CONTEXT: To meet the nutritional and environmental needs of a growing population, dairy producers must increase milk production while minimizing the farm-gate environmental impact and adapting to the effects of climate change. OBJECTIVE: Here we comprehensively assess the effects of climate change on the environmental performance and productivity of three typical US dairy farms, and evaluate the potential benefits of adaptation strategies and implementation of Beneficial Management Practices (BMPs) for mitigating these effects and the potential in-creases in environmental impact. METHODS: Using the Integrated Farm System Model (IFSM), we predicted the productivity and environmental impact of these baseline farms under current emission scenarios and climate projections of 6 general circulation models (GCM), for high and low emission scenarios. We simulated farm-specific BMPs for current and future climate conditions for both unadapted and 'adapted' field cultivation plans, based on experiences from other climate locations. Finally, the IFSM predictions were compared to those of two other process-based models to test result robustness. RESULTS AND CONCLUSIONS: We find that the environmental impact of the three northern US dairy farms (New York, Pennsylvania, and Wisconsin) generally increases by mid-century, if no mitigation measures are taken. Overall, feed production is maintained, as decreased corn grain yields are compensated by increased forage yields. Adoption of farm-specific Beneficial Management Practices can substantially reduce the GHG emissions and nutrient losses from dairy farms under current climate conditions and stabilize the environmental impact in future climate conditions, while maintaining farm productivity (milk and feed production). A comparison of three models corroborates the estimated reductions in methane and ammonia emissions associated with BMPs, as well as the relative trend in P-loss reduction. SIGNIFICANCE: This study provides a holistic assessment of the impacts of climate change on dairy production systems focusing on both feed production and environmental impacts. It demonstrates the interest of BMPs to both reduce GHG emissions and contribute to more resilient farming systems in a changing climate.

It is well recognized that there are currently limitations in the spatial and temporal resolution of environmental exposure models due to significant variabilities and uncertainties in model inputs and parameters. Here we present the updated Pangea multi-scale multimedia model based on the more spatially resolved, catchment-based hydrological HydroBASINS dataset covering the entire globe. We apply it to predict spatially-explicit exposure concentrations of linear alkylbenzene sulphonate (LAS) and triclosan (TCS) as two chemicals found in homecare (HC) and personal care (PC) products in river catchments across Asia, and test its potential for identifying/prioritizing catchments with higher exposure concentrations. In addition, we also identify the key parameters in the model framework driving higher concentrations and perform uncertainty analyses by applying Monte Carlo simulations on emissions and other non-spatial model inputs. The updated combination of Pangea with the HydroBASINS hydrological data represents a substantial improvement from the previous model with the gridded hydrological dataset (WWDRII) for modelling substance fate, with higher resolution and improved coverage in regions with lower flows, with the results demonstrating good agreement with monitored concentrations for TCS in both the freshwater (R-2 = 0.55) and sediment (R-2 = 0.81) compartments. The ranking of water basins by Predicted Environmental Concentrations (PECs) was similar for both TCS and LAS, with highest concentrations (Indus, Huang He, Cauvery, Huai He and Ganges) being one to two orders of magnitude greater than the water basins with lowest predicted PECs (Mekong and Brahmaputra). Emissions per unit volume of each catchment, chemical persistence, and river discharge were deemed to be the most influential factors on the variation of predicted PECs. Focusing on the Huang He (Yellow River) water basin, uncertainty confidence intervals (factor 31 for LAS and 6 for TCS) are much lower than the variability of predicted PECs across the Huang He catchments (factors 90,700 for LAS and 13,500 for TCS).

The influence of farm-specific beneficial management practices (BMPs) on a set of comprehensive environmental impacts was characterized and quantified for two representative dairy farms in the Great Lakes region (a large 1500-cow farm in New York (NY) and a smaller 150-cow farm in Wisconsin (WI)). Comparative benefits or drawbacks of the effect of the adoption of selected management scenarios to environmental impacts were estimated by coupling the output from the Integrated Farm System Model (IFSM) to provide lifecycle inventory data for SimaPro©. The small dairy farm in WI generated a consistently larger carbon footprint than the large dairy farm in NY due to greater enteric methane (CH4) emissions from different feeds fed and greater nitrous oxide (N2O) emissions from the bedded pack housing facility for young stock. The flare scenario of burning biogas produced in a covered manure storage on the small farm and a whole-farm mitigation plan of combined feed, field, and manure management in the large farm demonstrate significant potential to reduce overall carbon footprint by 20.0% and 25.8%, respectively, compared to the baselines. The assessments of selective impact categories such as fossil energy use, water use, land occupation, aquatic eutrophication, terrestrial acidification, respiratory effects, human toxicity, and ecotoxicity are discussed and highlight hot spots relevant for sustainable dairy farm management. Normalization analysis indicates that eutrophication potential is the largest relative impact profile, which suggests that efforts to mitigate eutrophication can achieve relatively greater environmental impact reduction. Although this study identifies the beneficial adaptation of sustainable dairy production practices on individual impact profiles, trade-offs between impact categories make the analysis more complex when considering the comprehensive suite of environmental impacts.

Assessing and improving the sustainability of dairy production is essential to secure future food production. Implementation of Beneficial Management Practices (BMP) can mitigate GHG emissions and nutrient losses and reduce the environmental impact of dairy production, but comprehensive, whole-farm studies that evaluate the efficacy of multiple BMPs to reduce multiple environmental impacts and that include an assessment of productivity and farm profitability, are scarce. We used a process-based model (IFSM) to assess the efficacy of (10+) individual BMPs to reduce the carbon (C) footprint expressed per unit of milk produced of two model dairy farms, a 1500 cow farm and a 150 cow farm, with farming practices representative for the Great Lakes region. In addition to the C footprint, we assessed the effect of BMP implementation on the reactive nitrogen (N) footprint and total phosphorus (P) losses (per unit of milk produced), as well as milk production and farm profitability. We evaluated individual farm-component specific BMPs, that is, 5 dietary manipulations, 3 (150 cow farm) or 4 (1500 cow farm) manure interventions, and 6 field interventions, as well as an integrated whole-farm mitigation strategy based on the best performing individual BMPs. Our results show that reductions in the C footprint expressed per unit of milk are greatest with individual manure management interventions (4–20% reduction) followed by dietary manipulations (0–12% reduction) for both farm types. Field management BMPs had a modest effect on reducing this footprint (0–3% reduction), but showed substantial potential to reduce the reactive N footprint (0–19% reduction) and P losses (1–47% reduction). We found that the whole-farm mitigation strategy can substantially reduce the C footprint, reactive N footprint and total P loss of both farms with predicted reductions of approximately 41%, 41% and 46% respectively, while increasing milk production and the net return per cow by approximately 11% and 27%. To contextualize IFSM predictions for the whole-farm mitigation, we compared components of IFSM predictions to those of three other process-based models (CNCPS, Manure-DNDC and EPIC). While we did observe differences in model predictions for individual flows (particularly P erosion and P leaching losses), with exception of the total P loss, the models generally predicted similar overall mitigation potentials. Overall, our analysis shows that an integrated set of BMPs can be implemented to reduce GHG emissions and nutrient losses of dairy farms in the Great Lakes region without sacrificing productivity or profit to the farmer.

Precise control of the glutathione/glutathione disulfide (GSH/GSSG) redox balance is vital for the developing embryo, but regulatory mechanisms are poorly understood. We developed a novel, mechanistic mass-balance model for GSH metabolism in the organogenesis stage (gestational day 10.0-11.13) rat conceptus predicting the dynamics of 8 unique metabolites in 3 conceptal compartments: the visceral yolk sac (VYS), the extra-embryonic fluid (EEF) and the embryo proper (EMB). Our results show that thiol concentrations in all compartments are well predicted by the model. Protein synthesis is predicted to be a major efflux pathway for all amino acid precursors of GSH synthesis and an essential model element. Our model provides quantitative insights in the transport fluxes and enzymatic fluxes needed to maintain thiol redox balances under normal physiological conditions. This is crucial to further elucidate the mechanisms through which chemical exposure can perturb redox homeostasis, causing oxidative stress, and potentially birth defects.

Assessing and improving the sustainability of dairy production systems is essential to secure future food production. This requires a holistic approach to reveal trade-offs between emissions of the different greenhouse gases (GHG) and nutrient-based pollutants and to ensure that interactions between farm components are taken into account. Process-based models are essential to support whole-farm mass balance accounting. However, since variation between process-based model results can be large, there is a need to compare and better understand the strengths and limitations of various models. Here, we use a whole-farm mass-balance approach to compare five process-based models in terms of predicted carbon (C), nitrogen (N) and phosphorus (P) flows and potential global warming impact (GWI) associated with milk production at the animal, field and farm-scale. We include two whole-farm models complemented by two field-scale models and one animal-based model. A whole-farm mass-balance framework was used to facilitate model comparison at different scales. GWIs were calculated from predicted emissions of methane (CH4) and nitrous oxide (N2O) and soil C change. Results show that predicted whole-farm GWIs were similar for the two whole farm models, ManureDNDC and IFSM, with a predicted GWI of 9.3 and 10.8 Gg CO2eq. year−1 for ManureDNDC and IFSM, respectively. Enteric CH4 emissions were the single most important source of greenhouse gas emissions contributing 47%–70% of the total farm GWI. Model predictions were comparable, that is, within a factor of 1.5, for most flows related to the animal, barn and manure management system. In contrast, predicted field emissions of N2O and ammonia (NH3) to air, N and P losses to the hydrosphere and soil C change, were highly variable across models. This indicates that there is a need to further our understanding of soil and crop N, P and C flows and that measurement data on nutrient and C flows are particularly needed for the field. In addition, there is a need to further understand how anaerobic digestion influences manure composition and subsequent emissions of N2O and NH3 after application of digestate to the field. Empirical data on manure composition before and after anaerobic digestion are essential for model evaluation.

•A whole farm mass-balance framework was used to compare five process-based models.•Models were compared in terms of predicted nutrient (N,P,C) flows and GHG emissions.•Predicted nutrient and carbon flows related to the animal, barn and manure management are comparable.•Predicted field emissions were very variable across models.•Insight is provided in model strengths and weaknesses and key areas of improvement.

Precise control of the glutathione (GSH): glutathione disulfide (GSSG) balance is vital for the developing embryo, but it is not yet well understood how GSH levels and the GSH redox state are regulated, maintained, and modulated over the course of mammalian embryonic development. In this study, we characterize and connect thiol redox dynamics, protein synthesis, volumetric growth and net cysteine fluxes over the course of early organogenesis (gestational day (GD) 10-GD11.13) in the rat embryo. Our results show that despite a significant exponential growth of conceptal volumes and protein mass, the GSH: GSSG redox balance is remarkably stable during early organogenesis, with distinct redox potentials for the visceral yolk sac (VYS) (- 218 mV) and the embryo proper (EMB) (- 222 mV). The yolk sac was found to play a key role in maintaining GSH levels and the GSH: GSSG redox balance in the developing embryo. Based on an overall cysteine (Cys) mass-balance, we show that until GD10.6, yolk sac supply of Cys, the rate-limiting precursor for GSH synthesis, is sufficient to sustain embryonic demands for its GSH synthesis and protein synthesis needs. After GD10.6, the EMB maintains the amino acid intake flux, resulting in a significant depletion of most thiols in the amniotic fluid and the yolk sac fluid. Cysteine, was found to be predominantly used for de novo protein synthesis in the developing embryo (approximately 90% of total Cys). Protein synthesis (rates) should thus be included in any quantitative assessment of GSH redox dynamics in the developing embryo. Our time-course dataset of thiol dynamics, developed exponential relationships for protein synthesis and volumetric growth, and yolk sac surface area-mediated protein influx, provide important quantitative insights in GSH redox dynamics during embryonic development and are a prerequisite to further develop quantitative 'systems biology' models for GSH metabolism in the developing embryo.

Quantitative structure-activity relationships (QSARs) were developed to predict the in vitro clearance (CLINT) of xenobiotics metabolised in human hepatocytes (118 compounds) and microsomes (115 compounds). Clearance values were gathered from the scientific literature and multiple linear models were built and validated selecting at most 6 predictors from a pool of over 2000 potential molecular descriptors. For the hepatocytes QSAR, the explained variance (R-adj(2)) was 67% and the predictive ability (R-ext(2)) was 62%. For the microsomes QSAR, R-adj(2) was 50% and R-ext(2) 30%. For both liver assays, the most important descriptor relates to electronic properties of the compound. Functional groups of fragments were useful to identify specific compounds that have a deviating reaction rate compared to the others, such as polychlorobiphenyls (PCBs) and organic amides which were poorly metabolised by hepatocytes and microsomes, respectively. For hepatocytes, clearance was predominantly determined by electronic characteristics, while size and shape characteristics were less important and partitioning properties were absent. This may suggest that uptake across the membrane and enzyme binding are not rate-limiting steps. Particularly for hepatocytes the QSAR statistics are encouraging, allowing application of the outcomes in in vitro to in vivo extrapolation. (C) 2016 Elsevier B.V. All rights reserved.

Human demands on marine resources and space are currently unprecedented and concerns are rising over observed declines in marine biodiversity. A quantitative understanding of the impact of industrial activities on the marine environment is thus essential. Life cycle assessment (LCA) is a widely applied method for quantifying the environmental impact of products and processes. LCA was originally developed to assess the impacts of land-based industries on mainly terrestrial and freshwater ecosystems. As such, impact indicators for major drivers of marine biodiversity loss are currently lacking. We review quantitative approaches for cause–effect assessment of seven major drivers of marine biodiversity loss: climate change, ocean acidification, eutrophication-induced hypoxia, seabed damage, overexploitation of biotic resources, invasive species and marine plastic debris. Our review shows that impact indicators can be developed for all identified drivers, albeit at different levels of coverage of cause–effect pathways and variable levels of uncertainty and spatial coverage. Modeling approaches to predict the spatial distribution and intensity of human-driven interventions in the marine environment are relatively well-established and can be employed to develop spatially-explicit LCA fate factors. Modeling approaches to quantify the effects of these interventions on marine biodiversity are less well-developed. We highlight specific research challenges to facilitate a coherent incorporation of marine biodiversity loss in LCA, thereby making LCA a more comprehensive and robust environmental impact assessment tool. Research challenges of particular importance include i) incorporation of the non-linear behavior of global circulation models (GCMs) within an LCA framework and ii) improving spatial differentiation, especially the representation of coastal regions in GCMs and ocean-carbon cycle models.

The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test the ability of GUTS to predict survival of aquatic organisms across different pesticide exposure patterns, time scales and species. Firstly, using synthetic data, we identified experimental data requirements which allow for the estimation of all parameters of the GUTS proper model. Secondly, we assessed how well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates to build species sensitivity distributions for different exposure patterns. We find that GUTS adequately predicts survival across exposure patterns that vary over time. When toxicity is assessed for time-variable concentrations species may differ in their responses depending on the exposure profile. This can result in different species sensitivity rankings and safe levels. The interplay of exposure pattern and species sensitivity deserves systematic investigation in order to better understand how organisms respond to stress, including humans.

Quantitative structure-activity relationships (QSARs) were developed to predict the Michaelis-Menten constant (K-m) and the maximum reaction rate (V-max) of xenobiotics metabolised by four enzyme classes in mammalian livers: alcohol dehydrogenase (ADH), aldehyde dehydrogenase (ALDH), flavin-containing monooxygenase (FMO), and cytochrome P450 (CYP). Metabolic constants were gathered from the literature and a genetic algorithm was employed to select at most six predictors from a pool of over 2000 potential molecular descriptors using two-thirds of the xenobiotics in each enzyme class. The resulting multiple linear models were cross-validated using the remaining one-third of the compounds. The explained variances (R-adj(2)) of the QSARs were between 50% and 80% and the predictive abilities (R-ext(2)) between 50% and 60%, except for the V-max QSAR of FMO with both R-adj(2) and R-ext(2) less than 30%. The V-max values of FMO were independent of substrate chemical structure because the rate-limiting step of its catalytic cycle occurs before compound oxidation. For the other enzymes, V-max was predominantly determined by functional groups or fragments and electronic properties because of the strong and chemical-specific interactions involved in the metabolic reactions. The most relevant predictors for K-m were functional groups or fragments for the enzymes metabolising specific compounds (ADH, ALDH and FMO) and size and shape properties for CYP, likely because of the broad substrate specificity of CYP enzymes. The present study can be helpful to predict the K-m and V-max of four important oxidising enzymes in mammals and better understand the underlying principles of chemical transformation by liver enzymes. (C) 2014 Elsevier B.V. All rights reserved.

The increased application of nanoparticles (NPs) is increasing the risk of their release into the environment. Although many toxicity studies have been conducted, the environmental risk is difficult to estimate, because uptake mechanisms are often not determined in toxicity studies. In the present study, the authors review dominant uptake mechanisms of NPs in cells, as well as the effect of NP properties, experimental conditions, and cell type on NP uptake. Knowledge of NP uptake is crucial for risk assessment and is essential to predict the behavior of NPs based on their physical-chemical properties. Important uptake mechanisms for eukaryotic cells are macropinocytosis, receptor-mediated endocytosis, and phagocytosis in specialized mammalian cells. The studies reviewed demonstrate that uptake into nonphagocytic cells depends strongly on NP size, with an uptake optimum at an NP diameter of approximately 50 nm. Increasing surface charges, either positive or negative, have been shown to increase particle uptake in comparison with uncharged NPs. Another important factor is the degree of (homo-) aggregation. Results regarding shape have been ambiguous. Difficulties in the production of NPs, with 1 property changed at a time, call for a full characterization of NP properties. Only then will it be possible to draw conclusions as to which property affected the uptake. Environ Toxicol Chem 2014;33:481-492. (c) 2013 SETAC

Biotransformation is one of the processes which influence the bioaccumulation of chemicals. The enzymatic action of metabolism involves two processes, i.e. the binding of the substrate to the enzyme followed by a catalytic reaction, which are described by the Michaelis–Menten constant (Km) and the maximum rate (Vmax). Here, we developed Quantitative Structure–Activity Relationships (QSARs) for Log(1/Km) and LogVmax for substrates of four enzyme classes. We focused on oxidations catalysed by alcohol dehydrogenase (ADH), aldehyde dehydrogenase (ALDH), flavin-containing monooxygenase (FMO) and cytochrome P450 (CYP) in mammals. The chemicals investigated were xenobiotics, including alcohols, aldehydes, pesticides and drugs. We applied general linear models for this purpose, employing descriptors related to partitioning, geometric characteristics, and electronic properties of the substrates, which can be interpreted mechanistically. The explained variance of the QSARs varied between 20% and 70%, and it was larger for Log(1/Km) than for LogVmax. The increase of 1/Km with compound logP and size suggests that weak interactions are important, e.g. by substrate binding via desolvation processes. The importance of electronic factors for 1/Km was described in relation to the catalytic mechanism of the enzymes. Vmax was particularly influenced by electronic properties, such as dipole moment and energy of the lowest unoccupied molecular orbital. This can be explained by the nature of the catalysis, characterised by the cleavage and formation of covalent or ionic bonds (strong interactions). The present study may be helpful to understand the underlying principles of the chemical specific activity of four important oxidising enzymes.

ToxicoKinetic ToxicoDynamic (TKTD) models are considered essential tools to further advance acute toxicity prediction of metals for a range of species and exposure conditions, but they are currently underutilized. We present a mechanistic TKTD model for acute toxicity prediction of silver (Ag) in freshwater organisms. In this new approach, we explicitly link relevant TKTD processes to species (physiological) characteristics, which facilitates model application to other untested freshwater organisms. The model quantifies the reduction in whole-body sodium concentration over time as a function of the target site inhibition over time, the target site density and the species-specific sodium turnover rate. Freshwater species are assumed to die instantly when they have lost a critical amount of their initial whole-body sodium concentration. Results show that mortality is significantly related to sodium loss (r(2) = 0.86) for various aquatic organisms and exposure durations. The model accurately predicts lethal effect concentrations for different freshwater organisms, including Daphnia magna, rainbow trout and juvenile crayfish, and is able to capture the observed size-specific variation of nearly 2 orders of magnitude in empirical LC50s.

It is unknown whether metal absorption efficiencies in terrestrial soft-bodied species can be predicted with the same metal properties as for aquatic species. Here, we developed models for metal absorption efficiency from the dissolved phase for terrestrial worms and several aquatic species, based on 23 metal physicochemical properties. For the worms, the absorption efficiency was successfully related to 7 properties, and is best predicted with the ionic potential. Different properties (8 in total) were found to be statistically significant in regressions predicting metal absorption in aquatic species, with the covalent index being the best predictor. It is hypothesized that metal absorption by soft-bodied species in soil systems is influenced by the rate of metal supply to the membrane, while in aquatic systems accumulation is solely determined by metal affinity to membrane bound transport proteins. Our results imply that developing predictive terrestrial bioaccumulation and toxicity models for metals must consider metal interactions with soil solids. This may include desorption of a cation bound to soil solids through ion exchange, or metal release from soil surfaces involving breaking of metal-oxygen bonds. (C) 2014 Published by Elsevier Ltd.

Most toxicokinetic models consider passive diffusion as the only mechanism when modeling the oral uptake of chemicals. However, the overall uptake of nutrients and xenobiotics, such as pharmaceuticals and environmental pollutants, can be increased by influx transport proteins. We incorporated carrier-mediated transport into a one-compartment toxicokinetic model originally developed for passive diffusion only. The predictions were compared with measured oral uptake efficiencies of nutrients and pharmaceuticals, i.e. the fraction of the chemical reaching systemic circulation. Including carrier-mediated uptake improved model predictions for hydrophilic nutrients (RMSE = 10% vs. 56%, Coefficient of Efficiency CoE = 0.5 vs. -9.6) and for pharmaceuticals (RMSE = 21% vs. 28% and CoE = -0.4 vs. -1.1). However, the negative CoE for pharmaceuticals indicates that further improvements are needed. Most important in this respect is a more accurate estimation of v(mAx) and K-m as well as the determination of the amount of expressed and functional transport proteins both in vivo and in vitro. (C) 2014 Elsevier B.V. All rights reserved.

Assessing and improving the sustainability of dairy production systems requires an accurate quantification of greenhouse gas (GHG) emissions and major nutrient (N, C, P) flows associated with milk production at the animal, farm and field-scale. Life cycle inventory databases are, however, often based on rough estimates of GHG emissions and nutrient flows, and cannot account for spatially-explicit variation in these flows. Emission estimates can be improved when underlying processes influencing GHG emissions and nutrient balances are explicitly considered. We aim to improve life cycle inventory databases for milk production in the US by integrating process-based models into LCA data acquisition. We therefore perform a quantitative comparison of five process-based models to determine major nutrient flows and GHG emissions of milk production at the animal, farm and field-scale.

Crude oil poses a risk to marine ecosystems due to its toxicity and tendency to accumulate in biota. The present study evaluated the applicability of the OMEGA model for estimating oil accumulation in aquatic species by comparing model predictions of kinetic rates (absorption and elimination) and bioconcentration factors (BCF) with measured values. The model was a better predictor than the means of the measurements for absorption and elimination rate constants, but did not outperform the mean measured BCF. Model estimates and measurements differed less than one order of magnitude for 91%, 80% and 61% of the absorption and elimination rates and BCFs of all oil constituents, respectively. Of the "potentially modifying" factors: exposure duration, biotransformation, molecular mass, and water temperature, the last two tended to influence the performance of the model. Inclusion of more explanatory variables in the bioaccumulation model, like the molecular mass, is expected to improve model performance.