Multiple linear regression modelling of on-farm direct water and electricity consumption on pasture based dairy farms

peer-reviewedAn analysis into the impact of milk production, stock numbers, infrastructural equipment, managerial procedures and environmental conditions on dairy farm electricity and water consumption using multiple linear regression (MLR) modelling was carried out. Electricity and water consumption data were attained through the utilisation of a remote monitoring system installed on a study sample of 58 pasture-based, Irish commercial dairy farms between 2014 and 2016. In total, 15 and 20 dairy farm variables were analysed on their ability to predict monthly electricity and water consumption, respectively. The subsets of variables that had the greatest prediction accuracy on unseen electricity and water consumption data were selected by applying a univariate variable selection technique, all subsets regression and 10-fold cross validation. Overall, electricity consumption was more accurately predicted than water consumption with relative prediction error values of 26% and 49% for electricity and water, respectively. Milk production and the total number of dairy cows had the largest impact on electricity consumption while milk production, automatic parlour washing and whether winter building troughs were reported to be leaking had the largest impact on water consumption. A standardised regression analysis found that utilising ground water for pre-cooling milk increased electricity consumption by 0.11 standard deviations, while increasing water consumption by 0.06 standard deviations when recycled in an open loop system. Milk production had a large influence on model overprediction with large negative correlations of −0.90 and −0.82 between milk production and mean percentage error for electricity and water prediction, respectively. This suggested that overprediction was inflated when milk production was low and vice versa. Governing bodies, farmers and/or policy makers may use the developed MLR models to calculate the impact of Irish dairy farming on natural resources or as decision support tools to calculate potential impacts of on-farm mitigation practises

Assessment of Medication Adherence Barriers in COPD Patients in A Secondary Care Teaching Hospital

Background: COPD is characterised by persistent airway obstruction in which better clinical outcome can be attained by appropriate management of disease. Adherence to COPD medication is poorly understood due to chronic nature of the disease. It is crucial to identify the barriers of non-adherence to build up and execute policies and interventions to upgrade medication adherence. Objective: To identify the predisposing barriers of medication adherence and to find the association between medication adherence and variables. Methods: A descriptive analytical study was conducted and data was collected from COPD outpatients. The Morisky Medication Adherence Scale was used to measure adherence and self-assessed questionnaire was employed to identify the predictors of poor adherence. Chi square test was carried out to find the relationship between medication adherence and variables such as age, gender, literacy, socioeconomic class, polypharmacy, delivery device and climate. Results: A total of 403 patients were involved in the study where 68% reported lower adherence. The most common adherence barriers found were forgetfulness (88%), intentional stoppage of medicines when symptoms improve (83%) and negligence towards medication (82%).A significant association was found between gender, literacy, socioeconomic class, polypharmacy, delivery device and climate. Conclusion: Adherence to medication regimen in COPD patients is poor, even though it is a preventable and a treatable disease. Well-structured education, training, counseling is required to overcome medication adherence particularly among illiterate and low socioeconomic class patients. The combined interventions should be used such as video clips demonstrations of inhaler technique should be given. Keywords: COPD, Morisky medication adherence scale, Chi square test

Photospheric Signatures of Granular-scale Flux Emergence and Cancellation at the Penumbral Boundary

We studied flux emergence events of sub-granular scale in a solar active region. New Solar Telescope (NST) of Big Bear Solar Observatory made it possible to clearly observe the photospheric signature of flux emergence with very high spatial (0".11 at 7057{\AA}) and temporal (15 s) resolution. From TiO observations with the pixel scale of 0".0375, we found several elongated granule-like features (GLFs) stretching from the penumbral filaments of a sunspot at a relatively high speed of over 4 km s-1. After a slender arched darkening appeared at a tip of a penumbral filament, a bright point (BP) developed and quickly moved away from the filament forming and stretching a GLF. The size of a GLF was approximately 0.5" wide and 3" long. The moving BP encountered nearby structures after several minutes of stretching, and a well-defined elongated shape of a GLF faded away. Magnetograms from SDO/HMI and NST/IRIM revealed that those GLFs are photospheric indicators of small-scale flux emergence, and their disappearance is related to magnetic cancellation. From two well-observed events, we describe detailed development of the sub-structures of GLFs, and different cancellation processes that each of the two GLFs underwent.Comment: Accepted for publication in The Astrophysical Journa

DSSED: Decision Support System for Energy use in Dairy Production

peer-reviewedThe following report provides a comprehensive description of the background, implementation and dissemination of project RDD/00117. This project pertains to the development of an online portal for dairy farmers whereby the users of the portal will receive comprehensive information relating to energy use, electricity costs, carbon emissions, renewable energy and potential on-farm technology investments. With the rapid expansion of the Irish dairy industry resulting from the abolition of European Union milk quotas, the importance of decision support and information for dairy farmers has become extremely important. The first phase of the project involved monitoring the energy usage of 58 Irish dairy farms, and the subsequent development of a large database pertaining to energy usage on Irish dairy farms. In order to provide a detailed breakdown of energy use, electricity costs and carbon emissions on Irish dairy farms, a wide-ranging statistical analysis was carried out. The results of this analysis are available to farmers as part of the aforementioned portal, with a breakdown of mean energy consumption, cost and carbon emissions presented according to each energy consuming process on Irish dairy farms, as well as monthly trends relative to cow number and milk production. This statistical analysis is constantly updated due to continuous monitoring of 20 Irish dairy farms, with a dynamic information loop in operation between the farms in question and the statistical database. The second phase of the project involved the development of a dairy farm technology calculator which was included as part of the online portal. This tool provides a means for dairy farmers to input details of their current farm and calculates how investment in renewable and energy efficient technologies will affect their farm from economic, energy and environmental points of view. The technologies which may be analysed are plate coolers, variable speed drives (VSDs), heat recovery systems, solar water heating systems, solar photovoltaic (PV) systems and wind turbines. In addition, the technology calculator may be used as a tool for informing policy relating to incentivising the purchase of these technologies. It is anticipated that the online portal developed as part of this project will be used extensively in the future to assist farmers in making informed decisions pertaining to dairy farm energy, costs and carbon emissions. It can also be used by state bodies to aid them in policy related decisions

Variable response to phosphorus mitigation measures across the nutrient transfer continuum in a dairy grassland catchment

peer-reviewedPhosphorus (P) loss from soils to water can be a major pressure on freshwater quality and dairy farming, with higher animal stocking rates, may lead to potentially greater nutrient source pressures. In many countries with intensive agriculture, regulation of P management aims to minimise these losses. This study examined the P transfer continuum, from source to impact, in a dairy-dominated, highly stocked, grassland catchment with free-draining soils over three years. The aim was to measure the effects of P source management and regulation on P transfer across the nutrient transfer continuum and subsequent water quality and agro-economic impacts. Reduced P source pressure was indicated by: (a) lower average farm-gate P balances (2.4 kg ha−1 yr−1), higher P use efficiencies (89%) and lower inorganic fertilizer P use (5.2 kg ha−1 yr−1) relative to previous studies; (b) almost no recorded P application during the winter closed period, when applications were prohibited, to avoid incidental transfers; and (c) decreased proportions of soils with excessive P concentrations (32–24%). Concurrently, production and profitability remained comparable with the top 10% of dairy farmers nationally with milk outputs of 14,585 l ha−1, and gross margins of € 3130 ha−1. Whilst there was some indication of a response in P delivery in surface water with declines in quick flow and interflow pathway P concentrations during the winter closed period for P application, delayed baseflows in the wetter third year resulted in elevated P concentrations for long durations and there were no clear trends of improving stream biological quality. This suggests a variable response to policy measures between P source pressure and delivery/impact where the strength of any observable trend is greater closer to the source end of the nutrient transfer continuum and a time lag occurs at the other end. Policy monitoring and assessment efforts will need to be cognisant of this

Increased water vapour lifetime due to global warming

Water vapour in the atmosphere is the source of a major climate feedback mechanism and potential increases in the availability of water vapour could have important consequences for mean and extreme precipitation. Future precipitation changes further depend on how the hydrological cycle responds to drivers of climate change, such as greenhouse gases and aerosols. Currently, neither the total anthropogenic influence on the hydrological cycle, nor those from individual drivers, are constrained sufficiently to make solid projections. We investigate how integrated water vapour (IWV) responds to different drivers of climate change. Results from 11 global climate models have been used, based on simulations where CO2, methane, solar irradiance, black carbon (BC), and sulphate have been perturbed separately. While the global-mean IWV is usually assumed to increase by ~7% per degree K surface temperature change, we find that the feedback response of IWV differs somewhat between drivers. Fast responses, which include the initial radiative effect and rapid adjustments to an external forcing, amplify these differences. The resulting net changes in IWV range from 6.4±0.9%/K for sulphate to 9.8±2%/K for BC. We further calculate the relationship between global changes in IWV and precipitation, which can be characterized by quantifying changes in atmospheric water vapour lifetime. Global climate models simulate a substantial increase in the lifetime, from 8.2±0.5 to 9.9±0.7 days between 1986-2005 and 2081-2100 under a high emission scenario, and we discuss to what extent the water vapour lifetime provides additional information compared to analysis of IWV and precipitation separately. We conclude that water vapour lifetime changes are an important indicator of changes in precipitation patterns and that BC is particularly efficient in prolonging the distance between evaporation and precipitation

Prodigal: prokaryotic gene recognition and translation initiation site identification

<p>Abstract</p> <p>Background</p> <p>The quality of automated gene prediction in microbial organisms has improved steadily over the past decade, but there is still room for improvement. Increasing the number of correct identifications, both of genes and of the translation initiation sites for each gene, and reducing the overall number of false positives, are all desirable goals.</p> <p>Results</p> <p>With our years of experience in manually curating genomes for the Joint Genome Institute, we developed a new gene prediction algorithm called Prodigal (PROkaryotic DYnamic programming Gene-finding ALgorithm). With Prodigal, we focused specifically on the three goals of improved gene structure prediction, improved translation initiation site recognition, and reduced false positives. We compared the results of Prodigal to existing gene-finding methods to demonstrate that it met each of these objectives.</p> <p>Conclusion</p> <p>We built a fast, lightweight, open source gene prediction program called Prodigal <url>http://compbio.ornl.gov/prodigal/</url>. Prodigal achieved good results compared to existing methods, and we believe it will be a valuable asset to automated microbial annotation pipelines.</p