset program and to approve and implement the resulting offset projects in a case-by-case manner. An ecosystem management approach acknowledges the inter connectivity of the parts within an ecosystem and sees the environment as a single functioning landscape.This approach recognizes that considering only a single species, pollutant, or practice can be detrimental when it successfully decreases one harm but incidentally increases another harm that may be just as, if not more, harmful to the ecosystem. Accordingly, any increase in herbicide use, and subsequent nitrous oxide emissions, or any other potentially harmful externality would be accounted for in the offset program. Under an ecosystem approach, offset programs would not favor projects or regulations that induce harms of a larger or more detrimental magnitude than the harm which is to be prevented by the program. The need for an ecosystem approach, and the regulating agency’s response to this need, is illustrated by the Endangered Species Act.Certain species are listed and protected under the Endangered Species Act and a federal budget is allocated to preserving those listed species. However, many believe that the environment and society would be better served by protecting and managing ecosystems on a larger scale as opposed to individual species.Recognizing this and similar needs in different areas under their jurisdiction, the U.S. Fish and Wildlife Service published An Ecosystem Approach to Fish and Wildlife Conservation, which includes guidelines the FWS strives to use in order to incorporate the ecosystem approach into their conservation work. The Pacific Islands Forum Fisheries Agency , a group formed to help its Pacific Island members to manage, control, and develop the fisheries within the Exclusive Economic Zones, encourages its countries to utilize an ecosystem approach to manage their fisheries.The FFA’s Ecosystem Approach to Fisheries Management consists of four steps.The first step is to determine the scope of the assessment by clearly identifying what is to be managed.The second step is to identify all the issues to be assessed within five key areas and to agree on the values that are to be achieved for each issue.The third step is to determine which issues should be directly managed.The last step is to determine acceptable performance levels, what management arrangements will achieve these levels, and the review process for assessing performance.During the creation of the offset program, an ecosystem approach could be utilized to determine whether the program should be created at all. If no agricultural soil carbon sequestration offset project could ever in theory have a net benefit when considering all the greenhouse gas sources and sinks and other externalities created by an individual project, then the analysis under an ecosystem approach may indicate that the offset program should not be approved. If the analysis of the offset program under an ecosystem approach indicates that only certain types of projects could result in a net benefit to the environment, the program could be limited to those particular types of projects. The ecosystem approach could also be used to assist in determining on a case-by-case basis whether an agricultural soil carbon sequestration offset project should be approved under the offset program. Because the variables associated with each agricultural soil carbon sequestration project will be different for each project and have the potential to vary greatly, a case-bycase ecosystem approach for the approval process for each project would help decision makers to properly determine whether the offset project is quantifiable, permanent, real, and additional. Which externalities should be included in an ecosystem approach analysis of agricultural soil carbon sequestration projects would be a basis for much disagreement, and would depend on scientific and policy analysis beyond the scope of this Comment. However, at a minimum, the effects of increased herbicide use on the surrounding ecosystem and the increase of nitrous oxide emissions should be included in the analysis, as those are some of the more egregious oversights in certain existing agricultural soil carbon sequestration programs, as discussed in Part V.B. In addition to legitimizing a future offset program and resulting offset projects in general, applying case-by-case and ecosystem approaches have the potential to resolve specific issues regarding quantifiability, additionality, and incidental effects identified in Part V.At least two main methods of quantifying agricultural soil carbon sequestration for the purposes of allocating credits for agricultural soil carbon sequestration offset projects could be envisioned. One is a simpler standards-based approach and the other follows a case-by-case process. Although a case-by-case approach may be impracticable in practice, this example illustrates why a case-by-case approach would be more appropriate and crucial for an agricultural soil carbon sequestration offset program. The standards-based option is to give an offset credit per a certain acreage of land covered by an offset project.That particular acreage of land would, on average, sequester one metric ton of carbon dioxide or carbon dioxide equivalent regardless of individual features of the land.

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The main reason was a reduction in direct and indirect nitrous oxide emissions

Phase I also considered agricultural adaptation strategies that addressed regional issues such as hydrology, growers’ attitudes toward climate change, and urbanization versus preservation of farmland. These topics are explored in more quantitative ways here.Since 1960, total crop acreage in Yolo County has been declining. Vegetable and orchard crop areas have increased, while field crop acreage has declined . There has been an increase in higher‐revenue‐per‐acre crops, especially a shift out of barley, and a shift into more processing tomatoes, wine grapes, and walnuts. Many factors affect changes in acreage, including changes in market conditions , input supplies, and climate. Among factors affecting acreage decisions, we investigated whether changes in climate have affected acreage allocations across crops. If responses to climate changes in the past continue to hold in the future, we can use hisorical information to learn more about how crop acreages are likely to change in response to the forecasted Yolo County climate changes from 2010 to 2050. We developed econometric models that relate acreages of each major crop to relative prices and key climate variables . The models are applied to the data including 60 years of acreage for major crops and 100 years of local climate history. Our climate history indicates that during the past century, the increase in annual temperature appears to be mainly due to warmer winters rather than to warmer summers . There was a decrease of about 150 winter chill hours in the last 100 years. Using historical reationships between climate and acreage allows investigation of how forecasted climate changes in Yolo County may affect Yolo acreage patterns. Acreage projections use climate projections for the B1 and A2 scenarios from 2010 to 2050 with GCM data from GFDL‐BCCA. Acreage projections hold constant relevant drivers of crop acreage, except for local climate variables. Among field crops,planting gutter warmer winter temperatures were projected to cause wheat acreage to decline and alfalfa acreage to rise . Thus, future decisions to increase alfalfa acreage present an interesting implication for water use: wheat uses little irrigation; whereas, alfalfa is one of the more intense water users.

By 2050, tomato acreage is projected to increase compared to the current level . This is also related to the increase in growing degree days in the winter months. A warmer climate in late winter/early spring has allowed early planting and provided favorable conditions for establishment. The forecasted climate changes have only moderate impacts on projected tree and vine crop acreage, in part because the climate changes that have occurred have not yet affected key variables enough to induce a significant change in the acreage of perennials when market conditions have been favorable. Almond acreage is projected to increase slightly with warmer temperatures in 2035–2050 . Almonds have a relatively low winter chill hour requirement. Walnut acreage, however, would decline slightly ; it has a higher winter chill requirement. This is consistent with the finding that surveyed orchard growers express concern about a decrease in winter chill hours . These projections rely on using historical relationships between acreage change and climate variables change. They are based actual past responses of acreage to climate. However, no attempt is made to forecast the relative prices, technical changes, new markets, or other factors that will also affect acreage. Water supply vulnerabilities for agriculture and other sectors can be mediated through traditional infrastructure improvements or alternative water policies . Local stewardship that is implemented by water managers and agricultural users tends to be more economical and have less environmental impact than developing new supplies. One tool that has helped water resource managers integrate climate change projections into their decision making process is the Water Evaluation and Planning system . WEAP, a modeling platform that enables integrated assessment of a watershed’s climate, hydrology, land use, infrastructure, and water management priorities , is used here for the Yolo County Flood Control and Water Conservation District service area. It covers 41 percent of the county’s irrigated area and is located in the western and central portion of the county .Recognizing the key role that land‐use planning will play in achieving the goals of AB 32, legislators passed Senate Bill 375 in 2008, requiring sustainable land‐use plans that are aligned with AB 32 .

Local governments must address GHG mitigation in the environmental impact report that accompanies any update to their general plan or carry out a specific “climate action plan” . Emissions of GHG from agriculture are often missing from existing inventory tools geared to local planners. The local government of Yolo County was among the first in California to pass a climate action plan . This project contributed to this climate action plan, and developed a set of guidelines to estimate GHG emissions from agriculture within a local inventory framework . The Tier 1 methods used here have been adapted for local activity data largely from three main sources: the California Air Resources Board Technical Support Document for the 1990–2004 California GHG Emissions Inventory ; the U.S. Environmental Protection Agency Emissions Inventory Improvement Program Guidelines ; and the 2006 IPCC Guidelines for National GHG Inventories . In Yolo County, total agricultural emissions declined by 10.4 percent between 1990 and 2008 .Lower fertilizer use was driven by two important land use trends: a 6 percent reduction in the county’s irrigated cropland; and a general shift away from crops that have high N rates coupled with an expansion in alfalfa and grape area, which require less fertilizer . In both years, emissions of CO2, N2O, and methane from diesel‐powered mobile farm equipment were responsible for 20.0 to 23.0 percent of total agricultural emissions in Yolo County between 1990 and 2008 . Fuel consumption per unit area for several important crops offset the small decline in irrigated cropland. Using the Tier 1 method prescribed by ARB, emissions of CH4 from rice cultivation were estimated to increase from 25.9 to 31.2 kilotons carbon dioxide equivalent between 1990 and 2008, entirely due to an expansion in the area under rice cultivation. Studies also suggest that cultivation practices that combine straw incorporation and winter flooding tend to generate more CH4 emissions than burning rice straw . Thus, estimates generated using the DeNitrification‐DeComposition model showed a larger increase in emissions over the study period because the Tier 3 method accounted for changes in residue and water management made in compliance with the state air quality regulations that have phased out rice straw burning, and the increase in cultivated area . 

Many agricultural practices to mitigate GHG emissions offer agricultural co‐benefits. For example, economic factors are prompting local farmers to shift more of their land to crops that happen to require less N fertilizer and diesel fuel, and to adopt practices that reduce these inputs. Growers cite rising cost and market volatility of inputs, rather than mitigation per se, as a more immediate motivation to use fertilizer and fuel more efficiently. In 1990, emissions sources associated with urban areas accounted for approximately 86 percent of the total GHG emissions countywide,gutter berries while unincorporated areas supporting agriculture were responsible for 14 percent . If calculated on an area‐wide basis the county’s urban areas emitted approximately 152.0 tons CO2e per hectare per year . By contrast, this inventory results indicates that in 1990 Yolo County’s irrigated cropland averaged 2.16 t CO2e ha‐1 yr‐1 and that livestock in rangelands emitted only 0.70 t CO2e ha‐1 yr‐1 . This 70‐fold difference in the annual rate of emissions between urbanized land and irrigated cropland suggests that land‐use policies that protect existing farmland from urban development are likely to help stabilize and or reduce future GHG emissions, particularly if they are coupled with “smart growth” policies that prioritize urban infill over expansion .Many factors affect farmers’ perceptions and response to climate change; for example, characteristics of the individual farmer and their farm; social networks and involvement in programs run by local institutions, agricultural organizations, and extension services; and views on government programs and environmental policies. The goal of this sub-project is to: examine Yolo County farmers’ perceptions of climate change and its risks to agriculture; and develop a better understanding of how such factors might influence farmers’ adoption of proposed adaptation and mitigation practices. We conducted semi‐structured interviews with eleven farmers and two agricultural extension workers in the fall of 2010. The sampling strategy recruited respondents from a cross section of farm sizes, local cropping systems, and market orientations. Interviewers followed a set of open‐ended questions to minimize prompting and interviewer bias, and were used to develop a quantitative survey which was mailed to farmers in Yolo County during February and March of 2011. The survey sample was drawn from a list of 572 individuals who have submitted conventional or certified organic pesticide use permits to the Yolo County Agriculture Commissioner’s office. The final response rate was 34.0 percent. Results of the survey indicated that 54.4 percent of farmers agreed to some extent with the statement “the global climate is changing” . A minority indicated that local summer temperatures had decreased over time, while only 5.6 percent observed an increase. While contrary to statewide mean temperatures, this corresponds with local climate records which show little change in maximum summer temperature over the last century . A majority of farmers indicated that rainfall, drought, and flooding had not changed over the course of their career, but a sizable minority reported water availability had decreased and <1 percent said it had increased. In 1976, the newly constructed Indian Valley Reservoir began supplementing the District’s surface water supplies to local growers.

However, a recent drought in 2009 and 2010 reduced water releases in those years to less than 40 percent of the average for the preceding decade . The memory of this recent a drought may therefore occupy a central place in farmers’ perception of water related trends. Respondents with greater concern for drought and less reliable water were more likely to pump groundwater, drill new wells, and adopt drip irrigation . A farmer’s views on climate change affected the inclination to implement voluntary mitigation practices. More specifically, farmers who disagreed with the statement “The global climate is changing” were less likely to adopt mitigation practices than those who agreed with the statement. Likewise, skepticism that human activities are an important cause of climate change meant less inclination to adopt mitigation practices. Farmers who had frequent contact with local agricultural organizations were more likely to implement mitigation strategies Farmers are often more concerned about the future impact of government regulations than they are about the direct impacts of climate change. This ranking of concern is not surprising given the gradual nature of climate change. However, it does underscore the importance of understanding how farmers view environmental regulations and the information needed to influence their likelihood to adopt mitigation and adaptation practices. Strategies to expand the reach of local agricultural organizations and government conservation programs by improving farmer participation in their activities are thus seen as an important way to strengthen adaptation and mitigation efforts.UPlan relies on a number of demographic inputs . Attractors are given a positive value . Discouragements are given negative values . A system of weights is used to rank the attractive or discouraging property of each variable. We modified UPlan to allow development within existing urban areas, on the assumption that a significant urban redevelopment is likely within the 2010–2050 time frame. The A2 scenario loses two times more acreage of high quality soils to urbanization compared to B1 . One of the most striking findings is just how little land is required to house future populations at higher densities. The B1 and AB 32+ scenarios require 44 percent and 7 percent of the urbanized land of the A2 scenario, respectively. Even holding population increase constant at B1 levels, these scenarios use 63 percent and 38 percent of the land of the A2 scenario, most or all of it within existing urban areas, and also greatly reduce GHG emissions from transportation. These results suggest that the most important climate change mitigation policy that Yolo County could adopt would be to restrict urban development to infill locations within existing cities, and to keep existing farmland in agriculture.

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Many ecological processes governing agricultural pest abundance occur over a large spatial scale

Additionally, as the amount of land in cropland increases, opportunities for invasion or refuge from pesticide applications may be reduced, thus leading to a negative effect of landscape simplification on pesticide use. Three recent reviews of empirical, landscape-scale ecological studies evaluating the effect of landscape complexity on insect pests reported similarly equivocal results, with some studies finding reduced pest pressure, pest abundance, or pest diversity, whereas others find no relationship or the opposite relationships . The variability in the literature may reflect the inadequacy of current study designs to disentangle the net effect of landscape simplification on pesticide use. Confounding variables, such as crop type, or endogenously determined variables, such as farm size or income, could give misleading results if not properly controlled for. Alternatively, studies that are small scale or over short time periods may miss important underlying drivers of pest abundance. Pests disperse large distances, both naturally and aided by the movement of people and goods. Agricultural pests are thus likely governed in large part by meta population processes . Within an agricultural landscape pests may go locally extinct from crop patches because of pesticide use or because of stochasticity influencing small populations, only to be recolonized from a persistent meta population existing in the surrounding agricultural matrix or from a new invasion into the system. Natural enemies too may require resources outside of individual crop fields for alternative prey and shelter for overwintering or from disturbances, such as pesticide application or harvest . Furthermore,dutch buckets the periodic disturbance of crop fields may disrupt predator–prey dynamics by reducing natural enemies directly or by temporarily reducing pest populations to the level below which predators can be supported.

As a result of pest and natural enemy dispersal and immigration, the effect of local processes on regional abundances may be small, despite large effects on within-field abundances. Thus, small-scale studies that fail to account for the landscape-scale dynamics of agricultural pests and their natural enemies could result in spurious associations of what promotes or limits pest abundance. For these reasons, landscape-scale studies provide the best insight into the effect of habitat simplification on pests . Beyond meta population dynamics and trophic interactions, invasion and spread of insect pests and natural enemies are partly stochastic processes influenced by yearly environmental conditions and by the timing of insect pest and natural enemy arrival . Thus, temporal scale may be equally as important as spatial scale to disentangle the effects of landscape simplification on pest abundance. For example, a heat wave at the right time of the growing season may result in widespread pest mortality and high crop yields, whereas a heat wave at a different time of the season may stress crops, making them more susceptible to pest outbreak but having little effect on the pests themselves. This variability over time could appear like ambivalent results of landscape simplification when it is instead the result of the interaction between insect pests and weather. If we are to mitigate the effects of pesticide use on both human health and ecological systems, it is necessary to understand the underlying abiotic or biotic factors resulting in differences in pesticide use. Here I take advantage of longitudinal data from the US Department of Agriculture Census of Agriculture to revisit whether landscape simplification is a consistent driver of insecticide use. I perform cross-sectional analyses for five USDA census years in seven Midwestern US states at the county level. I follow this with a panel data analysis using a fixed-effects model, which identifies the effect of landscape simplification on insecticide use using year-to-year variation within counties.

I specifically focus on insecticides in these states to compare this multiyear analysis with a recent single-year study by Meehan et al. . I check the robustness of these results by comparing data from the USDA Census of Agriculture to the National Agricultural Statistics Service Cropland Data Layer , and check different selection criteria for included counties. I compare these results to that of Meehan et al. , who used the same data sources and model specifications for 2007 only, and find that incorporating multiple years of data as I do here provides insights impossible to glean from a single data year.Annual expenditure on insecticides is over 4 billion dollars in the United States , which equates to the use of almost 100 million pounds of active ingredients . Given the many health and environmental consequences related to insecticide exposure, it is critical to understand what farm, landscape, or environmental characteristics drive the insect pests that motivate insecticide use. It has long been thought that landscape simplification is one of these characteristics. Reviews of empirical evidence for this theory have been largely inconclusive , although a recent statistical analysis of the Midwestern United States in 2007 found a strong, positive relationship between landscape simplification and insecticide use . Here I analyzed data from five USDA Census of Agriculture years using cross-sectional and fixed-effects models. The cross sectional results show that landscape simplification does not consistently drive higher insecticide use. Although the coefficient on proportion of county in cropland, my metric for landscape simplification, is positive and significant in the 2007 analyses, that relationship is absent or reversed in prior census years. Furthermore, adjacent census years, such as 2002–2007 and 1992– 1997, show large changes in the magnitude and changes in significance of the landscape-simplification coefficient.It is evident that the drivers of insecticide use may not be easily or reliably identified using single time-period studies. Using a fixed-effects model to remove unobserved characteristics, I find a non-significant relationship between landscape simplification and proportion of county in cropland. Counter intuitively, these results suggest that as cropland increases, the proportion of cropland sprayed with insecticides is unaffected.

The existence of a null relationship between landscape simplification and insecticide use is not unlike the results of Hutchison et al. , who reported large reductions in the European corn borer in non-Bacillus thuringiensis corn as a positive externality from B. thuringiensis corn plantings. Although pesticides may have negative effects on public health, biodiversity, and ecosystem services,grow bucket the application of pesticides by a nearby farm may reduce pest incidence on surrounding farms because of pesticide drift or pest suppression . Additionally, as the amount of land in cropland increases, opportunities for invasion from natural or untreated areas may be reduced. As a result of landscape simplification, natural lands have been isolated to farm boundaries, fallow lands, or wood lots . Numerous ecological studies have found that these fragmented natural or less intensively managed areas can act as a source for natural enemies and pest species that recolonize species poor crop fields . If the cost of pest invasion is greater than the benefits of natural enemy pest suppression stemming from non-crop land, these habitats can have a net negative impact on the farmer in terms of pest control. The above mechanisms may explain why a null relationship is observed in the fixed-effects model; however, they do not account for the importance of year. What could explain the wild variation in the landscape simplification coefficient in the cross sectional analyses and why year fixed effects are so important? There are a number of drivers that could be behind the year-to year variability, and deciphering which mechanism is at play is critical because different policy measures are needed to address different types of drivers. For example, a stochastic driver such as weather could be the culprit. Insect development is strongly influenced by weather conditions, such as temperature and precipitation, and thus yearly differences in these or other environmental conditions could have an important effect on insecticide demand and the relationship between landscape simplification and insecticide use. Preliminary analysis indicates that the effect of weather on this relationship is complex. [Preliminary analysis using growing season precipitation and degree days based on the National Climatic Data Center Global Historical Climatology Network Daily file does not explain the variation in the cross-sectional relationship between landscape simplification and insecticide use.] This finding may be because the timing of pest arrival relative to the growing season may determine the likelihood of pest outbreaks and the benefits of applying insecticides . Furthermore, temperature and precipitation affect the survival and development of different pests differently, and thus which pests and enemies are present may determine the effect of weather on the relationship between landscape simplification and insecticide use. Refined data on pest outbreaks or type and timing of insecticide use are currently not available for the study area examined. However, the development of such data or further empirical study focusing on abiotic conditions would greatly increase our understanding of the link between weather events and insect outbreaks, and thus increase our ability to forecast variation in insecticide use both now and under future climate change. It is also conceivable that the change in the relationship between landscape simplification and insecticide use between 2007 and all previous years reflects a systematic and predictable trend in insecticide use. For example, in 1996 there was a major change in the regulation of pesticides in the form of the Food Quality Protection Act .

FQPA prompted the reevaluation of all registered pesticides, and promoted the use of more selective, less persistent “reduced-risk” pesticides via a fast-track registration process . FQPA could affect the relationship between landscape simplification and insecticide use because insecticides that are effective against a multitude of insect pests and persist in the environment for longer periods of time may have provided higher positive externalities to surrounding crop fields, thus necessitating less insecticide use in landscapes dominated by agricultural fields. The implementation of FQPA and the resulting use restrictions took 10 y, and phasing out of certain chemicals is still in progress . Because changes in available insecticides were occurring between 1996 and 2007, it is difficult to statistically evaluate the effect of FQPA on the results reported here. Future Census’ of Agriculture or more refined insecticide data that include information on the active ingredient in use could elucidate how policy changes are interacting with the relationship between landscape simplification and insecticide use. Agriculture has vast impacts on the Earth’s environment and these impacts are only expected to grow as demand increases in the coming decades . The challenge, as Balmford et al. discuss, is how to meet the increasing demand with the least effect on native biodiversity and the ecosystem services intact ecosystems provide. There are various advantages and disadvantages to whether increased demand should be met by increased intensity of farming on current agricultural land or by increased land conversion to agriculture to be farmed with more biologically harmonious farming methods . In the Midwestern United States, it appears that land-sparing at the county level does not lead to consistent increases in the proportion of cropland treated with insecticides. However, without understanding what is behind the year-to-year variation in the relationship between landscape simplification and insecticide use, it is impossible to predict how land sharing or land-sparing as a policy initiative would affect insecticide use in the future. As suggested by this study and recent empirical reviews , the presence and direction of the relationship between landscape simplification and insecticide use can be positive, negative, or null. If this variation is driven by variation in yearly weather, whether simplified landscapes cause more or less insecticide use could flip flop unpredictably. If the variation is driven by extreme weather or weather characteristics that will be altered with climate change, perhaps there will be some directionality. If the relationship between landscape simplification and insecticide use is an indirect consequence of management policies, perhaps 2007 is a glimpse of the future. The data available are currently inadequate to decipher the underlying mechanisms. However, given the different policy implications of a stochastic driver, such as weather, versus a predictable driver, such as regulatory change, developing the necessary data sources to tease apart the underlying causes is imperative. Perhaps most importantly, this study emphasizes the need for longer-term research agendas, especially when investigating a politically, economically, and ecologically important question, such as insecticide or pesticide use.