Integrated Arable Farming Systems and their potential uptake in the UK

Integrated Arable Farming Systems are examined from the perspective of the farmer considering the use of such techniques, and data are presented which suggest that the uptake of the approach may expose the manager to a greater degree of risk. Observations are made about the possible uptake of such systems in the UK and the implications this may have for agricultural and environmental policy in general

Sanitizing the fortress: protection of ant brood and nest material by worker antibiotics

Social groups are at particular risk for parasite infection, which is heightened in eusocial insects by the low genetic diversity of individuals within a colony. To combat this, adult ants have evolved a suite of defenses to protect each other, including the production of antimicrobial secretions. However, it is the brood in a colony that are most vulnerable to parasites because their individual defenses are limited, and the nest material in which ants live is also likely to be prone to colonization by potential parasites. Here, we investigate in two ant species whether adult workers use their antimicrobial secretions not only to protect each other but also to sanitize the vulnerable brood and nest material. We find that, in both leaf-cutting ants and weaver ants, the survival of the brood was reduced and the sporulation of parasitic fungi from them increased, when the workers nursing them lacked functional antimicrobial-producing glands. This was the case for both larvae that were experimentally treated with a fungal parasite (Metarhizium) and control larvae which developed infections of an opportunistic fungal parasite (Aspergillus). Similarly, fungi were more likely to grow on the nest material of both ant species if the glands of attending workers were blocked. The results show that the defense of brood and sanitization of nest material are important functions of the antimicrobial secretions of adult ants and that ubiquitous, opportunistic fungi may be a more important driver of the evolution of these defenses than rarer, specialist parasites

Heat stress in dairy cattle – a review, and some of the potential risks associated with the nutritional management of this condition

Heat stress occurs when animals are exposed to environmental temperatures in excess of 25°C (the upper critical temperature), particularly in combination with high relative humidity or sunshine. High humidity makes the sweating mechanism relatively ineffective, thereby making cattle unable to maintain their core body temperature. Affected cows attempt to reduce heat load by reducing exercise, feed intake and lactation. They actively seek shade and wet areas. As their body temperature rises animals become agitated and distressed, have laboured open-mouth breathing and eventually collapse, convulse and die. Heat stress that is not life-threatening leads to reduced milk production and impaired reproductive performance, and may predispose amongst others to subclinical acidosis. Treatment of severely affected animals is by cooling with cold water and/or fans. Prevention is by providing good-quality drinking water and shade (natural or artificial), and the use of water sprinklers and/or fans. Changes to the diet (i.e. high energy density and low protein) are also beneficial and often implemented. However, there may be some potential risks associated with the nutritional management of heat stress in dairy cattle; i.e. the animals are at increased risk of developing subacute rumen acidosis, with ensuing laminitis/lameness, and displaced abomasum. The first part of this paper provides a brief review of heat stress in dairy cattle. The second part discusses how increasing the energy density of the diet (i.e. increasing the grain/forage ratio), as part of the nutritional management of heat stress, may put the cows at greater risk of the above mentioned digestive disorders

Ordering of Trotterization: impact on errors in quantum simulation of electronic structure

Trotter–Suzuki decompositions are frequently used in the quantum simulation of quantum chemistry. They transform the evolution operator into a form implementable on a quantum device, while incurring an error—the Trotter error. The Trotter error can be made arbitrarily small by increasing the Trotter number. However, this increases the length of the quantum circuits required, which may be impractical. It is therefore desirable to find methods of reducing the Trotter error through alternate means. The Trotter error is dependent on the order in which individual term unitaries are applied. Due to the factorial growth in the number of possible orderings with respect to the number of terms, finding an optimal strategy for ordering Trotter sequences is difficult. In this paper, we propose three ordering strategies, and assess their impact on the Trotter error incurred. Initially, we exhaustively examine the possible orderings for molecular hydrogen in a STO-3G basis. We demonstrate how the optimal ordering scheme depends on the compatibility graph of the Hamiltonian, and show how it varies with increasing bond length. We then use 44 molecular Hamiltonians to evaluate two strategies based on coloring their incompatibility graphs, while considering the properties of the obtained colorings. We find that the Trotter error for most for systems involving heavy atoms, using a reference magnitude ordering, is less than 1 kcal/mol. Relative to this, the difference between ordering schemes can be substantial, being approximately on the order of millihartrees. The coloring-based ordering schemes are reasonably promising—particularly for systems involving heavy atoms—however further work is required to increase dependence on the magnitude of terms. Finally, we consider ordering strategies based on the norm of the Trotter error operator, including an iterative method for generating the new error operator terms added upon insertion of a term into an ordered Hamiltonian

Farmers’ attitudes to disease risk management in England: a comparative analysis of sheep and pig farmers

The UK Department for Environment, Food and Rural Affairs (Defra) identified practices to reduce the risk of animal disease outbreaks. We report on the response of sheep and pig farmers in England to promotion of these practices. A conceptual framework was established from research on factors influencing adoption of animal health practices, linking knowledge, attitudes, social influences and perceived constraints to the implementation of specific practices. Qualitative data were collected from nine sheep and six pig enterprises in 2011. Thematic analysis explored attitudes and responses to the proposed practices, and factors influencing the likelihood of implementation. Most feel they are doing all they can reasonably do to minimise disease risk and that practices not being implemented are either not relevant or ineffective. There is little awareness and concern about risk from unseen threats. Pig farmers place more emphasis than sheep farmers on controlling wildlife, staff and visitor management and staff training. The main factors that influence livestock farmers’ decision on whether or not to implement a specific disease risk measure are: attitudes to, and perceptions of, disease risk; attitudes towards the specific measure and its efficacy; characteristics of the enterprise which they perceive as making a measure impractical; previous experience of a disease or of the measure; and the credibility of information and advice. Great importance is placed on access to authoritative information with most seeing vets as the prime source to interpret generic advice from national bodies in the local context. Uptake of disease risk measures could be increased by: improved risk communication through the farming press and vets to encourage farmers to recognise hidden threats; dissemination of credible early warning information to sharpen farmers’ assessment of risk; and targeted information through training events, farming press, vets and other advisers, and farmer groups, tailored to the different categories of livestock farmer

A review of the financial impact of production diseases in poultry production systems

Whilst the academic literature widely asserts that production diseases have a significant financial impact on poultry production, these claims are rarely supported by empirical 17 evidence. There is a risk, therefore, that the information needs of poultry producers regarding the costs associated with particular diseases are not being adequately met. A systematic literature review of poultry production diseases was undertaken, first to scope the availability of studies that estimate the financial impacts of production diseases on poultry systems and second, based on these studies, estimates were generated of the magnitude of these impacts. Nine production diseases, selected by a panel of stakeholders as being economically important in the EU, were examined. The review found that the poultry disease literature has primarily an epidemiological focus, with very few publications providing estimates of the financial impacts of diseases. However, some publications quantified the physical impacts of production diseases and control interventions, e.g. using measures such as output volumes, mortality rates, bacteria counts, etc. Using these data in standard financial models, partial financial analyses were possible for some poultry production diseases. Coccidiosis and clostridiosis were found to be the most common production diseases in broiler flocks, with salpingoperitonitis the most common in layers. While the financial impact of untreated diseases varied, most uncontrolled diseases were estimated to make flocks loss-making. However, in all cases, interventions were available that signficantly reduced these losses. The review reinforces the concern that the available literature is not providing sufficient information for poultry producers to decide on financially-optimal disease prevention and treatment measures

Fault diagnosis of PEMFC based on the AC voltage response and 1D convolutional neural network

Real-time diagnosis is required to ensure the safety, reliability, and durability of the polymer electrolyte membrane fuel cell (PEMFC) system. Two categories of methods are (1) intrusive, time consuming, or require alterations to the cell architecture but provide detailed information about the system or (2) rapid and benign but low-information-yielding. A strategy based on alternating current (AC) voltage response and one-dimensional (1D) convolutional neural network (CNN) is proposed as a methodology for detailed and rapid fuel cell diagnosis. AC voltage response signals contain within them the convoluted information that is also available via electrochemical impedance spectroscopy (EIS), such as capacitive, inductive, and diffusion processes, and direct use of time-domain signals can avoid time-frequency conversion. It also overcomes the disadvantage that EIS can only be measured under steady-state conditions. The utilization of multi-frequency excitation can make the proposed approach an ideal real-time diagnostic/characterization tool for fuel cells and other electrochemical power systems

Factors affecting dairy farmers' attitudes towards antimicrobial medicine usage in cattle in England and Wales

There has been growing concern about bacterial resistance to antimicrobials in the farmed livestock sector. Attention has turned to sub-optimal use of antimicrobials as a driver of resistance. Recent reviews have identified a lack of data on the pattern of antimicrobial use as an impediment to the design of measures to tackle this growing problem. This paper reports on a study that explored use of antibiotics by dairy farmers and factors influencing their decision-making around this usage. We found that respondents had either recently reduced their use of antibiotics, or planned to do so. Advice from their veterinarian was instrumental in this. Over 70% thought reducing antibiotic usage would be a good thing to do. The most influential source of information used was their own veterinarian. Some 50% were unaware of the available guidelines on use in cattle production. However, 97% thought it important to keep treatment records. The Theory of Planned Behaviour was used to identify dairy farmers’ drivers and barriers to reduce use of antibiotics. Intention to reduce usage was weakly correlated with current and past practice of antibiotic use, whilst the strongest driver was respondents’ belief that their social and advisory network would approve of them doing this. The higher the proportion of income from milk production and the greater the chance of remaining in milk production, the significantly higher the likelihood of farmers exhibiting positive intention to reduce antibiotic usage. Such farmers may be more commercially minded than others and thus more cost-conscious or, perhaps, more aware of possible future restrictions. Strong correlation was found between farmers’ perception of their social referents’ beliefs and farmers’ intent to reduce antibiotic use. Policy makers should target these social referents, especially veterinarians, with information on the benefits from, and the means to, achieving reductions in antibiotic usage. Information on sub-optimal use of antibiotics as a driver of resistance in dairy herds and in humans along with advice on best farm practice to minimise risk of disease and ensure animal welfare, complemented with data on potential cost savings from reduced antibiotic use would help improve poor practice

Improved i-Vector Representation for Speaker Diarization

This paper proposes using a previously well-trained deep neural network (DNN) to enhance the i-vector representation used for speaker diarization. In effect, we replace the Gaussian Mixture Model (GMM) typically used to train a Universal Background Model (UBM), with a DNN that has been trained using a different large scale dataset. To train the T-matrix we use a supervised UBM obtained from the DNN using filterbank input features to calculate the posterior information, and then MFCC features to train the UBM instead of a traditional unsupervised UBM derived from single features. Next we jointly use DNN and MFCC features to calculate the zeroth and first order Baum-Welch statistics for training an extractor from which we obtain the i-vector. The system will be shown to achieve a significant improvement on the NIST 2008 speaker recognition evaluation (SRE) telephone data task compared to state-of-the-art approaches