CLIMATE CHANGE IMPACTS ON US AGRICULTURE

There is general consensus in the scientific literature that human-induced climate change has taken place and will continue to do so over the next century. The Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change concludes with “very high confidence” that anthropogenic activities such as fossil fuel burning and deforestation have affected the global climate. The AR4 also indicates that global average temperatures are expected to increase by another 1.1°C to 5.4°C by 2100, depending on the increase in atmospheric concentrations of greenhouse gases that takes place during this time. Increasing atmospheric carbon dioxide levels, temperature increases, altered precipitation patterns and other factors influenced by climate have already begun to impact U.S. agriculture. Climate change will continue to have significant effects on U.S. agriculture, water resources, land resources, and biodiversity in the future as temperature extremes begin exceeding thresholds that harm crop growth more frequently and precipitation and runoff patterns continue to change. In this study, we provide an assessment of the potential long-term implications of climate change on landowner decisions regarding land use, crop mix, and production practices in the U.S., combining a crop process model (Environmental Policy Integrated Climate model) and an economic model of the U.S. forestry and agricultural sector (Forest and Agricultural Sector Optimization Model). Agricultural producers have always faced numerous production and price risks, but forecasts of more rapid changes in climatic conditions in the future have raised concerns that these risks will increase in the future relative to historical conditions.climate change, crop yields, EPIC, FASOM, Crop Production/Industries, Environmental Economics and Policy, International Relations/Trade, Land Economics/Use, Resource /Energy Economics and Policy, C61, Q18, Q54,

Regional Estimation of Soil Carbon and Other Environmental Indicators Using EPIC and i_EPIC

Computer models are important tools for assessing regional carbon sequestration and other environmental impacts of agricultural management practices. The Environmental Policy Integrated Climate (EPIC) model is a very flexible model that has been used to make a wide range of field- and regional-scale environmental assessments. Large regional-scale applications of EPIC and similar models can require thousands of runs, resulting in a huge data management task. To address this problem, the Center for Agricultural and Rural Development (CARD) has developed an interactive EPIC (i_EPIC) software package that provides an automated approach to executing large sets of EPIC simulations. Overviews of both the latest EPIC version and the i_EPIC software package are presented. We also present examples of regional applications using both EPIC and i_EPIC conducted by the Resource and Environmental Policy Division of CARD, by the Joint Global Change Research Institute of the University of Maryland and the Pacific Northwest National Laboratory, and by the Resource Assessment Division of the Natural Resources Conservation Service, U.S. Department of Agriculture

Prospects for the Measurement of the Higgs Yukawa Couplings to b and c quarks, and muons at CLIC

The investigation of the properties of the Higgs boson, especially a test of the predicted linear dependence of the branching ratios on the mass of the final state is going to be an integral part of the physics program at colliders at the energy frontier for the foreseeable future. The large Higgs boson production cross section at a 3TeV CLIC machine allows for a precision measurement of the Higgs branching ratios. The cross section times branching ratio of the decays H->bb, H->cc and H->{\mu}{\mu} of a Standard Model Higgs boson with a mass of 120 GeV can be measured with a statistical uncertainty of 0.23%, 3.1% and 15%, respectively, assuming an integrated luminosity of 2 ab-1.Comment: 6 pages, 4 figure

Multi-Year Lags Between Forest Browning and Soil Respiration at High Northern Latitudes

High-latitude northern ecosystems are experiencing rapid climate changes, and represent a large potential climate feedback because of their high soil carbon densities and shifting disturbance regimes. A significant carbon flow from these ecosystems is soil respiration (RS, the flow of carbon dioxide, generated by plant roots and soil fauna, from the soil surface to atmosphere), and any change in the high-latitude carbon cycle might thus be reflected in RSobserved in the field. This study used two variants of a machine-learning algorithm and least squares regression to examine how remotely-sensed canopy greenness (NDVI), climate, and other variables are coupled to annual RS based on 105 observations from 64 circumpolar sites in a global database. The addition of NDVI roughly doubled model performance, with the best-performing models explaining ~62% of observed RS variability. We show that early-summer NDVI from previous years is generally the best single predictor of RS, and is better than current-year temperature or moisture. This implies significant temporal lags between these variables, with multi-year carbon pools exerting large-scale effects. Areas of decreasing RS are spatially correlated with browning boreal forests and warmer temperatures, particularly in western North America. We suggest that total circumpolar RS may have slowed by ~5% over the last decade, depressed by forest stress and mortality, which in turn decrease RS. Arctic tundra may exhibit a significantly different response, but few data are available with which to test this. Combining large-scale remote observations and small-scale field measurements, as done here, has the potential to allow inferences about the temporal and spatial complexity of the large-scale response of northern ecosystems to changing climate

Investigation of fatigue by Australian General Practice Registrars: a cross-sectional study

INTRODUCTION: Fatigue is the most common undifferentiated problem presenting in general practice. Previous studies have shown that this presentation leads to multiple investigations. There is no published literature describing the management of patients with fatigue by general practice (GP) registrars. AIM: To document the investigation-ordering behaviour of GP registrars in managing patients with a new diagnosis of unexplained fatigue. METHODS: This was a cross-sectional analysis of data from Registrar Clinical Encounters in Training (ReCEnT), an ongoing cohort study of GP registrars’ consultations. We established the prevalence of new diagnoses of unexplained fatigue and associations with that diagnosis, the rate of test ordering and the number and types of investigations ordered. RESULTS: 644 registrars contributed data from 68 986 encounters. In 0.78% of patient encounters, a new diagnosis of unexplained fatigue was made. Pathology was ordered in 78.4% of these problems (versus 18.1% in non-fatigue problems), at a rate of 488 tests per 100 new fatigue problems. DISCUSSION: Our study suggests that unexplained fatigue elicits a non-rational approach to test ordering by registrars. These findings contribute to the understanding of GP registrar management of fatigue, and undifferentiated presentations more broadly, and suggest educational approaches to improve practice, including dealing with uncertainty

Regional scale cropland carbon budgets: Evaluating a geospatial agricultural modeling system using inventory data

Accurate quantification and clear understanding of regional scale cropland carbon (C) cycling is critical for designing effective policies and management practices that can contribute toward stabilizing atmospheric CO2 concentrations. However, extrapolating site-scale observations to regional scales represents a major challenge confronting the agricultural modeling community. This study introduces a novel geospatial agricultural modeling system (GAMS) exploring the integration of the mechanistic Environmental Policy Integrated Climate model, spatially-resolved data, surveyed management data, and supercomputing functions for cropland C budgets estimates. This modeling system creates spatiallyexplicit modeling units at a spatial resolution consistent with remotely-sensed crop identification and assigns cropping systems to each of them by geo-referencing surveyed crop management information at the county or state level. A parallel computing algorithm was also developed to facilitate the computationally intensive model runs and output post-processing and visualization. We evaluated GAMS against National Agricultural Statistics Service (NASS) reported crop yields and inventory estimated county-scale cropland C budgets averaged over 2000e2008. We observed good overall agreement, with spatial correlation of 0.89, 0.90, 0.41, and 0.87, for crop yields, Net Primary Production (NPP), Soil Organic C (SOC) change, and Net Ecosystem Exchange (NEE), respectively. However, we also detected notable differences in the magnitude of NPP and NEE, as well as in the spatial pattern of SOC change. By performing crop-specific annual comparisons, we discuss possible explanations for the discrepancies between GAMS and the inventory method, such as data requirements, representation of agroecosystem processes, completeness and accuracy of crop management data, and accuracy of crop area representation. Based on these analyses, we further discuss strategies to improve GAMS by updating input data and by designing more efficient parallel computing capability to quantitatively assess errors associated with the simulation of C budget components. The modularized design of the GAMS makes it flexible to be updated and adapted for different agricultural models so long as they require similar input data, and to be linked with socio-economic models to understand the effectiveness and implications of diverse C management practices and policies

Predicting the public health benefit of vaccinating cattle against Escherichia coli O157

Identifying the major sources of risk in disease transmission is key to designing effective controls. However, understanding of transmission dynamics across species boundaries is typically poor, making the design and evaluation of controls particularly challenging for zoonotic pathogens. One such global pathogen is Escherichia coli O157, which causes a serious and sometimes fatal gastrointestinal illness. Cattle are the main reservoir for E. coli O157, and vaccines for cattle now exist. However, adoption of vaccines is being delayed by conflicting responsibilities of veterinary and public health agencies, economic drivers, and because clinical trials cannot easily test interventions across species boundaries, lack of information on the public health benefits. Here, we examine transmission risk across the cattle–human species boundary and show three key results. First, supershedding of the pathogen by cattle is associated with the genetic marker stx2. Second, by quantifying the link between shedding density in cattle and human risk, we show that only the relatively rare supershedding events contribute significantly to human risk. Third, we show that this finding has profound consequences for the public health benefits of the cattle vaccine. A naïve evaluation based on efficacy in cattle would suggest a 50% reduction in risk; however, because the vaccine targets the major source of human risk, we predict a reduction in human cases of nearly 85%. By accounting for nonlinearities in transmission across the human–animal interface, we show that adoption of these vaccines by the livestock industry could prevent substantial numbers of human E. coli O157 cases

Real-time continuous glucose monitoring in preterm infants (REACT): an international, open-label, randomised controlled trial.

BACKGROUND: Hyperglycaemia and hypoglycaemia are common in preterm infants and have been associated with increased risk of mortality and morbidity. Interventions to reduce risk associated with these exposures are particularly challenging due to the infrequent measurement of blood glucose concentrations, with the potential of causing more harm instead of improving outcomes for these infants. Continuous glucose monitoring (CGM) is widely used in adults and children with diabetes to improve glucose control, but has not been approved for use in neonates. The REACT trial aimed to evaluate the efficacy and safety of CGM in preterm infants requiring intensive care. METHODS: This international, open-label, randomised controlled trial was done in 13 neonatal intensive care units in the UK, Spain, and the Netherlands. Infants were included if they were within 24 h of birth, had a birthweight of 1200 g or less, had a gestational age up to 33 weeks plus 6 days, and had parental written informed consent. Infants were randomly assigned (1:1) to real-time CGM or standard care (with masked CGM for comparison) using a central web randomisation system, stratified by recruiting centre and gestational age (<26 or ≥26 weeks). The primary efficacy outcome was the proportion of time sensor glucose concentration was 2·6-10 mmol/L for the first week of life. Safety outcomes related to hypoglycaemia (glucose concentrations <2·6 mmol/L) in the first 7 days of life. All outcomes were assessed on the basis of intention to treat in the full analysis set with available data. The study is registered with the International Standard Randomised Control Trials Registry, ISRCTN12793535. FINDINGS: Between July 4, 2016, and Jan 27, 2019, 182 infants were enrolled, 180 of whom were randomly assigned (85 to real-time CGM, 95 to standard care). 70 infants in the real-time CGM intervention group and 85 in the standard care group had CGM data and were included in the primary analysis. Compared with infants in the standard care group, infants managed using CGM had more time in the 2·6-10 mmol/L glucose concentration target range (mean proportion of time 84% [SD 22] vs 94% [11]; adjusted mean difference 8·9% [95% CI 3·4-14·4]), equivalent to 13 h (95% CI 5-21). More infants in the standard care group were exposed to at least one episode of sensor glucose concentration of less than 2·6 mmol/L for more than 1 h than those in the intervention group (13 [15%] of 85 vs four [6%] of 70). There were no serious adverse events related to the use of the device or episodes of infection. INTERPRETATION: Real-time CGM can reduce exposure to prolonged or severe hyperglycaemia and hypoglycaemia. Further studies using CGM are required to determine optimal glucose targets, strategies to obtain them, and the potential effect on long-term health outcomes. FUNDING: National Institute for Health Research Efficacy and Mechanisms Evaluation Programme.NIHR EM

Simulating Potential Switchgrass Production in the United States

Using results from field trials of switchgrass (Panicum virgatum L.) in the United States, the EPIC (Environmental Policy Integrated Climate) process-level agroecosystem model was calibrated, validated, and applied to simulate potential productivity of switchgrass for use as a biofuel feedstock. The model was calibrated with a regional study of 10-yr switchgrass field trials and subsequently tested against a separate compiled dataset of field trials from across the eastern half of the country. An application of the model in a national database using 8-digit watersheds as the primary modeling unit produces 30-yr average switchgrass yield estimates that can be aggregated to 18 major watersheds. The model projects average annual switchgrass productivity of greater than 7 Mg ha-1 in the Upper Mississippi, Lower Mississippi, and Ohio watersheds. The major factors limiting simulated production vary by region; low precipitation is the primary limiting factor across the western half of the country, while moderately acidic soils limit yields on lands east of the Mississippi River. Average projected switchgrass production on all crop land in the continental US is 5.6 Mg ha-1. At this level of productivity, 28.6 million hectares of crop land would be required to produce the 16 billion gallons of cellulosic ethanol called for by 2022 in the 2007 Energy Independence and Security Act. The model described here can be applied as a tool to inform the land-use and environmental consequences of switchgrass production