Mitigation of gaseous emissions from dairy livestock: A farm-level method to examine the financial implications.

Feeding the world's population while minimising the contribution of agriculture to climate change is one of the greatest challenges facing modern society. This challenge is particularly pronounced for dairy production where the carbon footprint of products and the mitigation costs are high, relative to other food stuffs. This paper reviews a number of mitigation measures that may be adopted by dairy farmers to reduce greenhouse gas emissions from their farms. A simulation model is developed to assess the cost-benefit of a range of mitigation measures. The model is applied to data from Ireland, a country with a large export-oriented dairy industry, for a range of farms including top, middle and bottom performing farms from a profitability perspective. The mitigation measures modelled included animal productivity, grass production and utilisation, better reproductive performance, early compact calving, reduced crude protein, decreased fertiliser N, protected urea, white clover, slurry tank cover and low emission slurry spreading (LESS). The results show that over half of the greenhouse gas abatement potential and most of the ammonia abatement potential were realised with cost-beneficial measures. Animal and feed-related measures that increased efficiency drove the abatement of GHG emissions. Low-emission slurry spreading was beneficial for the bottom and middle one-third of farms, while protected urea and reducing nitrogen use accounted for most of the ammonia abatement potential for the most profitable farms. Results showed that combining mitigation measures resulted in a decrease of 23%, 19%, and 12% in GHG emissions below 2020 levels for the bottom, middle, and top performing dairy farms, respectively. The findings imply that top dairy farms, that are already managed efficiently and optimally, may struggle to achieve the national and international GHG reduction targets with existing technologies and practices.

Evaluating the effects of grass management technologies on the physical, environmental, and financial performance of Irish pasture-based dairy farms.

Grass management technologies (grass measuring devices and grassland management decision support tools) have been identified as important tools to improve the performance of pasture-based dairy farms. They have the potential to significantly improve the efficiency and sustainability of dairy systems by increasing milk production through enhanced pasture growth and utilization, which would reduce the need for supplementary feeds, along with increased output, therefore increasing farm profitability and environmental sustainability. Despite the several potential benefits of grass management technologies, there is a lack of empirical research around the effects of these technologies on the performance of pasture-based dairy systems. The current study aimed to fill this knowledge gap by using a 2018 nationally representative survey of Irish dairy farms and a propensity score matching approach to determine the effects of adopting grass management technologies on the physical, environmental, and financial performance of Irish pasture-based dairy farms. The findings showed that dairy farms utilizing grass management technologies had, on average, higher farm physical, environmental, and financial performance (in terms of grazed pasture use, total pasture use, length of the grazing season, milk yield, milk solids, greenhouse gas emissions per kilogram of fat- and protein-corrected milk, gross output, and gross margin) compared with dairy farms not utilizing these technologies. However, when controlling for selection bias, we can only attribute a positive causal effect of grass management technology adoption on the use of grazed pasture per cow, grazing season length, milk yield per cow, and milk solids per cow. This might be due to dairy farmers not yet using the technologies to their full potential, 2018 being an unusual year in terms of weather (and therefore not being able to capture the full range of farm performance benefits), or because grass management technologies need to be adopted in association with other technologies and practices to achieve their expected performance outcomes. Future research should include updated farm-level data to capture the weather and learning effects and so be able to determine the impact of grass management technologies on a wider range of performance indicators.

Factors associated with intensity of technology adoption and with the adoption of 4 clusters of precision livestock farming technologies in Irish pasture-based dairy systems.

Precision livestock farming (PLF) technologies have been widely promoted as important tools to improve the sustainability of dairy systems due to perceived economic, social, and environmental benefits. However, there is still limited information about the level of adoption of PLF technologies (percentage of farms with a PLF technology) and the factors (farm and farmer characteristics) associated with PLF technology adoption in pasture-based dairy systems. The current research aimed to address this knowledge gap by using a representative survey of Irish pasture-based dairy farms from 2018. First, we established the levels of adoption of 9 PLF technologies (individual cow activity sensors, rising plate meters, automatic washers, automatic cluster removers, automatic calf feeders, automatic parlor feeders, automatic drafting gates, milk meters, and a grassland management decision-support tool) and grouped them into 4 PLF technology clusters according to the level of association with each other and the area of dairy farm management in which they are used. The PLF technology clusters were reproductive management technologies, grass management technologies, milking management technologies, and calf management technologies. Additionally, we classified farms into 3 categories of intensity of technology adoption based on the number of PLF technologies they have adopted (nonadoption, low intensity of adoption, and high intensity of adoption). Second, we determined the factors associated with the intensity of technology adoption and with the adoption of the PLF technology clusters. A multinomial logistic regression model and 4 logistic regressions were used to determine the factors associated with intensity of adoption (low and high intensity of adoption compared with nonadoption) and with the adoption of the 4 PLF technology clusters, respectively. Adoption levels varied depending on PLF technology, with the most adopted PLF technologies being those related to the milking process (e.g., automatic parlor feeders and milk meters). The results of the multinomial logistic regression suggest that herd size, proportion of hired labor, agricultural education, and discussion group membership were positively associated with a high intensity of adoption, whereas age of farmer and number of household members were negatively associated with high intensity of adoption. However, when analyzing PLF technology clusters, the magnitude and direction of the influence of the factors in technology adoption varied depending on the PLF technology cluster being investigated. By identifying the PLF technologies in which pasture-based dairy farmers are investing more and by detecting potential drivers and barriers for the adoption of PLF technologies, the current study could allow PLF technology companies, practitioners, and researchers to develop and target strategies that improve future adoption of PLF technologies in pasture-based dairy settings.

Early stages of sensorimotor map acquisition: neurochemical signature in primary motor cortex and its relation to functional connectivity.

The earliest stages of sensorimotor learning involve learning the correspondence between movements and sensory results-a sensorimotor map. The present exploratory study investigated the neurochemical underpinnings of map acquisition by monitoring 25 participants as they acquired a new association between movements and sounds. Functional magnetic resonance spectroscopy was used to measure neurochemical concentrations in the left primary motor cortex during learning. Resting-state functional magnetic resonance imaging data were also collected before and after training to assess learning-related changes in functional connectivity. There were monotonic increases in γ-aminobutyric acid (GABA) and decreases in glucose during training, which extended into the subsequent rest period and, importantly, in the case of GABA correlated with the amount of learning: participants who showed greater behavioral learning showed greater GABA increase. The GABA change was furthermore correlated with changes in functional connectivity between the primary motor cortex and a cluster of voxels in the right intraparietal sulcus: greater increases in GABA were associated with greater strengthening of connectivity. Transiently, there were increases in lactate and reductions in aspartate, which returned to baseline at the end of training, but only lactate showed a statistical trend to correlate with the amount of learning. In summary, during the earliest stages of sensorimotor learning, GABA levels are linked on a subject-level basis to both behavioral learning and a strengthening of functional connections that persists beyond the training period. The findings are consistent with the idea that GABA-mediated inhibition is linked to maintenance of newly learned information.NEW & NOTEWORTHY Learning the mapping between movements and their sensory effects is a necessary step in the early stages of sensorimotor learning. There is evidence showing which brain areas are involved in early motor learning, but their role remains uncertain. Here, we show that GABA, a neurotransmitter linked to inhibitory processing, rises during and after learning and is involved in ongoing changes in resting-state networks.

Validation of in vivo MRS measures of metabolite concentrations in the human brain.

PURPOSE: In vivo magnetic resonance spectroscopy (MRS) is the only technique capable of non-invasively assessing metabolite concentrations in the brain. The lack of alternative methods makes validation of MRS measures challenging. The aim of this study is to assess the validity of MRS measures of human brain metabolite concentrations by comparing multiple MRS measures acquired using different MRS acquisition sequences. METHODS: Single-voxel SPECIAL and MEGA-PRESS MR spectra were acquired from both the dorsolateral prefrontal cortex and primary motor cortices in 15 healthy subjects. The SPECIAL spectrum, as well as both the edit-off and difference spectra of MEGA-PRESS were each analyzed in LCModel to obtain estimates of the absolute concentrations of total choline (TCh; glycerophosphocholine + phosphocholine), total creatine (TCr; creatine + phosphocreatine), N-acetylaspartate (NAA), N-acetylaspartylglutamate (NAAG), NAA + NAAG, glutamate (Glu), glutamine (Gln), Glu + Gln, scyllo-inositol (Scyllo), myo-inositol (Ins), glutathione (GSH), γ-aminobutyric acid (GABA), lactate (Lac) and aspartate (Asp). Then, having obtained up to three independent measures of each metabolite per brain region per subject, correlations between the different measures were assessed. RESULTS: The degree of correlation between measures varied greatly across both the metabolites and sequences tested. As expected, metabolites with the most prominent spectral peaks (TCh, TCr, NAA + NAAG, Ins and Glu) had the most well-correlated measures between methods, while metabolites with less prominent spectral peaks (Lac, Gln, GABA, Asp, and NAAG) tended to have poorly-correlated measures between methods. Some metabolites with relatively less prominent spectral peaks (GSH, Scyllo) had fairly well-correlated measures between some methods. Combining metabolites improved the agreement between methods for measures of NAA + NAAG, but not for Glu + Gln. CONCLUSIONS: Given that the ground truth for in vivo MRS measures is never known, the method proposed here provides a promising means to assess the validity of in vivo MRS measures, which has not yet been explored widely.

Giardia and Cryptosporidium antibody prevalence and correlates of exposure among Alaska residents, 2007-2008.

Giardia duodenalis and Cryptosporidium spp. are common intestinal protozoa that can cause diarrhoeal disease. Although cases of infection with Giardia and Cryptosporidium have been reported in Alaska, the seroprevalence and correlates of exposure to these parasites have not been characterised. We conducted a seroprevalence survey among 887 residents of Alaska, including sport hunters, wildlife biologists, subsistence bird hunters and their families and non-exposed persons. We tested serum using a multiplex bead assay to evaluate antibodies to the Giardia duodenalis variant-specific surface protein conserved structural regions and to the Cryptosporidium parvum 17- and 27-kDa antigens. Approximately one third of participants in each group had evidence of exposure to Cryptosporidium. Prevalence of Giardia antibody was highest among subsistence hunters and their families (30%), among whom positivity was associated with lack of community access to in-home running water (adjusted prevalence ratio [aPR] 1.15, 95% confidence interval (CI) 1.02-1.28) or collecting rain, ice, or snow to use as drinking water (aPR 1.09, 95% CI 1.01-1.18). Improving in-home water access for entire communities could decrease the risk of exposure to Giardia.

Factors associated with profitability in pasture-based systems of milk production.

The global dairy industry needs to reappraise the systems of milk production that are operated at farm level with specific focus on enhancing technical efficiency and competitiveness of the sector. The objective of this study was to quantify the factors associated with costs of production, profitability, and pasture use, and the effects of pasture use on financial performance of dairy farms using an internationally recognized representative database over an 8-yr period (2008 to 2015) on pasture-based systems. To examine the associated effects of several farm system and management variables on specific performance measures, a series of multiple regression models were developed. Factors evaluated included pasture use [kg of dry matter/ha and stocking rate (livestock units/ha)], grazing season length, breeding season length, milk recording, herd size, dairy farm size (ha), farmer age, discussion group membership, proportion of purchased feed, protein %, fat %, kg of milk fat and protein per cow, kg of milk fat and protein per hectare, and capital investment in machinery, livestock, and buildings. Multiple regression analysis demonstrated costs of production per hectare differed by year, geographical location, soil type, level of pasture use, proportion of purchased feed, protein %, kg of fat and protein per cow, dairy farm size, breeding season length, and capital investment in machinery, livestock, and buildings per cow. The results of the analysis revealed that farm net profit per hectare was associated with pasture use per hectare, year, location, soil type, grazing season length, proportion of purchased feed, protein %, kg of fat and protein per cow, dairy farm size, and capital investment in machinery and buildings per cow. Pasture use per hectare was associated with year, location, soil type, stocking rate, dairy farm size, fat %, protein %, kg of fat and protein per cow, farmer age, capital investment in machinery and buildings per cow, breeding season length, and discussion group membership. On average, over the 8-yr period, each additional tonne of pasture dry matter used increased gross profit by €278 and net profit by €173 on dairy farms. Conversely, a 10% increase in the proportion of purchased feed in the diet resulted in a reduction in net profit per hectare by €97 and net profit by €207 per tonne of fat and protein. Results from this study, albeit in a quota limited environment, have demonstrated that the profitability of pasture-based dairy systems is significantly associated with the proportion of pasture used at the farm level, being cognizant of the levels of purchased feed.

Advanced processing and simulation of MRS data using the FID appliance (FID-A)-An open source, MATLAB-based toolkit.

PURPOSE: To introduce a new toolkit for simulation and processing of magnetic resonance spectroscopy (MRS) data, and to demonstrate some of its novel features. METHODS: The FID appliance (FID-A) is an open-source, MATLAB-based software toolkit for simulation and processing of MRS data. The software is designed specifically for processing data with multiple dimensions (eg, multiple radiofrequency channels, averages, spectral editing dimensions). It is equipped with functions for importing data in the formats of most major MRI vendors (eg, Siemens, Philips, GE, Agilent) and for exporting data into the formats of several common processing software packages (eg, LCModel, jMRUI, Tarquin). This paper introduces the FID-A software toolkit and uses examples to demonstrate its novel features, namely 1) the use of a spectral registration algorithm to carry out useful processing routines automatically, 2) automatic detection and removal of motion-corrupted scans, and 3) the ability to perform several major aspects of the MRS computational workflow from a single piece of software. This latter feature is illustrated through both high-level processing of in vivo GABA-edited MEGA-PRESS MRS data, as well as detailed quantum mechanical simulations to generate an accurate LCModel basis set for analysis of the same data. RESULTS: All of the described processing steps resulted in a marked improvement in spectral quality compared with unprocessed data. Fitting of MEGA-PRESS data using a customized basis set resulted in improved fitting accuracy compared with a generic MEGA-PRESS basis set. CONCLUSIONS: The FID-A software toolkit enables high-level processing of MRS data and accurate simulation of in vivo MRS experiments. Magn Reson Med 77:23-33, 2017. © 2015 Wiley Periodicals, Inc.

Persistence of antibody to Hepatitis A virus 20 years after receipt of Hepatitis A vaccine in Alaska.

Hepatitis A vaccine is recommended for children ≥1 year old to prevent hepatitis A virus (HAV) infection. However, the duration of vaccine-induced immunity is unknown. We evaluated a cohort of Alaska Native persons 20 years after HAV vaccination. Children aged 3-6 years had been previously randomized to receive three doses of HAV vaccine (360 ELISA units/dose) at: (i) 0,1,2 months; (ii) 0,1,6 months; and (iii) 0,1,12 months. We measured anti-HAV antibody concentrations every 2-3 years; described geometric mean concentrations (GMC) and the proportion with protective antibody (≥20 mIU mL-1 ) over time; and modelled the change in GMC using fractional polynomial regression. Of the 144 participants, after 20 years 52 (36.1%) were available for the follow-up (17, 18, 17 children in Groups A, B and C, respectively). Overall, 46 (88.5%) of 52 available participants had anti-HAV antibody concentrations ≥20 mIU mL-1 , and overall GMC was 107 mIU mL-1 . Although GMC levels were lower in Group A (60; CI 34-104) than in Group B (110; CI 68-177) or Group C (184; CI 98-345) (B vs C: P=.168; A vs B/C: P=.011), there was no difference between groups after adjusting for peak antibody levels post-vaccination (P=.579). Models predicted geometric mean concentrations of 124 mIU mL-1 after 25 years, and 106 mIU mL-1 after 30 years. HAV vaccine provides protective antibody levels 20 years after childhood vaccination. Lower antibody levels in Group A may be explained by a lower initial peak response. Our results suggest a booster vaccine dose is unnecessary for at least 25-30 years.

Housing characteristics and indoor air quality in households of Alaska Native children with chronic lung conditions.

Alaska Native children experience high rates of respiratory infections and conditions. Household crowding, indoor smoke, lack of piped water, and poverty have been associated with respiratory infections. We describe the baseline household characteristics of children with severe or chronic lung disease participating in a 2012-2015 indoor air study. We monitored indoor PM2.5, CO2 , relative humidity %, temperature, and VOCs and interviewed caregivers about children's respiratory symptoms. We evaluated the association between reported children's respiratory symptoms and indoor air quality indicators using multiple logistic regression analysis. Compared with general US households, study households were more likely overcrowded 73% (62%-82%) vs 3.2% (3.1%-3.3%); had higher woodstove use as primary heat source 16% (9%-25%) vs 2.1% (2.0%-2.2%); and higher proportion of children in a household with a smoker 49% (38%-60%) vs 26.2% (25.5%-26.8%). Median PM2.5 was 33 µg/m3 . Median CO2 was 1401 ppm. VOCs were detectable in all homes. VOCs, smoker, primary wood heat, and PM2.5>25 µg/m3 were associated with higher risk for cough between colds; VOCs were associated with higher risk for wheeze between colds and asthma diagnosis. High indoor air pollutant levels were associated with respiratory symptoms in household children, likely related to overcrowding, poor ventilation, woodstove use, and tobacco smoke.

A case-control study of risk factors for death from 2009 pandemic influenza A(H1N1): is American Indian racial status an independent risk factor?

Historically, American Indian/Alaska Native (AI/AN) populations have suffered excess morbidity and mortality from influenza. We investigated the risk factors for death from 2009 pandemic influenza A(H1N1) in persons residing in five states with substantial AI/AN populations. We conducted a case-control investigation using pandemic influenza fatalities from 2009 in Alaska, Arizona, New Mexico, Oklahoma and Wyoming. Controls were outpatients with influenza. We reviewed medical records and interviewed case proxies and controls. We used multiple imputation to predict missing data and multivariable conditional logistic regression to determine risk factors. We included 145 fatal cases and 236 controls; 22% of cases were AI/AN. Risk factors (P 45 years vs. <18 years], pre-existing medical conditions (mOR 7·1), smoking (mOR 3·0), delayed receipt of antivirals (mOR 6·5), and barriers to healthcare access (mOR 5·3). AI/AN race was not significantly associated with death. The increased influenza mortality in AI/AN individuals was due to factors other than racial status. Prevention of influenza deaths should focus on modifiable factors (smoking, early antiviral use, access to care) and identifying high-risk persons for immunization and prompt medical attention.

Impact of providing in-home water service on the rates of infectious diseases: results from four communities in Western Alaska.

Approximately 20% of rural Alaskan homes lack in-home piped water; residents haul water to their homes. The limited quantity of water impacts the ability to meet basic hygiene needs. We assessed rates of infections impacted by water quality (waterborne, e.g. gastrointestinal infections) and quantity (water-washed, e.g. skin and respiratory infections) in communities transitioning to in-home piped water. Residents of four communities consented to a review of medical records 3 years before and after their community received piped water. We selected health encounters with ICD-9CM codes for respiratory, skin and gastrointestinal infections. We calculated annual illness episodes for each infection category after adjusting for age. We obtained 5,477 person-years of observation from 1032 individuals. There were 9,840 illness episodes with at least one ICD-9CM code of interest; 8,155 (83%) respiratory, 1,666 (17%) skin, 241 (2%) gastrointestinal. Water use increased from an average 1.5 gallons/capita/day (g/c/d) to 25.7 g/c/d. There were significant (P-value < 0.05) declines in respiratory (16, 95% confidence interval (CI): 11-21%), skin (20, 95%CI: 10-30%), and gastrointestinal infections (38, 95%CI: 13-55%). We demonstrated significant declines in respiratory, skin and gastrointestinal infections among individuals who received in-home piped water. This study reinforces the importance of adequate quantities of water for health.

Reinfection after successful eradication of Helicobacter pylori in three different populations in Alaska.

We performed a study to determine rates of reinfection in three groups followed for 2 years after successful treatment: American Indian/Alaska Native (AI/AN) persons living in urban (group 1) and rural (group 2) communities, and urban Alaska non-Native persons (group 3). We enrolled adults diagnosed with H. pylori infection based on a positive urea breath test (13C-UBT). After successful treatment was documented at 2 months, we tested each patient by 13C-UBT at 4, 6, 12 and 24 months. At each visit, participants were asked about medication use, illnesses and risk factors for reinfection. We followed 229 persons for 2 years or until they became reinfected. H. pylori reinfection occurred in 36 persons; cumulative reinfection rates were 14·5%, 22·1%, and 12·0% for groups 1, 2, and 3, respectively. Study participants who became reinfected were more likely to have peptic ulcer disease (P = 0·02), low education level (P = 0·04), or have a higher proportion of household members infected with H. pylori compared to participants who did not become reinfected (P = 0·03). Among all three groups, reinfection occurred at rates higher than those reported for other US populations (<5% at 2 years); rural AI/AN individuals appear to be at highest risk for reinfection.

Relating the carbon footprint of milk from Irish dairy farms to economic performance.

Mitigating greenhouse gas (GHG) emissions per unit of milk or the carbon footprint (CF) of milk is a key issue for the European dairy sector given rising concerns over the potential adverse effects of climate change. Several strategies are available to mitigate GHG emissions, but producing milk with a low CF does not necessarily imply that a dairy farm is economically viable. Therefore, to understand the relationship between the CF of milk and dairy farm economic performance, the farm accountancy network database of a European Union nation (Ireland) was applied to a GHG emission model. The method used to quantify GHG emissions was life cycle assessment (LCA), which was independently certified to comply with the British standard for LCA. The model calculated annual on- and off-farm GHG emissions from imported inputs (e.g., electricity) up to the point milk was sold from the farm in CO2-equivalent (CO2-eq). Annual GHG emissions computed using LCA were allocated to milk based on the economic value of dairy farm products and expressed per kilogram of fat- and protein-corrected milk (FPCM). The results showed for a nationally representative sample of 221 grass-based Irish dairy farms in 2012 that gross profit averaged € 0.18/L of milk and € 1,758/ha and gross income was € 40,899/labor unit. Net profit averaged € 0.08/L of milk and € 750/ha and net income averaged € 18,125/labor unit. However, significant variability was noted in farm performance across each financial output measure. For instance, net margin per hectare of the top one-third of farms was 6.5 times higher than the bottom third. Financial performance measures were inversely correlated with the CF of milk, which averaged 1.20 kg of CO2-eq/kg of FPCM but ranged from 0.60 to 2.13 kg of CO2-eq/kg of FPCM. Partial least squares regression analysis of correlations between financial and environmental performance indicated that extending the length of the grazing season and increasing milk production per hectare or per cow reduced the CF of milk and increased farm profit. However, where higher milk production per hectare was associated with greater concentrate feeding, this adversely affected the CF of milk and economic performance by increasing both costs and off-farm emissions. Therefore, to mitigate the CF of milk and improve economic performance, grass-based dairy farms should not aim to only increase milk output, but instead target increasing milk production per hectare from grazed grass.

Detecting atoms trapped in an optical lattice using a tapered optical nanofiber.

Optical detection of structures with dimensions smaller than an optical wavelength requires devices that work on scales beyond the diffraction limit. Here we present the possibility of using a tapered optical nanofiber as a detector to resolve individual atoms trapped in an optical lattice in the Mott insulator phase. We show that the small size of the fiber combined with an enhanced photon collection rate can allow for the attainment of large and reliable measurement signals.

Shaping the evanescent field of optical nanofibers for cold atom trapping.

We investigate trapping geometries for cold, neutral atoms that can be created in the evanescent field of a tapered optical fibre by combining the fundamental mode with one of the next lowest possible modes, namely the HE(21) mode. Counter propagating red-detuned HE(21) modes are combined with a blue-detuned HE(11) fundamental mode to form a potential in the shape of four intertwined spirals. By changing the polarization from circular to linear in each of the two counter-propagating HE(21) modes simultaneously the 4-helix configuration can be transformed into a lattice configuration. The modification to the 4-helix configuration due to unwanted excitation of the the TE(01) and TM(01) modes is also discussed.

Extended grazing: a detailed analysis of Irish dairy farms.

Profitability and factors affecting grazing season length were econometrically analyzed using a representative sample of Irish dairy farms. The objective of this study was to explore what potential exists on Irish dairy farms to extend the grazing season and to quantify the possible economic benefits that result from lengthening the grazing season. Regression results indicate that location factors affect the length of the grazing season, but even when physical factors are controlled, farmer characteristics, such as education, also affect the grazing season length. The results of a panel data analysis show that significant cost reductions can be achieved by extending the grazing season. Overall, the findings indicate that lengthening the grazing season offers a cost-saving alternative on many Irish dairy farms, which could contribute to strengthening the competitiveness of the Irish dairy sector.

Visions for the 20th International Epidemiological Association's World Congress of Epidemiology (WCE 2014).

During August 17th-21st, 2014, the University of Alaska Anchorage, along with other local, state, and federal agencies throughout Alaska, will host the 20(th) International Epidemiological Association's (IEA) World Congress of Epidemiology (WCE 2014). The theme for this Congress is "Global Epidemiology in a Changing Environment: The Circumpolar Perspective." The changing environment includes the full range of environments that shape population health and health inequities from the physical to the social and economic. Our circumpolar perspective on these environments includes views on how political systems, work, immigration, Indigenous status, and gender relations and sexuality affect the global world and the health of its people. Suggestions and insights from the 3(rd) North American Congress of Epidemiology (2011) and the first-ever joint regional workshop co-organized by the IEA North American Region and the IEA Latin American and Caribbean Region held at the 19(th) IEA World Congress of Epidemiology (2011) have helped direct the focus for WCE 2014. Since the Arctic regions are feeling the effects of climate change first, we believe focusing on the emerging data on the health impacts of climate change throughout the world will be an important topic for this Congress. This will include a broad range of more traditional epidemiology areas such as infectious disease epidemiology, environmental epidemiology, health disparities, and surveillance and emergency preparedness. Addressing health inequities and promoting health equity is likewise a key concern of the Congress. This Congress will also host presentations on injury epidemiology, occupational health, infectious diseases, chronic diseases, maternal and child health, surveillance and field epidemiology, mental health, violence (from self-directed, e.g., suicide, to interpersonal to structural), psychoactive substance use (including tobacco), and measures of subjective health. Attention will be given to epidemiology's theoretical frameworks and emphasizing knowledge translation, from epidemiology to health systems, to policy, and to the broader public. We also plan to offer many hands-on workshops including practical uses of epidemiology to improve health systems and reduce health inequities within and between countries; the manner in which epidemiology can inform public health practice; the understanding and use of the Dictionary of Epidemiology; and many others.

Establishing nationally representative benchmarks of farm-gate nitrogen and phosphorus balances and use efficiencies on Irish farms to encourage improvements.

Agriculture faces considerable challenges of achieving more sustainable production that minimises nitrogen (N) and phosphorus (P) losses and meets international obligations for water quality and greenhouse gas emissions. This must involve reducing nutrient balance (NB) surpluses and increasing nutrient use efficiencies (NUEs), which could also improve farm profitability (a win-win). To set targets and motivate improvements in Ireland, nationally representative benchmarks were established for different farm categories (sector, soil group and production intensity). Annual farm-gate NBs (kg ha-1) and NUEs (%) for N and P were calculated for 1446 nationally representative farms from 2008 to 2015 using import and export data collected by the Teagasc National Farm Survey (part of the EU Farm Accountancy Data Network). Benchmarks for each category were established using quantile regression analysis and percentile rankings to identify farms with the lowest NB surplus per production intensity and highest gross margins (€ ha-1). Within all categories, large ranges in NBs and NUEs between benchmark farms and poorer performers show considerable room for nutrient management improvements. Results show that as agriculture intensifies, nutrient surpluses, use efficiencies and gross margins increase, but benchmark farms minimise surpluses to relatively low levels (i.e. are more sustainable). This is due to, per ha, lower fertiliser and feed imports, greater exports of agricultural products, and for dairy, sheep and suckler cattle, relatively high stocking rates. For the ambitious scenario of all non-benchmark farms reaching the optimal benchmark zone, moderate reductions in farm nutrient surpluses were found with great improvements in profitability, leading to a 31% and 9% decrease in N and P surplus nationally, predominantly from dairy and non-suckler cattle. The study also identifies excessive surpluses for each level of production intensity, which could be used by policy in setting upper limits to improve sustainability.