Assessing nitrate groundwater hotspots in Europe reveals an inadequate designation of Nitrate Vulnerable Zones.

Monitoring networks show that the European Union Nitrates Directive (ND) has had mixed success in reducing nitrate concentrations in groundwater. By combining machine learning and monitored nitrate concentrations (1992-2019), we estimate the total area of nitrate hotspots in Europe to be 401,000 km2, with 47% occurring outside of Nitrate Vulnerable Zones (NVZs). We also found contrasting increasing or decreasing trends, varying per country and time periods. We estimate that only 5% of the 122,000 km2 of hotspots in 2019 will meet nitrate quality standards by 2040 and that these may be offset by the appearance of new hotspots. Our results reveal that the effectiveness of the ND is limited by both time-lags between the implementation of good practices and pollution reduction and an inadequate designation of NVZs. Substantial improvements in the designation and regulation of NVZs are necessary, as well as in the quality of monitoring stations in terms of spatial density and information available concerning sampling depth, if the objectives of EU legislation to protect groundwater are to be achieved.

Influence of key factors on ammonia and nitrous oxide emission factors for excreta deposited by livestock and land-applied manure.

Ammonia (NH3) and nitrous oxide (N2O) emissions from livestock manure management have a significant impact on air quality and climate change. There is an increasing urgency to improve our understanding of drivers influencing these emissions. We analysed the DATAMAN ("DATAbase for MANaging greenhouse gas and ammonia emissions factors") database to identify key factors influencing (i) NH3 emission factors (EFs) for cattle and swine manure applied to land and (ii) N2O EFs for cattle and swine manure applied to land, and (iii) cattle urine, dung and sheep urine deposited during grazing. Slurry dry matter (DM) content, total ammoniacal nitrogen (TAN) concentration and method of application were significant drivers of NH3 EFs from cattle and swine slurry. Mixed effect models explained 14-59 % of the variance in NH3 EFs. Apart from the method of application, the significant influence of manure DM, manure TAN concentration or pH on NH3 EFs suggests mitigation strategies should focus on these. Identifying key factors influencing N2O EFs from manures and livestock grazing was more challenging, likely because of the complexities associated with microbial processes and soil physical properties impacting N2O production and emissions. Generally, significant factors were soil-related e.g. soil water content, pH, clay content, suggesting mitigations may need to consider the conditions of the receiving environment for manure spreading and grazing deposition. Total variability explained by terms in mixed effect model was on average 66 %, with the random effect 'experiment identification number' explaining, on average, 41 % of the total variability in the models. We suspect this term captured the effect of non-measured manure, soil and climate factors and any biases in application and measurement technique effects associated with individual experiments. This analysis has helped to improve our understanding of key factors of NH3 and N2O EFs for inclusion within models. With more studies over time, insights into the underlying processes influencing emissions will be further improved.

Short communication: Identifying key parameters for modelling the impacts of livestock health conditions on greenhouse gas emissions.

Improved animal health can reduce greenhouse gas (GHG) emissions intensity in livestock systems while increasing productivity. Integrated modelling of disease impacts on farm-scale emissions is important in identifying effective health strategies to reduce emissions. However, it requires that modellers understand the pathways linking animal health to emissions and how these might be incorporated into models. A key barrier to meeting this need has been the lack of a framework to facilitate effective exchange of knowledge and data between animal health experts and emissions modellers. Here, these two communities engaged in workshops, online exchanges and a survey to i) identify a comprehensive list of disease-related model parameters and ii) test its application to evaluating models. Fifty-six parameters were identified and proved effective in assessing the potential of farm-scale models to characterise livestock disease impacts on GHG emissions. Easy wins for the emissions models surveyed include characterising disease impacts related to feeding.

To what extent is climate change adaptation a novel challenge for agricultural modellers?

Modelling is key to adapting agriculture to climate change (CC), facilitating evaluation of the impacts and efficacy of adaptation measures, and the design of optimal strategies. Although there are many challenges to modelling agricultural CC adaptation, it is unclear whether these are novel or, whether adaptation merely adds new motivations to old challenges. Here, qualitative analysis of modellers' views revealed three categories of challenge: Content, Use, and Capacity. Triangulation of findings with reviews of agricultural modelling and Climate Change Risk Assessment was then used to highlight challenges specific to modelling adaptation. These were refined through literature review, focussing attention on how the progressive nature of CC affects the role and impact of modelling. Specific challenges identified were: Scope of adaptations modelled, Information on future adaptation, Collaboration to tackle novel challenges, Optimisation under progressive change with thresholds, and Responsibility given the sensitivity of future outcomes to initial choices under progressive change.

Comparison of ammonia emissions related to nitrogen use efficiency of livestock production in Europe.

The increasing global demand for food and the environmental effects of reactive nitrogen losses in the food production chain, increase the need for efficient use of nitrogen (N). Of N harvested in agricultural plant products, 80% is used to feed livestock. Because the largest atmospheric loss of reactive nitrogen from livestock production systems is ammonia (NH3), the focus of this paper is on N lost as NH3 during the production of animal protein. The focus of this paper is to understand the key factors explaining differences in Nitrogen Use Efficiency (NUE) of animal production among various European countries. Therefore we developed a conceptual framework to describe the NUE defined as the amount of animal-protein N per N in feed and NH3-N losses in the production of milk, beef, pork, chicken meat and eggs in The Netherlands, Switzerland, United Kingdom, Germany, Austria and Denmark. The framework describes how manure management and animal-related parameters (feed, metabolism) relate to NH3 emissions and NUE. The results showed that the animal product with the lowest NUE had the largest NH3 emissions and vice versa, which agrees with the reciprocal relationship between NUE and NH3 within the conceptual framework. Across animal products for the countries considered, about 20% of the N in feed is lost as NH3. The significant smallest proportion (12%) of NH3-N per unit of Nfeed is from chicken production. The proportions for other products are 17%, 19%, 20% and 22% for milk, pork, eggs and beef respectively. These differences were not significantly different due to the differences among countries. For all countries, NUE was lowest for beef and highest for chicken. The production of 1 kg N in beef required about 5 kg N in feed, of which 1 kg N was lost as NH3-N. For the production of 1 kg N in chicken meat, 2 kg N in feed was required and 0.2 kg was lost as NH3. The production of 1 kg N in milk required 4 kg N in feed with 0.6 kg NH3-N loss, the same as pork and eggs, but those needed 3 and 3.5 kg N in feed per kg N in product respectively. Except for beef, the differences among these European countries were mainly caused by differences in manure management practices and their emission factors, rather than by animal-related factors including feed and digestibility influencing the excreted amount of ammoniacal N (TAN). For beef, both aspects caused important differences. Based on the results, we encourage the expression of N losses as per N in feed or per N in product, in addition to per animal place, when comparing production efficiency and NUE. We consider that disaggregating emission factors into a diet/animal effect and a manure management effect would improve the basis for comparing national NH3 emission inventories.

How do farm models compare when estimating greenhouse gas emissions from dairy cattle production?

The European Union Effort Sharing Regulation (ESR) will require a 30% reduction in greenhouse gas (GHG) emissions by 2030 compared with 2005 from the sectors not included in the European Emissions Trading Scheme, including agriculture. This will require the estimation of current and future emissions from agriculture, including dairy cattle production systems. Using a farm-scale model as part of a Tier 3 method for farm to national scales provides a more holistic and informative approach than IPCC (2006) Tier 2 but requires independent quality control. Comparing the results of using models to simulate a range of scenarios that explore an appropriate range of biophysical and management situations can support this process by providing a framework for placing model results in context. To assess the variation between models and the process of understanding differences, estimates of GHG emissions from four farm-scale models (DairyWise, FarmAC, HolosNor and SFARMMOD) were calculated for eight dairy farming scenarios within a factorial design consisting of two climates (cool/dry and warm/wet)×two soil types (sandy and clayey)×two feeding systems (grass only and grass/maize). The milk yield per cow, follower:cow ratio, manure management system, nitrogen (N) fertilisation and land area were standardised for all scenarios in order to associate the differences in the results with the model structure and function. Potential yield and application of available N in fertiliser and manure were specified separately for grass and maize. Significant differences between models were found in GHG emissions at the farm-scale and for most contributory sources, although there was no difference in the ranking of source magnitudes. The farm-scale GHG emissions, averaged over the four models, was 10.6 t carbon dioxide equivalents (CO2e)/ha per year, with a range of 1.9 t CO2e/ha per year. Even though key production characteristics were specified in the scenarios, there were still significant differences between models in the annual milk production per ha and the amounts of N fertiliser and concentrate feed imported. This was because the models differed in their description of biophysical responses and feedback mechanisms, and in the extent to which management functions were internalised. We conclude that comparing the results of different farm-scale models when applied to a range of scenarios would build confidence in their use in achieving ESR targets, justifying further investment in the development of a wider range of scenarios and software tools.

Epidemiology of fractures in Armenia: development of a country-specific FRAX model and comparison to its surrogate.

Fracture probabilities derived from the surrogate FRAX model for Armenia were compared to those from the model based on regional estimates of the incidence of hip fracture. Disparities between the surrogate and authentic FRAX models indicate the importance of developing country-specific FRAX models. Despite large differences between models, differences in the rank order of fracture probabilities were minimal. OBJECTIVE: Armenia has relied on a surrogate FRAX model based on the fracture epidemiology of Romania. This paper describes the epidemiology of fragility fractures in Armenia used to create an Armenia-specific FRAX model with an aim of comparing this new model with the surrogate model. METHODS: We carried out a population-based study in two regions of Armenia (Ararat and Vayots Dzor representing approximately 11% of the country's population). We aimed to identify all low-energy fractures: retrospectively from hospital registers in 2011-2012 and prospectively in 2013 with the inclusion of primary care sources. RESULTS: The differences in incidence between the surveys with and without data from primary care suggested that 44% of patients sustaining a hip fracture did not receive specialized medical care. A similar proportion of forearm and humeral fractures did not come to hospital attention (48 and 49%, respectively). Only 57.7% of patients sustaining a hip fracture were hospitalized. In 2013, hip fracture incidence at the age of 50 years or more was 201/100,000 for women and 136/100,000 for men, and age- and sex-specific rates were incorporated into the new "authentic" FRAX model for Armenia. Compared to the surrogate model, the authentic model gave lower 10-year fracture probabilities in men and women aged less than 70 years but substantially higher above this age. Notwithstanding, there were very close correlations in fracture probabilities between the surrogate and authentic models (> 0.99) so that the revisions had little impact on the rank order of risk. CONCLUSION: A substantial proportion of major osteoporotic fractures in Armenia do not come to hospital attention. The disparities between surrogate and authentic FRAX models indicate the importance of developing country-specific FRAX models. Despite large differences between models, differences in the rank order of fracture probabilities were minimal.

NitroScape: a model to integrate nitrogen transfers and transformations in rural landscapes.

Modelling nitrogen transfer and transformation at the landscape scale is relevant to estimate the mobility of the reactive forms of nitrogen (N(r)) and the associated threats to the environment. Here we describe the development of a spatially and temporally explicit model to integrate N(r) transfer and transformation at the landscape scale. The model couples four existing models, to simulate atmospheric, farm, agro-ecosystem and hydrological N(r) fluxes and transformations within a landscape. Simulations were carried out on a theoretical landscape consisting of pig-crop farms interspersed with unmanaged ecosystems. Simulation results illustrated the effect of spatial interactions between landscape elements on N(r) fluxes and losses to the environment. More than 10% of the total N(2)O emissions were due to indirect emissions. The nitrogen budgets and transformations of the unmanaged ecosystems varied considerably, depending on their location within the landscape. The model represents a new tool for assessing the effect of changes in landscape structure on N(r) fluxes.

Developments in greenhouse gas emissions and net energy use in Danish agriculture - how to achieve substantial CO(2) reductions?

Greenhouse gas (GHG) emissions from agriculture are a significant contributor to total Danish emissions. Consequently, much effort is currently given to the exploration of potential strategies to reduce agricultural emissions. This paper presents results from a study estimating agricultural GHG emissions in the form of methane, nitrous oxide and carbon dioxide (including carbon sources and sinks, and the impact of energy consumption/bioenergy production) from Danish agriculture in the years 1990-2010. An analysis of possible measures to reduce the GHG emissions indicated that a 50-70% reduction of agricultural emissions by 2050 relative to 1990 is achievable, including mitigation measures in relation to the handling of manure and fertilisers, optimization of animal feeding, cropping practices, and land use changes with more organic farming, afforestation and energy crops. In addition, the bioenergy production may be increased significantly without reducing the food production, whereby Danish agriculture could achieve a positive energy balance.

Effects of farm heterogeneity and methods for upscaling on modelled nitrogen losses in agricultural landscapes.

The aim of this study is to illustrate the importance of farm scale heterogeneity on nitrogen (N) losses in agricultural landscapes. Results are exemplified with a chain of N models calculating farm-N balances and distributing the N-surplus to N-losses (volatilisation, denitrification, leaching) and soil-N accumulation/release in a Danish landscape. Possible non-linearities in upscaling are assessed by comparing average model results based on (i) individual farm level calculations and (ii) averaged inputs at landscape level. Effects of the non-linearities that appear when scaling up from farm to landscape are demonstrated. Especially in relation to ammonia losses the non-linearity between livestock density and N-loss is significant (p > 0.999), with around 20-30% difference compared to a scaling procedure not taking this non-linearity into account. A significant effect of farm type on soil N accumulation (p > 0.95) was also identified and needs to be included when modelling landscape level N-fluxes and greenhouse gas emissions.

Case report: Chalazion and its features visualized by ultrahigh resolution optical coherence tomography.

PURPOSE: The purpose of this case report is to highlight the clinical characteristics of a recurrent chalazion through the use of digital photography and ultra-high resolution optical coherence tomography (UHROCT). CASE REPORT: A single case is presented, along with digital biomicroscopic photographs and UHROCT images. DISCUSSION: A review of the literature describing the histopathological and associations of chalazia and other disorders, suggest it may be possible to differentiate different eyelid conditions based on their clinical manifestations and appearance on UHROCT tomograms. Based on the images presented here, it appears that this case is typical of a post-menopausal incidence of chalazion and risk for acne rosacea.

Chaotic Hamiltonian ratchets for pulsed periodic double-well potentials: classical correlations and the ratchet current.

We present analytical derivations of the diffusion rates, ratchet currents, and time scales of a new ratchet in a fully chaotic Hamiltonian system, introduced in Phys. Rev. Lett. 89, 194102 (2002)] with a proposed implementation using atoms in pulsed standing waves of light. The origin of this type of ratchet current is in asymmetric momentum diffusion rates which result when a "double-well" lattice is pulsed with unequal "kick" periods. The form of the new short-time correlations which modify the diffusion rates are derived. The resulting formulas for the classical energy diffusion rates are shown to give good agreement with numerical simulations. A closed analytical formula for the ratchet current is also obtained, which predicts correctly the current magnitudes and current reversals. The characteristic "ratchet time," a classical time scale associated with the momentum-diffusion ratchet is derived analytically. The competition between the ratchet time t(r) , and the quantum break time t(*) is investigated further.

Proposal for a chaotic ratchet using cold atoms in optical lattices.

We investigate a new type of quantum ratchet which may be realized by cold atoms in a double-well optical lattice, pulsed with unequal periods. The classical dynamics is chaotic and we find the classical diffusion rate D is asymmetric in momentum up to a finite time t(r). The quantum behavior produces a corresponding asymmetry in the momentum distribution which is "frozen-in" by dynamical localization provided the break time t(*)>or=t(r). We conclude that the cold atom ratchets require Db/ variant Planck's over 2pi approximately 1, where b is a small deviation from period-one pulses.

Long-term fluctuation in short-wavelength automated perimetry in glaucoma suspects and glaucoma patients.

PURPOSE: To determine the magnitude of the homogeneous, LF(Ho), and the heterogeneous, LF(He), components of the long-term fluctuation (LF) in glaucoma suspects and in stable primary open angle glaucoma (POAG) patients undergoing short-wavelength automated perimetry (SWAP) and to compare the magnitude of the SWAP LF components with those elicited by standard white-on-white (W-W) perimetry. METHODS: The sample comprised 33 glaucoma suspects and 17 patients with early-to-moderate stable POAG who underwent W-W perimetry and SWAP at each of six visits over a mean period of 12.75 months (SD, 2.29). The LF(Ho), LF(He), and error components of the long-term fluctuation were determined between the third and seventh visual field examinations. The intervening visual field examinations and the optic nerve head parameters, derived both by stereo observation and by the Heidelberg Retinal Tomograph, were used to confirm stability over the follow-up period. RESULTS: The LF(Ho) and LF(He) components were larger in the POAG patients than in the glaucoma suspects for both W-W perimetry and SWAP; the magnitude was independent of the depth of defect and of the short-term fluctuation. All three components of long-term fluctuation were greater for SWAP than for W-W perimetry, both in the glaucoma suspects and in the POAG patients. CONCLUSIONS: SWAP exhibits greater long-term fluctuation than white-on-white perimetry. The usefulness of SWAP will be limited if the extent of this variability is not overcome in future statistical procedures developed to detect progressive visual field loss.

N-losses and energy use in a scenario for conversion to organic farming.

The aims of organic farming include the recycling of nutrients and organic matter and the minimisation of the environmental impact of agriculture. Reduced nitrogen (N)-losses and energy (E)-use are therefore fundamental objectives of conversion to organic farming. However, the case is not straightforward, and different scenarios for conversion to organic farming might lead to reduced or increased N-losses and E-use. This paper presents a scenario tool that uses a Geographical Information System in association with models for crop rotations, fertilisation practices, N-losses, and E-uses. The scenario tool has been developed within the multidisciplinary research project Land Use and Landscape Development Illustrated with Scenarios (ARLAS). A pilot scenario was carried out, where predicted changes in N-losses and E-uses following conversion to organic farming in areas with special interests in clean groundwater were compared. The N-surplus and E-use were on average reduced by 10 and 54%, respectively. However, these reductions following the predicted changes in crop rotations, livestock densities, and fertilisation practices were not large enough to ensure a statistically significant reduction at the 95% level. We therefore recommend further research in how conversion to organic farming or other changes in the agricultural practice might help to reduce N-surpluses and E-uses. In that context, the presented scenario tool would be useful.

The long-term fluctuation of the visual field in stable glaucoma.

PURPOSE: To determine, in stable glaucoma, the characteristics of the between-examination variability of the visual field recorded with the Humphrey Field Analyser (HFA; Humphrey Systems, Dublin, CA) using the homogeneous, LF(Ho), and heterogeneous, LF(He), components of the long-term fluctuation (LF), thereby providing a technique for separating progressive loss from fluctuation in sensitivity. METHODS: The LF components were calculated using a two-factor analysis of variance (ANOVA) with replications and were determined between each pair of three successive HFA program 30-2 fields for each patient from two groups, each containing 30 patients with primary open-angle glaucoma. The interval between examinations for the first group was 6 to 9 months and for the second group was 3 weeks. RESULTS: The group mean values for LF(Ho) ranged from 1.50 to 2.19 dB and for LF(He) from 1.70 to 2.05 dB. The average difference between examinations was within +/-0.35 dB for each component, and the 95% limits of agreement for the two groups, respectively, were +/- 2.31 and +/- 2.39 dB for the LF(Ho) and +/- 2.36 and +/- 2.09 dB for the LF(He). The estimate of the 90% confidence limit for the LF(Ho) was 3.30 dB and for the LF(He), 3.60 dB. Little relationship was present between the LF components and the modulus differences in mean deviation (MD), the corrected pattern SD (CPSD), or the mean MD, mean short-term fluctuation, and mean CPSD, of the two fields. CONCLUSIONS: Estimation of the LF components and of the corresponding confidence limits yields an expression of the normal between-examination variability of two consecutive fields that can be used as a reliability index. A value outside the confidence limits indicates the necessity for a confirmatory follow-up field.

T lymphocyte-triggering factor of african trypanosomes is associated with the flagellar fraction of the cytoskeleton and represents a new family of proteins that are present in several divergent eukaryotes.

The trypanosome cytoskeleton consists almost entirely of microtubule-based structures. Although alpha- and beta-tubulin from Trypanosoma brucei have been well characterized, much less is known about other cytoskeleton-associated proteins in trypanosomes. Using biochemical fractionation, we demonstrate here that T lymphocyte-triggering factor (TLTF) from T. brucei is a component of the detergent-resistant and Ca(2+)-resistant fraction of the parasite cytoskeleton. This fraction contains the flagellar apparatus and a subset of cytoskeletal protein complexes that together function in cell motility, cytokinesis, and organelle inheritance. We also show that TLTF-related genes are present in several highly divergent eukaryotic organisms. Although the function of the corresponding proteins is not known, the mammalian TLTF-like gene (GAS11; growth arrest-specific gene 11) is up-regulated in growth-arrested cells and is a candidate tumor suppressor (Whitmore, S. A., Settasatian, C., Crawford, J., Lower, K. M., McCallum, B., Seshadri, R., Cornelisse, C. J., Moerland, E. W., Cleton-Jansen, A. M., Tipping, A. J., Mathew, C. G., Savnio, M., Savoia, A., Verlander, P., Auerbach, A. D., Van Berkel, C., Pronk, J. C., Doggett, N. A., and Callen, D. F. (1998) Genomics 52, 325-331), suggestive of a role in coordinating cytoskeleton activities. Consistent with this possibility, we show that the human GAS11 protein contains a 144-amino acid domain that co-localizes with microtubules when fused to the green fluorescent protein and expressed in mammalian cells. These findings suggest that TLTF represents a newly defined protein family, whose members contribute to cytoskeleton function in species as diverse as protozoa and mammals.

A novel protein targeting domain directs proteins to the anterior cytoplasmic face of the flagellar pocket in African trypanosomes.

The flagellar pocket of African trypanosomes is a critical sorting station for protein and membrane trafficking, and is considered to be an Achilles' heel of this deadly pathogen. Although several proteins, including receptors for host-derived growth factors, are targeted specifically to the flagellar pocket, the signals responsible for this restricted subcellular localization are entirely unknown. Using T lymphocyte triggering factor-green fluorescent protein (TLTF(1)-GFP) fusion proteins, we demonstrate that an internal 144 amino acid domain of TLTF from Trypanosoma brucei is sufficient for directing GFP to the cytoplasmic side of the anterior flagellar pocket. Immuno-gold electron microscopy reveals that the TLTF-GFP fusion protein is located in an electron dense structure that immediately abuts the anterior flagellar pocket membrane. The amino acid sequence of the TLTF targeting domain does not resemble previously characterized protein trafficking signals, and random mutagenesis reveals that flagellar pocket targeting is conferred by a structural motif, rather than a short, contiguous array of amino acids. The aberrant sorting of two mutant proteins into the flagellum, and the targeting of a related human protein to the plus end of the trypanosome's cytoskeletal microtubules, lead us to suggest that flagellar pocket targeting involves interactions with the trypanosome cytoskeleton. The finding that TLTF-GFP is restricted to the anterior, cytoplasmic face of the flagellar pocket membrane, suggests that there is structural heterogeneity in the membrane of this organelle.

Pointwise univariate linear regression of perimetric sensitivity against follow-up time in glaucoma.

PURPOSE: The authors compared pointwise univariate linear regression (ULR) of sensitivity against follow-up as an indicator of visual field progression with that of the corresponding ULR of mean deviation (MD) and with the Glaucoma Change Probability (GCP) analysis. The authors determined the influence of the number and sequence of prior examinations on the slope of the pointwise function. METHODS: Univariate linear regression was undertaken at each stimulus location on the arbitrarily assigned left eyes of 38 patients with glaucoma examined with the Humphrey Field Analyzer Programs 30-2 or 24-2 (stimulus size III, Humphrey Instruments Inc, San Leandro, CA). The mean age was 59.0 years (standard deviation [SD] = 12.9), the mean number of fields per patients was 12.0 (SD = 2.8), and the mean duration of follow-up was 6.0 years (SD = 1.6). RESULTS: Four patients showed statistically significant MD slopes. Of the 34 patients exhibiting a nonsignificant MD slope, 15 exhibited clusters of at least two contiguous progressing locations. Less than half of these locations were designated as progressing by GCP. The GCP detected less than one third of the locations considered progressing by ULR for the last six fields in the series: this was attributed to the nonlinear nature of the decline in sensitivity. CONCLUSIONS: The degree of agreement between the outcomes of ULR and GCP was dependent on the quality of the collected data, the magnitude of the baseline sensitivity, the extent and type of the subsequent visual field progression, and the position of the fields within the examination series. Good agreement was illustrated at those locations where the deterioration fell outside the limits of expected variability in stable glaucoma.