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Since the implementation of herding in the Neolithic period, specialized areas for livestock activities have emerged in the Western Mediterranean region, such as some caves and shelters, which were systematically used for the stabling of herds. By definition, this type of sites has a great potential for the study of the beginning of livestock practices and offer high-resolution information on the exploitation and management of herds. Different disciplines analyze these activities through the analysis of faunal, archaeobotanical or sedimentological remains from these sites. In this paper, we offer a state of the art on the contribution to the study of early husbandry practices of zooarchaeological studies of faunal assemblages from sheepfold caves. [ABSTRACT FROM AUTHOR]

Many dairy cattle worldwide are housed in tiestalls, meaning that they are tethered by the neck to individual stalls. On some farms, tied cattle are permitted seasonal access to pasture, but otherwise their movements are restricted compared with cows housed in freestall barns or other loose housing systems. The aim of this systematic review is to summarize the scientific literature pertaining the welfare of tied dairy cattle through comparison with less-restrictive housing systems. Articles identified by PubMed and Web of Science underwent a 5-phase screening process, resulting in the inclusion of 102 papers. These papers addressed measures of welfare related to affective state, natural behavior, and health (with the lattermost category subdivided into hoof and leg disorders, lameness, mastitis, transition disease, and other diseases or conditions). Health was the most researched topic (discussed in 86% of articles); only 19% and 14% of studies addressed natural behavior and affective state, respectively. Our review highlights different health benefits for tethered and loose cattle. For example, tied cattle experience reduced prevalence of white line disease and digital dermatitis, whereas loose cattle experience fewer leg lesions and injuries. The prevalence of mastitis, transition diseases, and other conditions did not differ consistently across housing types. We found that the expression of certain natural behaviors, particularly those associated with lying down (e.g., time spent kneeling, unfulfilled intentions to lie down), were impaired in tiestalls. Articles addressing affective state found benefits to loose housing, but these studies focused almost exclusively on (1) physiological measurements and (2) cow comfort, a concept that lacks a consistent operational definition across studies. We call for future research into the affective state of tied cattle that extends beyond these explorations and employs more sophisticated methodologies. [ABSTRACT FROM AUTHOR]

A dairy farmer's decision to cull or keep dairy cows is likely a complex decision based on animal health and farm management practices. The present paper investigated the relationship between cow longevity and animal health, and between longevity and farm investments, while controlling for farm-specific characteristics and animal management practices, by using Swedish dairy farm and production data for the period 2009 to 2018. We used the ordinary least square and unconditional quantile regression model to perform mean-based and heterogeneous-based analysis, respectively. Findings from the study indicate that, on average, animal health has a negative but insignificant effect on dairy herd longevity. This implies that culling is predominantly done for other reasons than poor health status. Investment in farm infrastructure has a positive and significant effect on dairy herd longevity. The investment in farm infrastructure creates room for new or superior recruitment heifers without the need to cull existing dairy cows. Production variables that prolong dairy cow longevity include higher milk yield and an extended calving interval. Findings from this study imply that the relatively short longevity of dairy cows in Sweden compared with some dairy producing countries is not a result of problems with health and welfare. Rather, dairy cow longevity in Sweden hinges on the farmers' investment decisions, farm-specific characteristics and animal management practices. [ABSTRACT FROM AUTHOR]

Bovine viral diarrhea virus (BVDV) is endemic in many parts of the world, and multiple countries have implemented surveillance activities for disease control or eradication. In such control programs, the disease-free status can be compromised by factors that pose risks for introduction or persistence of the virus. The aim of the present study was to gain a comprehensive overview of possible risk factors for BVDV infection in cattle herds in Europe and to assess their importance. Papers that considered risk factors for BVDV infection in cattle were identified through a systematic search. Further selection of papers eligible for quantitative analysis was performed using a predefined checklist, including (1) appropriate region (i.e., studies performed in Europe), (2) representativeness of the study population, (3) quality of statistical analysis, and (4) availability of sufficient quantitative data. In total, 18 observational studies were selected. Data were analyzed by a random-effects meta-analysis to obtain pooled estimates of the odds of BVDV infection. Meta-analyses were performed on 6 risk factors: herd type, herd size, participation in shows or markets, introduction of cattle, grazing, and contact with other cattle herds on pasture. Significant higher odds were found for dairy herds (odds ratio, OR = 1.63, 95% confidence interval, CI: 1.06–2.50) compared with beef herds, for larger herds (OR = 1.04 for every 10 extra animals in the herd, 95% CI: 1.02–1.06), for herds that participate in shows or markets (OR = 1.45, 95% CI: 1.10–1.91), for herds that introduced cattle into the herd (OR = 1.41, 95% CI: 1.18–1.69), and for herds that share pasture or have direct contact with cattle of other herds at pasture (OR = 1.32, 95% CI: 1.07–1.63). These pooled values must be interpreted with care, as there was a high level of heterogeneity between studies. However, they do give an indication of the importance of the most frequently studied risk factors and can therefore assist in the development, evaluation, and optimization of BVDV control programs. [ABSTRACT FROM AUTHOR]

• Lactose tolerance and the co-spreading of LCT/MCM6 and the high-risk MS allele, HLA-DRB1*15:01, throughout Europe. • Exposure to dairy-animal pathogen, Clostridium perfringens Epsilon neurotoxin (ETX) • Environmental trigger for multiple sclerosis (MS) On January 10th, 2024, Barrie et al. published a landmark paper identifying the ancestral European origin of the high-risk, multiple sclerosis (MS) allele, HLA-DRB1*15:01. The authors hypothesize that Bronze Age, Yamnaya migration to, and their population of Scandinavia and the British Isles, accounts for the high incidence of MS in these regions, stemming from the geographical spread of HLA-DRB1*15:01 as evidenced by their data. These data further indicate that this immune-related allele likely underwent positive-selection pressures, suggesting that HLA-DRB1*15:01 may have conferred increased immunological resistance to zoonotic pathogens that the Yamnaya would have been exposed to from a lifestyle of sheep, goat, and cattle herding, as well as from the prevalent consumption of dairy products harvested from these animals. We wish to expand the scope of the Barrie et al. hypothesis to include the other gene that emerged in their extensive genomic analysis; the lactase persistence allele, LCT/MCM6, which allows adults to consume milk beyond childhood without adverse physical effects. We propose that, in addition to the Yamnaya spread of HLA-DRB1*15:01, their spread of LCT/MCM6 engendered a cultural propensity for milk consumption that may play a significant role in the present-day, global distribution of MS. Specifically, we hypothesize that lactose tolerance, and the prominent dairy consumption it enables, may expose individuals to the enteric, dairy-animal pathogen, Clostridium perfringens type B/D and its Epsilon neurotoxin that targets the precise brain tissues damaged during each MS relapse. [ABSTRACT FROM AUTHOR]

The dairy industry requires substantial energy resources at all stages of production and supply to meet consumer needs in terms of quantity, quality and food safety. The expected future climate change effects will cause serious uncertainty to the dairy industry. Adapting to these upcoming conditions is a challenge and one that is compounded by the continuous increase in food demand, as a result of global population growth. Predictably, under current conditions, this situation might lead to a significant increase in the energy requirements of the dairy industry. Therefore, there is a clear need to mitigate energy use through enhanced energy conservation, waste reduction and waste management. This review paper presents and discusses alternative dairy operations and mitigation strategies that have the potential to lead the dairy industry towards net-zero carbon emissions. Further, the focus of this work turns to supply chain energy modelling (SCEM) as means to mitigate energy use, while relevant work in the literature is reviewed. Supply chain energy models can provide a complete overview of the energy demand and the energy mix of a dairy supply chain. Additionally, they can highlight the most energy consuming processes and allow the evaluation of alternative energy-saving operations that can lead towards the net-zero carbon target. Overall, the development or use of computational tools for simulating the energy demand in the industry has strong potential for improving sustainability across the dairy supply chain. • Insight on climate change effects on the dairy supply chain and impact on energy demand. • Understanding the relevance and importance of the "net-zero" carbon emissions target in the dairy supply chain. • Identification of novel energy mitigation strategies across the dairy supply chain. • Importance of developing supply chain energy models (SCEM) to address energy mitigation in the dairy sector. • Evaluation of modelling techniques to develop or deliver supply chain energy models. [ABSTRACT FROM AUTHOR]

• A spatial dynamic linear model for modeling of pigs' drinking patterns is evaluated. • The model relies on simultaneously monitored water consumption from multiple pens. • Seven model versions are tested with three lengths of time-windows. • The ability to detect events in specific pens and specific sections is tested. • Events are detected in specific pens and sections with high AUC (>0.80). The objectives of this paper are to evaluate the detection performance of a previously developed multivariate spatial dynamic linear model (DLM), which aim to predict outbreaks of either diarrhea or pen fouling amongst growing pigs, and to discuss potential post processing strategies for reducing alarms. The model is applied to sensor based water data from a commercial herd of finisher pigs (30–110 kg) and a research facility herd of weaner pigs (7–30 kg). Performance evaluation is conducted by applying a standardized two-sided Cusum , on the forecast errors generated by the spatial model. For each herd, forecast errors are generated at three spatial levels: Pen level, section level, and herd level. Seven model versions express different temporal correlations in the drinking patterns between pens and sections in a herd, and the performances of each spatial level are evaluated for every model version. The alarms generated by the Cusum are categorized as true positive (TP), false positive (FP), true negative (TN), or false negative (FN) based on time windows of three different lengths. In total, 126 combinations of herds, spatial levels, model versions, and time windows are evaluated, and the performance of each combination is reported as the area under the ROC curve (AUC). The highest performances are obtained at herd level given the longest time window and strongest temporal correlation (AUC = 0.98 (weaners) and 0.94 (finishers)). However, the settings most suitable for implementation in commercial herds, are obtained at section level given the medium-length time window and strongest temporal correlation (AUC = 0.86 (weaners) and 0.87 (finishers)). The combination of a spatial DLM and a two-sided tabular Cusum has high potential for prioritizing high-risk alarms as well as for merging alarms from multiple pens within the same section into a reduced number of alarms communicated to the caretaker. Thus, the spatial detection system described here, and in a previous paper, constitute a new and promising approach to sensor based monitoring tools in livestock production. [ABSTRACT FROM AUTHOR]

• A spatial dynamic linear model for modeling of pigs' drinking patterns is developed. • The model relies on simultaneously monitored water consumption from multiple pens. • Seven different correlation structures between drinking patterns are defined. • A weaner herd and a finisher herd are modeled separately. • Model fit (MSE) indicate drinking patterns are correlated. The overall objective of this paper is to present the development of a spatial multivariate dynamic linear model (DLM) modeling the water consumption of growing pigs throughout the entire growth periods. The water consumption from multiple pens in multiple sections are monitored simultaneously by flow meters in both a commercial herd of finisher pigs (30–110 kg) and a research facility herd of weaner pigs (7–30 kg). The diurnal drinking patterns are modeled by a multivariate DLM, which is superpositioned by four sub-models describing three harmonic waves and a growth trend. The overall hypothesis of this paper is that pens and sections in a herd of growing pigs are correlated, and that this correlation can be modeled using model parameters defined at different spatial levels. Therefore seven model versions are defined to reflect a variety of temporal correlation structures between the monitored drinking patterns. The model versions were trained on learning data of the two herds, and run on separate test data sets from the herds. Their ability to fit the test data is measured as mean square error (MSE). Results for the finisher herd indicate that drinking patterns from pens within the same section are correlated (MSE = 13.850). For the weaner herd, results indicate an inverse relation between the degree of correlation and the model fit. Thus, the best fit (MSE = 1.446) is found for the model version expressing least correlation in data from pens across the herd. [ABSTRACT FROM AUTHOR]

The abolition of the EU milk quota system since 2015 has allowed the dairy sector to fully react to market forces. This change should be properly reflected within the tools that support the design of EU/national policy interventions. This paper focuses on updating the milk supply responses at EU member state level in a context where still limited data is available. Using a Mixed Estimator a set of equations for the yield per cow and the size of the dairy herd, has been estimated, leaving the milk supply derived as an identity. An important outcome of this study is that milk supply at country level is inelastic, with the (short-run) yield and herd milk price elasticities being 0.2 and 0.1 respectively. The study concludes that two thirds of the impact of a milk price change is resulting from dairy cow yield changes, while a third is resulting from changes in the number of dairy cows. [ABSTRACT FROM AUTHOR]

The High Energy Cosmic Radiation Detection (HERD) facility will be installed on the China space station, and the critical sub-detector is the homogeneous, isotropic, and finely segmented 3D calorimeter (CALO), composed of 7500 LYSO cubes. In this paper, GEANT4 was used to simulate the optical photon propagation inside a single LYSO crystal including the Wavelength Shifting Fiber (WLSF) and photodiode (PD) and compared with experimental results. The results show that only a tiny fraction of the photons produced in the LYSO can be collected through the WLSF and PD, and the light output can be enhanced by increasing the number of rough surfaces. Additionally, the variability in WLSF and PD light output when particles entered the LYSO crystal from varying positions, known as non-uniformity, was examined through simulation. The simulated non-uniformity using thin X-ray beams is compatible within the experimental errors and can be improved at higher light output. Finally, the non-uniformity is highly sensitive to the polishing condition of the crystal surface, and better polishing conditions are preferred. [ABSTRACT FROM AUTHOR]

The increasing use of the payment for ecosystem services (PES) represents a prominent institutional shock triggered by external interventions on livelihoods of natural resource managers. To reconcile the needs for both conservation and development, a deep understanding on the influence mechanisms of PES on farmer livelihood resilience is paramount, particularly in dynamic and uncertain contexts. Taking the Rangeland Ecological Protection Subsidy and Reward scheme that has been implemented in pastoral China since 2011 as a case study, the paper empirically investigates the impacts of PES on livelihood resilience of livestock farmers in Qinghai Province. Based on a comprehensive evaluation of herder livelihood resilience from three dimensions—buffering, self-organizing and learning capacities, multiple linear regression was applied to examine the PES impacts. Resilience enhancement was mainly found in buffering capacity for herders with a medium to large herd size. Results of univariate parallel mediation models further reveal that household income growth and diversification and livestock upsizing were three key intermediary processes that mediated the enhancement and together contributed 28.83% of the total effect. The findings help narrow a knowledge gap in understanding whether, how and for whom PES contributes to improve farmer livelihood resilience. To achieve "win-win" outcomes, we stress that greater attention should be given to tailoring design and implementation of PES programs to align with the specificity of local contexts. • Impacts of one rangeland PES program on herder livelihood resilience are examined. • Ecological compensation has significantly enhanced the livelihood resilience of herders. • Buffering capacity is enhanced more than self-organizing and learning capacity. • Income growth and diversification as well as herd upsizing are three crucial mediators. • Larger-scale herders are improved stronger than smaller-scale ones. [ABSTRACT FROM AUTHOR]

This article focuses on optimizing electric vehicle charging in distribution networks, emphasizing technical and economic considerations. Unlike traditional methods, the proposed intelligent approach tailors each EV's charging based on specific daily trip energy requirements. Vehicle owners provide trip data to the charge management system, enabling precise charging calculations considering factors such as energy tariffs, distribution network limits, and charging levels. The paper introduces the horse herd optimization algorithm, inspired by horse herd behavior, offering advantages like reduced computational time and improved convergence in maximizing power generation, especially under shading conditions. The comparative analysis of smart EV charging under normal and fast conditions, considering various constraints and load response programs, demonstrates the proposed method's effectiveness. Numerical results reveal a 46.02 % average load reduction and a 20.53 % peak load decrease with a Load Response Program. Charging costs are optimized, with Case 2 exhibiting a 2.61 % cost reduction compared to Case 3. The study delves into charging frequencies, discharge frequencies, total unsupplied energy, and unloaded energy for each case, providing crucial insights into algorithmic performance. The horse herd optimization algorithm-based approach proves superior, offering a promising solution for efficient and cost-effective electric vehicle charging in distribution networks. Furthermore, graphical representations illustrate the algorithm's impact on charging power, energy allocation, power passing through distribution posts, and megavolt-ampere flow through network lines. These numerical and graphical analyses provide a comprehensive understanding of the horse herd optimization algorithm capabilities, emphasizing its potential to optimize power distribution, reduce costs, and enhance the resilience of residential distribution networks. • Prioritizes EV charging based on daily travel and battery levels using discrete charge rates. • Compares normal and fast charging rates, considering transformer overloads. • Adapts network and load model from real data, balancing subscriber and operator interests. • Assesses method's impact with and without a load response program for adaptability. • Introduces the Horse Herd Optimization Algorithm (HOA) inspired by horse herd behavior. [ABSTRACT FROM AUTHOR]

Despite a long history of Improved Cookstove (ICS) interventions by Non-Governmental Organizations, International Development partners and the Government of Nepal, the majority of rural Nepalese people cook on a traditional open fire for their large-scale cooking needs due to a significant lack of approved institutional-scale cooking solutions. Whilst 65.8% of rural Nepalese households cook with biomass as their primary fuel source to satisfy their personal energy needs, there is no information collected on institutional cooking use by the Government of Nepal. In this paper our main objective was to design, implement and evaluate a novel Institutional Improved Cookstove (IICS) to satisfy this gap and following its manufacturing and testing in a Government of Nepal approved test center, to identify the complex contextual factors that often override the technical capabilities of IICS. Our three-phase method combined qualitative and quantitative research approaches, as well as north-south collaborations involving a transdisciplinary research team to create an integrated systems approach taking into account the voices of all key energy stakeholders. Phase 1 included UK based co-design and testing at the University of Nottingham in 2017 to develop a novel IICS that could be used in rural Nepal. Phase 2 involved adapting the design to accommodate contextual factors highlighted by Nepalese partners and to meet testing requirements at a Government of Nepal approved testing center in late 2017. Phase 3 was conducted between December 2017 and April 2020 and focused on piloting the novel IICS in a range of locations, altitudes, socio-economic and cultural settings, monitoring sustained use and obtaining user feedback. We present our results through three case studies that highlight the highly contextualized nature of IICS adoption and sustained use, the importance of stacking, usability and cost savings, and a number of pathways to scale in an institutional setting. • Focus on institutional scale improved cookstoves. • Co-creation of cooking technology with UK and Nepali Engineers. • Present a novel Institutional scale Natural Draft Top-Lit Up-Draft (TLUD) Gasifier for use in off-grid rural settings. • 36 month long Small-Scale Study identifying complex contextual factors that act as barriers to adoption and sustained use. [ABSTRACT FROM AUTHOR]

Pastoral and agro-pastoral (PAP) systems in East Africa face a range of challenges including increased climate variability. Various measures have been proposed to improve the resilience of pastoral/agro-pastoral (PAP) systems to drought. However, identifying the most effective measure for a given system and location is complicated, and tools are required to appraise measures on a consistent basis. This paper develops a model of a PAP system and uses it to assess the effects of four measures (Index-based livestock insurance, IBLI; Commercial destocking with an early warning system, EWS; Rangeland restoration, RR; Fodder planting, FP) on the resilience of the PAP system. It also quantifies the greenhouse gas (GHG) effects of the measures, thereby identifying potential trade-offs and synergies between the policy objectives of resilience and climate smart agriculture (CSA). A dynamic model of the Borena pastoral cattle system was developed to undertake the analysis. At its core is a herd model that calculates the changes in cattle population over time. Feed availability and drought occurrence affect fertility and mortality rates, which in turn determine the population and (meat and milk) production. A suite of indicators covering the three dimensions of CSA (increasing productivity, enhancing resilience and reducing GHG emissions) were developed, and used to compare the situation with and without measures. Destocking with an early warning system provides the biggest increases (relative to the no measure situation) in production and profit, due to the way it changes the herd size and structure. It maintains a larger herd than any of the other measures, and a greater proportion of the herd are adult females. Fodder planting and rangeland restoration provide moderate increases in production and profit. Index-based livestock insurance provides a moderate increase in protein production, but has no effect on profit, as it is designed to reduce risk rather than increase productivity or profit, at least in the short term. All of the measures increase the total emissions relative to the no measure scenario. In terms of the three dimensions of climate-smart agriculture, IBLI leads to some improvements in productivity and resilience but leads to large increases in total emissions, and modest increases in emissions intensity (EI). EWS leads to large increases in productivity and resilience. However, it also leads to large increases in total emissions and a mixed effect on EI. FP and RR improve productivity and increase total emissions, while having little effect on EI or resilience. This paper illustrates the way in which systems dynamic model can be used to appraise measures designed to improve resilience. The result identify potential synergies and tensions between the goals of resilience and climate smart agriculture, and raises the question of whether fully climate-smart goals are viable in these systems. [Display omitted] • A model of a pastoral cattle system in Ethiopia was developed and used to investigate resilience. • Four measures were analysed: livestock insurance; managed destocking; rangeland restoration; fodder planting. • Destocking provides the biggest increase in production and profit, due to the way it changes the herd size and structure. • Fodder planting and rangeland restoration increase production and profit. Insurance increases production but not profit. • All of the measures increase the total GHG emissions but lead to little change in emissions intensity. [ABSTRACT FROM AUTHOR]

Scientific journals and popular press magazines are littered with articles in which the authors use data from dairy herd management software. Almost none of such papers include data cleaning and data quality assessment in their study design despite this being a very critical step during data mining. This paper presents 2 novel data cleaning methods that permit identification of animals with good and bad data quality. The first method is a deterministic or rule-based data cleaning method. Reproduction and mutation or life-changing events such as birth and death were converted to a symbolic (alphabetical letter) representation and split into triplets (3-letter code). The triplets were manually labeled as physiologically correct, suspicious, or impossible. The deterministic data cleaning method was applied to assess the quality of data stored in dairy herd management from 26 farms enrolled in the herd health management program from the Faculty of Veterinary Medicine Ghent University, Belgium. In total, 150,443 triplets were created, 65.4% were labeled as correct, 17.4% as suspicious, and 17.2% as impossible. The second method, a probabilistic method, uses a machine learning algorithm (random forests) to predict the correctness of fertility and mutation events in an early stage of data cleaning. The prediction accuracy of the random forests algorithm was compared with a classical linear statistical method (penalized logistic regression), outperforming the latter substantially, with a superior receiver operating characteristic curve and a higher accuracy (89 vs. 72%). From those results, we conclude that the triplet method can be used to assess the quality of reproduction data stored in dairy herd management software and that a machine learning technique such as random forests is capable of predicting the correctness of fertility data. [ABSTRACT FROM AUTHOR]

Exaggerated fear-reactions are associated with injurious flying, smothering, feather pecking and other events that compromise animal welfare in laying hens. The aim of this study was to test the hypothesis that chicks with access to litter during the first five weeks of life would be less fearful as adult hens compared to birds reared without access to litter. The hypothesis was tested in a national on-farm study in commercial aviary flocks in Norway. Five rearing farmers divided the pullets into two groups within their rearing houses. While the chicks were enclosed inside the aviary rows during the first five weeks of life, paper substrate where food and other particles could accumulate, covered the wire mesh floor in the treatment group, whereas the control group was reared on bare wire mesh. At 30 weeks of age, 23 aviary flocks (11 control flocks reared without paper and 12 treatment flocks reared with paper) were visited. During the visit, the fearfulness of the adult birds was tested in a stationary person test and a novel object test. The data was analysed by ANOVA or logistic regression as appropriate. The access to litter during rearing did not influence the number of birds that approached within 25 cm of the stationary person ( p = 0.51). All flocks, regardless of rearing treatment, had birds which came within 2 m of the stationary person. The latency to approach within 2 m of the stationary person tended to be influenced by provision of environmental enrichment as adults ( p = 0.08) and by the interaction between treatment × rearing farm ( p = 0.08). The number of birds that approached within 2 m of the stationary person was influenced by the interaction between treatment during rearing and provision of enrichment as adults ( p = 0.03), however, the post hoc test showed no pairwise differences. All flocks, regardless of rearing treatment, had birds that approached the novel object. The access to litter during rearing did not influence the birds’ latency to approach the novel object. The number of birds approaching the novel object was affected by the interaction between access to substrate during rearing and provision of environmental enrichment as adults ( p = 0.05). The results indicate that both adding paper substrate to chicks from the first day of life and environmental enrichment as adults, reduce fearfulness in laying hens. [ABSTRACT FROM AUTHOR]

In a recent NATURE paper, Nagy et al. find a well-defined hierarchy among the individuals of the pigeon flock, which may lead to a rapid decision making in the directional choice dynamics of the flock. Motivated by this interesting discovery, we present a novel analysis on the efficiency of the hierarchical topology among the leader-following systems in this paper. To this end, we first propose a measurement of the convergence rate of leader-following consensus, and then connect the convergence rates with the communication topologies of leader-following systems. It is proved that the hierarchical network organization can achieve the best performance in terms of convergence rates. It is also established that the connections between the leader and the followers have effective impacts on increasing the convergence rates. Extensive numerical results are provided to show the effectiveness of our conclusions. [ABSTRACT FROM AUTHOR]

The paper focuses on simulating an artificial life in which neural networks (recurrent (RNN) and Long Short Term Memory (LSTM) networks) control prey and predator agents. The research goal was to check whether a simple genetic algorithm evolves the LSTM based controller that competes with the classic RNN controller in the real world. We also examined the impact of audio communication within a given species on the survival of agents. Our experiments evidenced the LSTM network results were slightly worse than the RNN controller. We also showed that prey agents developed herd behavior in response to predator pressure. They learned to form herds that allowed them to resist predator attacks. It was also possible to observe the prey agent's cooperation in searching for food when the plants formed clusters. [ABSTRACT FROM AUTHOR]

In this paper a novel multilevel thresholding algorithm using a meta-heuristic Krill Herd Optimization (KHO) algorithm has been proposed for solving the image segmentation problem. The optimum threshold values are determined by the maximization of Kapur's or Otsu's objective function using Krill Herd Optimization technique. The proposed method reduces the computational time for computing the optimum thresholds for multilevel thresholding. The applicability and computational efficiency of the Krill Herd Optimization based multilevel thresholding is demonstrated using various benchmark images. A detailed comparative analysis with other existing bio-inspired techniques based multilevel thresholding techniques such as Bacterial Foraging (BF), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Moth-Flame Optimization (MFO) has been performed to prove the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach that extracts the maximum power from photovoltaic (PV) system utilizing the Horse Herd Optimization (HHO) algorithm under different weather conditions, including fast changes in solar radiation (FCSR) and partial shading conditions (PSCs). The HHO algorithm is a technique for optimization that mimics the movement behavior exhibited by horses within a herd. The proposed MPPT controller has been tested and compared with other recognized metaheuristic algorithms, including the Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Flower Pollination (FP), Deterministic PSO (DPSO), and Cuckoo Search (CS). According to the simulation results, the proposed HHO-based MPPT method is found to outperform other considered metaheuristic methods in terms of the maximum power extraction, fast-tracking, and settling times under various weather conditions. Additionally, the suggested MPPT controller is robust and can continuously track the MPP under the FCSR. The performance of the proposed HHO controller is validated experimentally on a real PV system. It is demonstrated that the proposed HHO algorithm is robust and capable of outperforming the other counterpart metaheuristic algorithms as well as offering the highest MPPT efficiency of about 98.81 % with a rapid tracking time of 2.2 s. Furthermore, it has the lowest power oscillations of about 1.91 %, ensuring stable and consistent power output. [ABSTRACT FROM AUTHOR]

The present study analysed the importance of individual variables and different thematic blocks of production areas, management, and herd infectious disease status on cow persistence, characterised by herd on-farm mortality rate (MR), culling rate (CR), and mean age of culled cows (MAofCC) applying multiblock partial least squares (mbPLS) analysis. This study included 120 free-stall dairy herds with ≥ 100 cows. Data on the previous year's predominant cow housing system and management practices were collected, and on-farm measurements and cow scoring were performed. Bulk tank milk (BTM) and heifer blood samples (10 samples per herd) were collected and analysed for antibodies against the selected pathogens. In total, 172 variables were aggregated into 14 thematic blocks. The annual CR, MR, and MAofCC values were calculated for each herd. Thematic blocks with significant impact on cow persistence (included herd MR, CR and MAofCC) were 'infectious diseases' (block importance index out of all blocks = 13.6%, 95% CI 10.3; 20.5), 'fertility management' (16.3%, 95% CI 6.8; 26.9), 'lactating cow management' (11.5%, 95% CI 6.4; 17.8), 'milking' (11.3%, 95% CI 3.2; 17.1), 'herd characteristics' (10.1%, 95% CI 6.3; 14.2), 'close-up period management' (9.7%, 95% CI 2.7; 15.7), 'calving management' (7.9%, 95% CI 3.1; 11.4) and 'disease management' (7.3%, 95% CI 0.2; 12.0). Variable categories with the highest importance in explaining composite outcome including herd MR, CR and MAofCC were rear-end and udder lesions in ≥ 20% of the cows, BTM and heifers seropositive to bovine respiratory syncytial virus, vaccination against bovine herpesvirus 1, twice daily milking and herd location in Northwest region. Larger herd size, higher levels of milk yield, and rearing predominantly Holstein breed cattle were herd factors associated with poorer cow persistency. Grazing cows and having semi-insulated barns were associated with lower CR and MR, respectively. Heat detection and farm pregnancy testing strategies were significant factors in the fertility block. Using disposable dry papers for teat cleaning and not using any wet teat-cleaning tools were risk factors for high MR. A robotic milking system was protective for increased herd MR and CR. A high pre-calving body condition score and poor rear body cleanliness of ≥ 30% of cows were associated with inferior herd persistency outcomes. Calving in group pens with deep litter bedding was associated with a lower CR. Multiblock PLS model is innovative tool that helped to identify most influential farming areas but also single risk factors associated with cow persistency described by multiple parameters. [ABSTRACT FROM AUTHOR]

• Sup plementary feeding of European bison influence Ashworthius sidemi nematode infection. • Sup plementary fed European bison have higher infection parameters than non-fed ones. • Management strategies may have an impact on pathogen spread in mammalian populations. • Application of adaptive management in reducing threats to protected species is important. Parasitic infections in wildlife are influenced by numerous factors, including those related to wildlife management. This includes sup plementary feeding widespread in numerous ungulates, including European bison. In this paper we analysed the influence of sup plementary feeding and winter ranging of European bison herds on the dynamics, prevalence and infection severity of the blood-sucking nematode Ashworthius sidemi in two areas in NE Poland: the Knyszyn Forest (KF) and the Białowieża Primeval Forest (BPF) with diversified management strategies. We found significant differences in A. sidemi abundance and intensity between European bison groups; sup plementary fed European bison from the BPF had higher parasitic load (3020 parasites, on average), than non-fed individuals from the KF (1400) and from the BPF (770). The prevalence was relatively high in all groups (93–96 %). In the KF the highest infection rate was observed 9 years after the first appearance of A. sidemi , with a maximum value of 8,620 nematodes; while in the BPF, after just 6 years with maximal load up to 44,310 A. sidemi. The most plausible mechanism behind the observed pattern is probably the winter ranging behaviour of differently managed herds. We found that increasing winter home range size of European bison was associated with a significant reduction in the A. sidemi abundance. Our study shows that different management strategies may have an impact on animal spatial behaviour and associated spread and dynamics of pathogens in mammalian populations, and stresses the importance of adaptive management in reducing threats to wildlife. [ABSTRACT FROM AUTHOR]

• A local outlier factor algorithm detects social density of suckling piglets. • This method could quantify the spatial relationship among different piglets. • This method could detect cluster in the piglet group. • The proposed method could detect outlier piglets. Pigs and their lactating piglets are herd animals, and breeders usually observe their social distance to determine their physiological status, such as when they form clusters during cold stress, or whether there are outlier piglets with abnormal growth conditions. Instead, papers on automated detection of piglet social distance are rare. This paper proposed a novel method, the convolutional neural network combined with modified local outlier factor (CNN-LOF), to quantify the piglet social density, and detect piglets far from the herd (outlier piglets). The convolutional neural network (CNN) model named YOLOv5 was used to construct the piglet detector, and auto-mark piglets by detection box, which adopted boxes' center points instead of the piglets. The optimized local outlier factor (LOF) algorithm was employed to calculate the social density of the piglets, and the outlier piglets were based on outlier factors greater than 2. Besides, the social density of different periods was calculated, compared, and analyzed. According to the results, the accuracy of CNN-LOF for detecting outlier piglets was 97.7% for the 6,113 test images measured. The larger social density of a piglet indicates more other piglets around and higher probability of being in the center of the herd. CNN-LOF and manual detection have a similarity of more than 80% in a continuous period of 1 h. In summary, this study quantifies the social density of suckling piglets, which also intuitively reveals the distribution of piglets in different environments, and provides technical support for precision livestock farming. [ABSTRACT FROM AUTHOR]

In recent years the concept of wellbeing, encompassing an individual's satisfaction with different aspects of their life, has received increased attention in literature. However, we find few studies of wellbeing in dairy farming. Therefore, the aim of this paper is to explore the wellbeing of Norwegian dairy farmers with automatic milking systems, and which factors are associated with wellbeing. We explore four dimensions of wellbeing; income, job satisfaction, mental health and family work balance. Data were collected from 739 Norwegian farmers using automatic milking systems. Linear regression, exploratory factor analysis and structural equation modelling were used to analyse the data. The results show that the following factors are associated with farmer wellbeing; gender, education, having a successor and colleagues, herd size and experience with automatic milking systems, together with training in use of the system and access to counselling. Our findings show that while being a female farmer is strongly associated with better family-work balance, it is negatively associated with mental health. Furthermore, we find that training in AMS, better management systems which avoids data overload, and access to extension services and colleagues are important for farmer wellbeing. Finally, our findings show that having a successor increases wellbeing, while increasing herd size reduces wellbeing. The findings have implications for farmers, extension services and for suppliers of automated systems to farmers. Suppliers and advisory services need to provide adequate training in AMS. Future advisory service models could offer back-office services monitoring key performance variables and help farmers interpret data for better decision making. Suppliers, preferrably in collaboration with extension services, should improve the AMS management software to avoid farmer technostress. Furthermore, suppliers and advisory services should facilitate networking among AMS-farmers to promote their wellbeing. • Wellbeing takes a broad perspective on quality of life. • Wellbeing is a useful concept to understand how farmers' experience their existence. • Male and female farmers experience wellbeing differently. • Having a successor is positively associated with wellbeing. • Data overload is negatively associated with wellbeing. [ABSTRACT FROM AUTHOR]

A high-quality animal health surveillance service is required to inform policy and decision-making in food-animal disease control, to substantiate claims regarding national animal health status and for the early detection of exotic or emerging diseases. In Ireland, the Department of Agriculture, Food and the Marine provides partially subsidized testing of farm animal samples and postmortem examinations to the Irish agriculture sector (farmers) at 6 regional veterinary laboratories (RVL) throughout the country. Diagnoses and data from these submissions are recorded and reported monthly and annually to enable animal health monitoring and disease surveillance. In a passive surveillance model, both the veterinary practitioner and the farmer play a vital role in sample submission by determining which cases are sent to the laboratory for postmortem or diagnostic testing. This paper identified factors influencing Irish dairy farmers' decisions to submit carcasses to RVL. Behavioral determinants of the submission of samples where veterinary professionals are concerned has been studied previously; however, limited work has studied determinants among farmers. This study conducted qualitative analyses of decisions of Irish dairy farmers relevant to diagnostic sample submission to an RVL and to examine the herd-level characteristics of farmers that submitted cases to an RVL. The biographical narrative interpretive method was used to interview 5 case-study farmers who were classified nonsubmitters, medium, or high submitters to the postmortem service based on the proportion of on-farm mortalities submitted to the laboratory service in 2016. The data obtained from these interviews were supplemented and triangulated through dairy farmer focus groups. The data were thematically analyzed and described qualitatively. In addition, quantitative analysis was undertaken. Data for herds within the catchment area of a central RVL were extracted, and a multivariable logistic regression model was constructed to examine the relationship between herds from which carcasses were submitted to the laboratory and those from which none were submitted. Results from the analysis show that the farmer's veterinary practitioner was the primary influence on submission of carcasses to the laboratory. Similarly, the type of incident, logistical issues with transporting carcasses to the laboratory, influence of peers, presence of alternative private laboratories, and a fear of government involvement were key factors emerging from the case-study interview and focus group data. Herd size was identified in both the qualitative and quantitative analysis as a factor determining submission. In the logistic regression model, herd size and increased levels of expansion were positively correlated with the odds of submission, whereas distance from the laboratory was negatively associated with odds of submission. These results identify the main factors influencing the use of diagnostic services for surveillance of animal health, signaling how services may be made more attractive by policy makers to a potentially wider cohort of users. [ABSTRACT FROM AUTHOR]

The use of test-day models to model milk mid-infrared (MIR) spectra for genetic purposes has already been explored; however, little attention has been given to their use to predict milk MIR spectra for management purposes. The aim of this paper was to study the ability of a test-day mixed model to predict milk MIR spectra for management purposes. A data set containing 467,496 test-day observations from 53,781 Holstein dairy cows in first lactation was used for model building. Principal component analysis was implemented on the selected 311 MIR spectral wavenumbers to reduce the number of traits for modeling; 12 principal components (PC) were retained, explaining approximately 96% of the total spectral variation. Each of the retained PC was modeled using a single trait test-day mixed model. The model solutions were used to compute the predicted scores of each PC, followed by a back-transformation to obtain the 311 predicted MIR spectral wavenumbers. Four new data sets, containing altogether 122,032 records, were used to test the ability of the model to predict milk MIR spectra in 4 distinct scenarios with different levels of information about the cows. The average correlation between observed and predicted values of each spectral wavenumber was 0.85 for the modeling data set and ranged from 0.36 to 0.62 for the scenarios. Correlations between milk fat, protein, and lactose contents predicted from the observed spectra and from the modeled spectra ranged from 0.83 to 0.89 for the modeling set and from 0.32 to 0.73 for the scenarios. Our results demonstrated a moderate but promising ability to predict milk MIR spectra using a test-day mixed model. Current and future MIR traits prediction equations could be applied on the modeled spectra to predict all MIR traits in different situations instead of developing one test-day model separately for each trait. Modeling MIR spectra would benefit farmers for cow and herd management, for instance through prediction of future records or comparison between observed and expected wavenumbers or MIR traits for the detection of health and management problems. Potential resulting tools could be incorporated into milk recording systems. [ABSTRACT FROM AUTHOR]

This paper is motivated by the recent discussion on the need of market supervisors, regulators, and policy makers, to take into account the behavioral elements of market participant attitudes and psychological and cognitive biases when taking policy decisions. We contribute to the discussion by studying, for the first time, the relationship between conventional and unconventional central bank monetary policy and herd behavior in equity markets, and argue that the transmission channel, through which monetary policy may affect herd behavior, is economic expectations and investor sentiment. We combine a range of research methodologies to measure monetary policy, herd behavior, and their possible relation, and our results indicate that conventional and unconventional Fed monetary policy explains a significant percentage of US equity market herd behavior variance, while ECB monetary policy explains a lower percentage of Eurozone herding variance. Impulse Response Functions indicate that Fed's conventional expansionary policy and non-standard policy reduces the levels of herding in the US equity market, while conventional ECB expansionary policy induces higher levels of herding in Spain and Italy. We also detect spill-over effects from Fed monetary policy to EU market herd behavior. [ABSTRACT FROM AUTHOR]

• Mammary involution is the regression of mammary tissue to non-secreting state. • Mammary involution is influenced by hormonal and local mechanims. • The mammary gland is particularly susceptible to infections after cessation of lactation. • Udder health management at the end of a lactation period aims to cure intramammary infections and prevent new ones. • Culling ewes contributes to decreasing mastitis incidence. • Culling should be complemented with intramammary antibiotic administration at the end of a lactation period. Objective of the paper is to describe mechanisms leading to mammary involution in ewes and changes taking place in the mammary gland during that period. Mammary involution is the regression of mammary tissue to non-secreting state and takes place as initiated, gradual or senile. In mutton-type production systems cessation of lactation is abrupt, whilst in dairy-type production systems it is progressive or abrupt. The period from cessation of milk removal until the beginning of subsequent lactation period is termed 'dry-period' and is distinguished into stage of active involution, stage of the 'steady-state' involution and stage of redevelopment and lactogenesis. The 'dry-period' is important in health management of sheep for optimum milk production during the subsequent lactation period, as it is necessary for renewal of mammary epithelial cells. Mammary involution is influenced by decreased activity of galactopoietic hormones and local mechanisms in response to milk accumulation in the gland. Milk accumulation plays a major role in triggering apoptosis of epithelial cells. There are two pathways leading to apoptosis, an intrinsic and an extrinsic. During involution, significant histological changes take place in the mammary gland, mainly reduction (up to 3.6% up to the 4th day) of the epithelial area of the gland and increase of its stromal part. The mammary gland is particularly susceptible to infections after cessation of lactation; progressively, that changes; a keratin plug is formed at the teat orifice, leucocytes accumulate in the gland and concentrations of immunoglobulins and lactoferrin increase. Udder health management at the end of a lactation period aims to cure infections that have occurred during the previous lactation period and prevent new intramammary infections during the 'dry-period'. Culling ewes with at least one mammary gland permanently damaged or ones chronically affected or others with incidents of relapsing mastitis or not fully respondent to mastitis treatment during the preceding lactation period contributes to decrease of veterinary expenses for mastitis control in the flock, elimination of sources of potential infection for other animals in the flock and decrease of flock bulk somatic cell counts in the subsequent lactation period. Culling should be complemented with intramammary antibiotic administration at the end of a lactation period. In many clinical studies from around the world, administration of antimicrobial agents at the end of a lactation period has been found beneficial. Antibiotic administration at drying-off may be performed to all animals in a flock ('complete') or only to those considered to be infected ('selective'). In all cases, maintenance of the prescribed withdrawal periods is essential to safeguard public health. The procedure should always be applied as part of a strategic udder health management plan in a flock; implementation improves the welfare of animals and affords significant financial benefits to the farmer. Correct udder health management in ewes at the end of a lactation period will contribute to improved mammary health for the forthcoming lactation period. [ABSTRACT FROM AUTHOR]

This paper presents the archaeobotanical and archaeozoological data of the 6th to 4th millennia cal BC sequence recently documented at Dehesilla Cave, and puts forward an interdisciplinary approach to the significant ecological patterns from this key archaeological site in the Southern Iberian Peninsula throughout the entire Neolithic period. indicate an ecological scenario characterised mainly by oak and wild olive forests, and human populations with agricultural practices and herds of mainly sheep and goats. However, this general panorama must have undergone several remarkable fluctuations. The first Neolithic populations of Dehesilla Cave, dated around the mid-6th millennium cal BC and linked to the Mediterranean impressa pottery complex, do not yet display evidences of agriculture, while all of the subsequent Early Neolithic levels indicate a model of small-scale populations with a mixed economy but still with a greater component of livestock. The second quarter of the 5th millennium cal BC shows a marked accentuation of the monoculture of naked wheats, which could have been related to the transition from an intensive to an extensive farming system. This may have entailed a selective pressure on the environment, leading to a large deforestation spanning the second half of the 5th millennium cal BC and the constitution of relatively open thermo-Mediterranean forests with a physiognomy similar to that of the dehesa. These ecological patterns are discussed within a review of the current state of the art of the use of plant and animal resources by the Neolithic human populations in the southern regions of the Iberian Peninsula. • A multidisciplinary archaeobotanical and archaeozoological approach is carried out. • A key Neolithic site in the Southern Iberian Peninsula is excavated and examined. • The first local Neolithic populations show evidence of livestock without agriculture. • A monoculture based on naked wheats was successful during 4800-4500 cal BC. • Human pressure on ecosystems led to the spread of open thermo-Mediterranean forests. [ABSTRACT FROM AUTHOR]

• Decomposition based multi-objective horse herd optimization algorithm is proposed. • A novel constraint handling technique is proposed. • The proposed algorithm is applied to wind farm incorporated power systems. • Several multi-objective optimal power flow case studies are performed. • Results are compared with recent research works. Optimal power flow plays an important role in integrating wind power into electric power networks. Because of its complexities, standard formulae are insufficient for the present scenario. Therefore, the multi-objective optimal power flow problems for wind farm incorporated power systems have been explored in this paper. The objectives are to minimize the generation costs, pollutant emissions, power losses, and voltage deviations. This paper proposes the development of a multi-objective meta-heuristic horse herd optimization for solving multi-objective optimal power flow problems. For this, a decomposition concept is introduced to the proposed algorithm leading to decomposition based multi-objective horse herd optimization algorithm. The constraint handling techniques in the previous papers have been found to be inefficient for optimal power flow problems. Therefore, a novel constraint handling technique is proposed in this paper to effectively control the variables out of bounds. In order to validate the performance and suitability of the proposed algorithm, seven case studies are examined. The algorithm is tested on wind farm incorporated IEEE-30, IEEE-57, and IEEE-118 bus test systems to demonstrate the efficiency in solving multi-objective optimal power flow problem for various problem sizes. The experimental results are demonstrating the efficiency of the proposed algorithm in solving complex optimization problems of various scales with multiple objectives. [ABSTRACT FROM AUTHOR]

Abstract: Anthropogenic impact in prehistoric settlements results in a considerable alteration of soil microbial communities depending on intensity and the character of human activities. This paper present a case study from a Late Bronze Age settlement located in the high-mountain part of the North Caucasus (Russia). The site represents a community, which presumably specialized in intensive livestock herding. Samples from settlement soils anthropogenically affected in the past and unmodified background soils were taken and studied. Of particular interest were divergences in soil microbial communities, expected to indicate different activities and animal presence in the site. The total microbial biomass, their respiratory activity, the biomass of fungal mycelium and the proportion of dark-colored hyphae were determined, as well as the quantitative state of keratinophilic fungi. The microbial characteristics vary considerably within the settlement locations, and contrast sharply with the reference soils exterior to the archaeological site. The cultural layer has higher percentage of active metabolizing microorganisms, whereas the total microbiological biomass is considerably lower than in the unmodified soils from the surroundings. A determining factor to transform the respiratory activity of microorganisms, in both qualitative and quantitative aspects, is the composition of the organic material which has been accumulated in the ground as a result of various human activities in the past. The cultural layers contain microorganisms, which can be reactivated when glucose is added. In the anthropogenically unmodified soils surrounding the prehistoric settlement, in contrast, 97% of the cells cannot be reactivated. Based on the mycological characteristics of the studied cultural layers and unmodified soils, in particular with regard to the total biomass of fungi mycelium, the dark pigmented fungal biomass, and the existence of keratin-decomposing soil fungi, detailed information about activity areas and their specific usage is given. The use of bio-indicators allows not only diagnosing anthropogenic impact in soils as such, but also significantly complements description of cultural layers of activity areas in the settlement, specifying their purpose. The paper presents the microbiological analyses applied and, moreover, discusses the potential of this approach as a non-destructive prospecting method on archaeological sites. [Copyright &y& Elsevier]

• This study demonstrated in economic terms that the elimination of Mycoplasma hyopneumoniae is economically viable in a few months. • The Mhyo-elimination protocols described in this paper have great success rate, and economic benefit is a likely outcome. • Even if the farm stayed negative only a year, the economic benefits downstream are worth it. • This information may be helpful for producers and veterinarians on decision-making process to conduct a Mhyo elimination protocol. Mycoplasma hyopneumoniae (Mhyo) is generally accepted to be the most common porcine respiratory pathogen worldwide causing big economical losses in swine production by affecting pig's downstream performance. The objective of this study was to develop a partial budget model to determine the payback period and economic value of two Mhyo elimination protocols. Retrospective data recorded from 2004 to 2017 from 70 breeding herds that implemented herd closure or whole-herd medication protocol targeting Mhyo elimination. Close out data was used to estimate differences in downstream performance between Mhyo-negative and positive flows. Assuming a 5000 sows breed-to-finish operation producing 135,870 weaned pigs and 125,000 finishing pigs/year, the total cost for implementing Mhyo elimination was $112,100 using the herd closure protocol, and $185,700 for the medication protocol. Statistically differences (p < 0.05) in downstream performance were observed for ADG and mortality, but not for feed conversion rate. The parameters that accounts for the greatest benefits were related to the improvement in ADG, savings in antibiotic medication in growing pigs and improvement in feed conversion rate. The benefit of Mhyo elimination was $877,375 per farm per year, or $7.00 per pig marketed. The estimated project value after 1 year was $616,121 for the herd closure considering a probability of success of 83%, and $323,177 for the medication protocol for 58% chance of success. The project value reached the break-even point when the cost per sow was $145.64 for the herd closure and $101.78 for the medication protocol. The payback period was 2 months after the start of marketing Mhyo-negative pigs for the herd closure, and 7 months for the medication protocol adjusted for the probability of success for each protocol. The protocols described here can be easily applied with a good success rate and showing that the benefits obtained are greater than the costs of project failure. Even if the farm stayed negative only a year, the economic benefits downstream are worth the investment. This information may help producers and veterinarians on decision-making process to conduct a Mhyo elimination protocol in their herds. [ABSTRACT FROM AUTHOR]

Abstract Automation of the measurement of the physiological and behavioural parameters of livestock has become an important goal for both scientists and farmers. Accurate data and knowledge about farmed animals, especially in cattle breeding, are needed. Proper early diagnosis of a cow's health status in real time allows for preventing the development of infection, oestrus detection and leads to reduced environmental stress. Thus, it contributes to more effective herd management. Among the physiological parameters, body temperature and its fluctuations are key indicators of health and well-being in animals. Currently, along with the development of technical solutions and their implementation, increasingly more attention is being paid to the continuously measurement of body core and peripheral temperature in animals. Recently there has been an increased number of publications devoted to this subject. However, there is a need to systematise this knowledge as these studies have had different purposes, have been performed in various environmental conditions, and the measurements were taken using different methods and equipment. As such, the results obtained by the different authors often may not be comparable. For this reason, this paper has two main purposes: to present the most widely used continuous methods of peripheral and body core temperature measurement, and to show its references values which characterise the individual locations of the cattle body in thermoneutral ambient. An analysis of the professional publications regarding measurements of peripheral and deep body temperature led to the conclusion that these methods have high research and diagnostic potential. However, it is necessary to standardised research to enable better and more comparable results, including among others; different cattle groups, animal age, health and environmental conditions. Highlights • Peripheral and deep body temperature measurements have high research potential. • Ambient conditions are the most important in affecting an animal's thermoregulation. • Individual areas of the body are characterised by different temperatures. • Nowadays technologically advanced devices enable to capture temperature changes. [ABSTRACT FROM AUTHOR]

Highlights • The effects of abortion and other herd-cow factors on risk of mastitis were estimated. • Probability of mastitis differed between herds, parities, milk yield levels, lactation stages, and calving seasons. • Abortion interacted with other factors influencing the risk of mastitis. • Cows with a previous experience of mastitis were at high risk of re-infection. • Considering interaction terms in a risk factor analysis was informative. Abstract This paper forms a part of a series of studies aiming to estimate the costs of abortion in Iranian dairy herds. In previous studies we have determined mastitis as a significant risk factor for abortion. In order to provide a more reliable estimation of the costs associated with abortion in Iranian dairy herds, the risk of a cow getting infected with mastitis needs to be included. Data from 6 commercial herds and 32,191 cows was assigned to 3-weeks in milk (3-WIM) records from 1 to 567 d after calving (1st–27th 3-WIM). The effect of herd, parity, calving season, past incidence of abortion, cumulative FCM yield level (CFCML), past incidence of mastitis in previous 3-WIM periods (EMAS), days in milk (DIM) and their significant 2-way interactions on mastitis in current 3-WIM period (MAS) were evaluated using a logistic regression model. Mastitis rate (MR) in studied herds was on average 28.3%. Results show that herd, parity, EMAS, CFCML, calving season, and lactation stage significantly (P < 0.01) influenced the risk of MAS. The risk of MAS increased with lactation number. Cows with EMAS had 4.98 times greater risk of MAS compared to cows with no EMAS. Additionally, cows with medium-level CFCML (i.e. 2, 3, and 4) had a higher risk of MAS compared to cows on level 1 and 5. Calving during the spring significantly (P < 0.01) increased the risk of MAS compared to other seasons. Past incidence of abortion, however, was not significantly associated with MAS, but remained in the final model because of the interaction with other factors. It can be concluded that a risk factor analysis with all significant interactions is more informative than a model without the interactions, especially when making the optimal decision for a cow with given characteristics. Moreover, knowledge on the effect of influential factors on mastitis will be useful when designing mastitis control programs at herd or national level. [ABSTRACT FROM AUTHOR]

• A new multipurpose simulation model was developed in MATLAB-Simulink environment. • A pasture-based mobile AMS was investigated in case of Sweden. • Pasture yield, growth rate, and grazing characteristics were investigated. • Milk yield, and milk marketing during grazing season were investigated. • On-site milk selling via milk vending machine was introduced. • The performance of the model was evaluated using secondary data and information from a dairy farm. It is essential to promote sustainable dairy farming which could lead to improved animal welfare, economic benefits, biodiversity and environmental benefits, milk quality, and customer satisfaction. In this regard, a mobile automated milking system (AMS) could contribute a lot. However, mobile AMS is a new innovative system which is not investigated well. Therefore, a simplified and integrated management approach should be introduced. The main objective of this study was to develop a multipurpose simulation model (DigiMilk model) specific to pasture-based mobile AMS. The model comprises five major subsystems: Pasture yield as dry matter (DM) and grazing characteristics; AMS Milking and milk yield characteristics; Milk handling and marketing; Resource consumption; and Economic assessment. This paper (Part-I) focuses on the first three components while the remaining two subsystems would be addressed in Part-II of this paper. DigiMilk model was built in MATLAB-Simulink environment. It was tested and evaluated using mainly secondary data and limited primary information acquired from a dairy farm in central Sweden. In this initial analysis, a continuous stocking system on pasture was assumed to be implemented from May 15 till September 15. Multiple sensitivity analyses were successfully conducted to get more insights. The results indicated that, considering maximum pasture growth rate of 77 kgDM day−1ha−1, the accumulated average pasture yield, over the grazing season, was estimated to be 6928 kgDM ha−1. For cows with average grazing rate of 16–18 kgDM day−1cow−1, the stocking rate of 3 cow ha−1 could lead to good performance of grazing management. When stocking rate and grazing rate of 3 cow ha−1 and 16 kgDM day−1cow−1 were considered respectively, the cumulative milk yield values (excluding amount consumed by calves) over the grazing season were estimated to be 2101 L cow−1 and 6303 L ha−1. Out of this 6303 L ha−1, 2952 L ha−1was estimated to be sold on-site, using milk vending machine (MVM), while 3351 L ha−1 was to be delivered to super market. The accuracy of results from the the simulation model could be improved with future work with more real data from actual demonstration of mobile AMS over the entire grazing season. In addition to its capacity to serve as an integrated decision making tool, DigiMilk model enables to have organized digital data that could be useful for future researches to evaluate the environmental and/or economic performances of pasture-based dairy systems with mobile AMS. [ABSTRACT FROM AUTHOR]

Water is an essential resource for humans, animals, and plants. Water is also necessary for the manufacture of many products such as milk, textiles, paper, and pharmaceutical composites. During manufacturing, some industries generate a large amount of wastewater containing numerous contaminants. In the dairy industry, for each litre of drinking milk produced, about 10 L of wastewater is generated. Despite this environmental footprint, the production of milk, butter, ice cream, baby formula, etc., are essential in many households. Common contaminants in dairy wastewater include high biological oxygen demand (BOD), chemical oxygen demand (COD), salts as well as nitrogen and phosphorus derivatives. Nitrogen and phosphorus discharges are one of the leading causes in the eutrophication of rivers and oceans. Porous materials have long held significant potential as a disruptive technology for wastewater treatment. However, thus far they have been understudied for use in dairy wastewater treatment. Ordered porous materials, such as zeolites and metal organic frameworks (MOFs), represent classes of porous materials with significant potential for the removal of nitrogen and phosphorus. This review explores the different zeolites and MOFs applied in the removal of nitrogen and phosphorus from wastewater and the prospect of their potential for use in wastewater management in the dairy industry. [Display omitted] • Dairy industries are bound to increase their production as the world population increases. • Dairy industries produce about 10 L of wastewater per litre of drinking milk containing Phosphorus and nitrogen. • Zeolites can be employed industrially for the removal of nitrogen and phosphorus from dairy wastewaters. • Metal organic framework can be employed industrially for the removal of nitrogen and phosphorus from dairy wastewaters. [ABSTRACT FROM AUTHOR]

Scholars and activists have called for a recognition of a "third way" of farming which is beyond the binary between organic and conventional agriculture and instead represents a spectrum of practices. In this paper, we ask if the way Plain (conservative Amish and Mennonite) farmers manage antibiotic use and natural remedies is an example of these middle paths. Antibiotic use in managing herd health is a practice with significant environmental and health implications. Regulations and use patterns may enhance potential bifurcation between conventional and organic production in the US compared to the European context. The data for this study were collected through semi-structured interviews with 29 Plain dairy producers and nine veterinary health professionals in Michigan and Pennsylvania. Interviews focused on attitudes and behavior around herd health and possible connections to family healthcare management. Overall, our results indicate that the Plain farmers, especially the Amish, combined minimal antibiotics with a mixture of natural remedies. This indicates that delineations between organic and conventional agriculture and natural healthcare and mainstream healthcare may be more nuanced and complex than has been characterized in previous literature. • Amish and Mennonite dairy farmers offer an example of "middle paths" in managing antibiotic use. • Amish and Mennonite dairy farmers use a "third way" between organic and conventional management. • Amish and Mennonite dairy farmers combine antibiotics and natural remedies for animal health. [ABSTRACT FROM AUTHOR]

• In this paper, a novel heart disease detection technique, namely Grey Wolf Horse Herd optimization-based Shepard Convolutional Neural Network (GWHHO-based ShCNN) is devised for detecting the heart disease. • Here, the heart disease detection is carried out based on Spark architecture, which comprises of master node and slave node. • In the slave node, the pre-processing and feature fusion is performed, whereas in the master node, heart disease detection is performed. Here, the pre-processing is carried out using missing value imputation and z-score normalization. • Subsequently, the feature fusion is done using Hellinger distance with Deep Q Network (DQN). Moreover, the heart disease detection is carried out using ShCNN, which is trained using developed GWHHO algorithm. • Besides, the experimentation of devised model provides the better outcome in terms of testing accuracy, sensitivity and specificity of 0.9325, 0.9472 and 0.9142 using VA long beach dataset. Analyzing massive amounts of data that contain many sorts of data is known as big data analytics. Additionally, the bulk of applications in the actual world need a significant amount of information. Machine learning techniques are used to automatically identify the types and severity of cardiac disease due to the rapid increase of biomedical and healthcare information. However, the ML approach has a number of drawbacks, and because of the complexity of the material, it did not always produce the best results. As a result, an improved deep learning strategy offers a superior fix for this problem. In this study, a brand-new method for diagnosing heart disease—the Grey Wolf Horse Herd optimization-based Shepard Convolutional Neural Network is developed. Here, the master node and slave node-based Spark architecture is used to carry out the heart disease detection process. Preprocessing and feature fusion are carried out in the slave node, whilst heart disease detection is done in the master node. Z-score normalization and missing value imputation are used in this case for pre-processing. The feature fusion is then carried out utilizing Hellinger distance and Deep Q Network (DQN). Furthermore, the ShCNN, which was trained using the created GWHHO algorithm, is used to identify heart disease. The Grey Wolf Optimizer (GWO) and Horse Herd Optimization (HHO) algorithms are also incorporated into the newly developed GWHHO algorithm. Additionally, employing the VA Long Beach dataset, the experimental of the developed model yields improved results in terms of testing accuracy, sensitivity, and specificity of 0.9325, 0.9472, and 0.9142. [ABSTRACT FROM AUTHOR]

The relationship between cows’ health, reproductive performance or disorders and their longevity is well demonstrated in the literature. However these associations at the cow level might not hold true at the herd level, and herd-level variables can modify cow-level outcomes independently of the cows’ characteristics. The interaction between cow-level and herd-level variables is a relevant issue for understanding the culling of dairy cows. However it requires the appropriate group-level variables to assess any contextual effect. Based on 10 years of health and production data, the objectives of this paper are:(a) to quantify the culling rates of dairy herds in Québec; (b) to determine the profiles of the herds based on herd-level factors, such as demographics, reproduction, production and health indicators, and whether these profiles can be related to herd culling rates for use as potential contextual variables in multilevel modelling of culling risk. A retrospective longitudinal study was conducted on data from dairy herds in Québec, Canada, by extracting health information events from the dairy herd health management software used by most Québec producers and their veterinarians. Data were extracted for all lactations taking place between January 1st, 2001 and December 31st, 2010. A total of 432,733 lactations from 156,409 cows out of 763 herds were available for analysis. Thirty cow-level variables were aggregated for each herd and years of follow-up, and their relationship was investigated by Multiple Factor Analysis (MFA). The overall annual culling rate was 32%, with a 95% confidence interval (CI) of [31.6%,32.5%]. The dairy sale rate by 60 days in milk (DIM) was 3.2% [2.8%,3.6%]. The annual culling rate within 60 DIM was 8.2% [7.9%,8.4%]. The explained variance for each axis from the MFA was very low: 14.8% for the first axis and 13.1% for the second. From the MFA results, we conclude there is no relationship between the groups of herd-level indicators, demonstrating the heterogeneity among herds for their demographics, reproduction and production performance, and health status. However, based on Principal Component Analysis (PCA), the profiles of herds could be determined according to specific, single, herd-level indicators independently. The relationships between culling rates and specific herd-level variables within factors were limited to livestock sales, proportion of first lactation cows, herd size, proportion of calvings occurring in the fall, longer calving intervals and reduced 21-day pregnancy rates, increased days to first service, average age at first calving, and reduced milk fever incidence. The indicators found could be considered as contextual variables in multilevel model-building strategies to investigate cow culling risk. [ABSTRACT FROM AUTHOR]

Rangeland productivity is strongly conditioned by the amount and temporal distribution of precipitation. Thus, the worsening of droughts with climate change could be a serious threat to their existence. This paper presents a modelling study aimed at evaluating the sensitivity of a valuable type of commercial rangelands, namely Spanish dehesas, to increases in the frequency and intensity of droughts driven by climate change. The assessment consisted in a multi-way ANOVA carried out on the basis of 5400 simulations of a multidisciplinary integrated model. It included two blocking factors linked to climate change scenarios, namely Representative Concentration Pathway and downscaling method, and two treatment factors, namely return period and severity of droughts. The levels of all factors were included as part of the simulation scenarios. The response variables constituted a summary of model's behaviour throughout one simulation. They were average profits per farmer and average stocking rate, both calculated over the entire simulation period, and remaining soil depth at the end of the simulation. The effects of the treatment factors on the response variables were small for all blocks, thereby suggesting that the sensitivity, and thus the vulnerability, of Spanish dehesas to the worsening of droughts would be low under climate change. Farmers were defined as conservative in all model simulations, that is, they minimized changes in the size of their herds and bought supplementary feed to meet shortfalls in livestock feed unless it was excessively expensive. Thus, we conclude that this group strategy could explain the adaptive capacity of Spanish dehesas to droughts. This paper shows that multidisciplinary integrated models are valuable learning tools to acquire insights into the relationships between climate, ecologic and socio-economic factors. Although there is a recurrent call for holistic studies, they are still rare in the rangeland literature. Hopefully, this paper will motivate some researchers to consider this approach. Unlabelled Image • Climate change heralds the worsening of droughts threatening rangeland sustainability. • Modelling assessment of the sensitivity of a dehesa rangelands to increases in the frequency and intensity of droughts. • The sensitivity, and thus the vulnerability, of dehesas to the worsening of droughts would be low. • A widespread conservative strategy would provide Mediterranean commercial rangelands with adaptive capacity to droughts. [ABSTRACT FROM AUTHOR]

In the current close-to-smart power systems, renewable energies are the most significant elements that need to be carefully addressed in power system studies. In this regard, microgrids (MGs) have been introduced recently to activate the large penetration of renewables. However, integration of such generation technologies that are associated with severe uncertainty in their power output would significantly impact the scheduling of energy resources in MGs. Thus, efficient energy management systems are required to be employed in MGs. Therefore, this paper presents a day-ahead scheduling framework for an MG equipped with a solar photovoltaic (PV) unit. In this respect, different climate conditions and their impacts on the power output of the PV unit and the optimal scheduling of the MG have been investigated in this paper. To this end, four different days from the four seasons have been used to extract the data of solar irradiance. The scheduling problem has been formulated in a single-objective optimization framework, where the objective function is defined as minimizing the total operating cost over the scheduling period. An effective optimization algorithm named "modified krill herd (MKH)" algorithm is proposed to solve the mentioned day-ahead scheduling problem, while there are renewable and non-renewable generating units, besides an energy storage system. Furthermore, a comprehensive comparison has been made between the MKH algorithm and some well-known optimization algorithms to verify the superior performance of the suggested method. [ABSTRACT FROM AUTHOR]

Accurate ultrasonographic determination of foetal number between 45 to 90 days of gestation has revolutionised reproductive management in sheep over 30 years ago and is now established as an essential component of flock health planning and productivity. Ultrasonographic measurements of fat and muscle depths over ribs 12 and 13 in growing lambs can accurately reflect carcass composition and have been incorporated in breeding programmes in sheep meat breeds. These ultrasound services may often be delivered by para-professionals, who individually scan tens of thousands of sheep annually. Currently, ultrasonographic examination in small ruminant practice is largely confined to the investigation of individual valuable pedigree animals referred to veterinary schools or other centres of expertise, despite publication of clinical papers illustrating its application in a wide range of common diseases, including respiratory diseases, urolithiasis, liver and kidney pathologies. Typically, ultrasonographic examination of any organ system should take no more than 1–2 min with the results available immediately, affording a much more informed diagnosis and prognosis. Presently, examination of all adult sheep in the flock for ovine pulmonary adenocarcinoma is being researched in an attempt to eliminate this disease, which has no commercially available diagnostic test or obvious flock control measure. Ultrasonographic investigation is much cheaper than common clinical biochemistry tests, e.g., haematological examination or protein measurements (albumin and globulin, acute phase proteins, fibrinogen and haptoglobin). The ability to record ultrasonographic findings as images or video files directly to mobile telephones, tablets and laptops allows clinicians to transmit such data to a specialist should a second opinion prove necessary. Tele-medicine is well established in several veterinary disciplines and will become increasingly common for diagnostic imaging in small ruminant practice during the next decade. Comparison of images collected over a period of time allows a disease process to be accurately monitored. Re-evaluation of ultrasonographic recordings is an invaluable learning exercise should necropsy results become available. [ABSTRACT FROM AUTHOR]

Herd health slaughter checks regularly identify enzootic pneumonia-like (EP-like) lesions and pleuritis. The aim of this paper is to determine the associations between these lesions and post-trimming carcass weight. Data were collected on the presence/ absence and severity of EP-like lesions and presence/ absence of pleuritis from pigs at the abattoir. Linear mixed models identified a significant association between an increase in EP-like lesion severity and a decrease in post-trimming carcass weight ( P =0.006) at the individual level. Each categorical increase in EP-like lesion severity (5 points step) was associated with a 0.37 kg reduction in post-trimming carcass weight. The presence of EP-like lesions in individual pigs, irrespective of severity ( P =0.034) and the presence of pleuritis ( P =0.038) were significantly associated with a reduction in post-trimming carcass weight of 1.26 kg and 1.25 kg respectively. The results confirm that the presence of these lesions at slaughter are associated with a significant decrease in production performance which can result in substantial economic implications for producers. [ABSTRACT FROM AUTHOR]

Infectious Bovine Rhinotracheitis (IBR) occurs worldwide, requiring significant resources for eradication programs or surveillance purposes. The status of infection is usually detected by serological methods using the virus neutralization test (VNT) or enzyme-linked immunosorbent assay (ELISA) on individual sera. The gE DIVA (Differentiating Infected from Vaccinated Animals) vaccines approach, adopted in order to reduce the virus circulation and prevent clinical signs, have tightened the range of available methods for the serological diagnosis. Different gE blocking ELISA could be performed to detect specific antibodies in sera of infected or whole virus-vaccinated animals but with less sensitivity if applied to bulk milk samples, especially in marker-vaccinated herds. A new rec-gE ELISA was recently developed in Italy and applied with good performances on blood serum samples. The present paper focuses on the application of a rapid protocol for purification/concentration of immunoglobulin G (IgG) from bulk milk and on the use of the new rec-gE indirect ELISA. The study involved three different partners and 225 herds (12,800 lactating cows) with different official IBR diagnostic statuses. The diagnostic specificity of the method was demonstrated closed to 100% while the diagnostic sensitivity was strictly related to the herd-seroprevalence. Considering 2.5% as the limit of detection of within-herd seropositivity prevalence, the diagnostic sensitivity showed by the proposed method was equal to 100%. A single reactivation of a whole strain vaccine in an old cow was detected inside a group of 67 lactating cows, showing the field applicability of the method. [ABSTRACT FROM AUTHOR]

Digitalization and the use of Smart Farming Technologies are considered a major opportunity for the future of agriculture. However, realisation of full benefits is constrained by: (1) farmers' interest in and use of big data to improve farm decision making; (2) issues of data sovereignty and trust between providers and users of data and technology; (3) institutional arrangements associated with the governance of data platforms. This paper examines the case of Australia's dairy herd milk recording system, arguably one of agriculture's first cases of 'big data' use, which collects, analyses and uses farm-level data (milk production, lactation and breeding records) to provide individual cow and herd performance information, used by individual farmers for farm management decisions. The aim of this study was to 1) examine the use of big data to add value to farm decision making; and 2) explore factors and processes, including institutional arrangements, which influence farmer engagement with and use of big data. This paper traces the Australian history of the organisation of dairy herd recording (established in 1912 and digitalized in late 1970s) and then uses findings from a longitudinal study of 7 case study dairy farms, which were incentivised to become involved in herd recording in 2015. Applying a conceptual framework linking path dependency in farm decision making and collaborative governance capacity, we find three new important dimensions of the farm user context influencing farmer demand for big data applications: 1) the transition to a new business stage; 2) the additionality farmers seek from data generated in one component of the farm system to other subsystems, and 3) the use of data in long term or strategic decision making. Further, we identified critical attributes of support services in addressing digital literacy, capacity and capability issues at farm level, including diversity in data presentation formats and facilitation of the on-farm transition process through intermediary herd test organisations. The role of farmers as governance actors, or citizens in the decisions of the trajectory of big data applications, adds to understanding of the nature of collaborative governance arrangements that support farm engagement. • Farmer interest in data relates to business stage and the decision's importance. • Cooperative governance structures facilitate farmer trust and use of big data. • Support services reduce the impact of low digital literacy, capacity and capability. • Inter-dependence occurs between existing data processes and optimising data use. [ABSTRACT FROM AUTHOR]

Flowchart of the proposed optimization-based RHE framework. • A novel krill herd (KH) based sharp edge enhancement framework is introduced for medical images. • Plateau limit and fitness function are proposed in this paper to achieve best enhanced image. • This explores KH algorithm to automatically adjust tunable parameter based on new fitness function. • Salp swarm algorithm (SSA) optimization is also used for the fair comparison of the proposed scheme. • The results show the effectiveness of the proposed method over well-known methods. In this paper, a novel krill herd (KH) based optimized contrast and sharp edge enhancement framework is introduced for medical images. Plateau limit and fitness function are proposed in this paper to achieve the best-enhanced image. A new plateau limit is applied to clip the histogram using minimum, maximum, mean, and median of the histogram with a tunable parameter. The residue pixels are reallocated to the relative vacancy available on histogram bins. This method explores KH meta-heuristic algorithm to automatically adjust the tunable parameter based on a novel fitness function. Fitness function contains two different objective functions, which use edge, entropy, gray level co-occurrence matrix (GLCM) contrast, and GLCM energy of image for best visual, contrast enhancement and improved different characteristic information of the anatomical images. This method is compared with a different state of the art methods to check the viability and vigorous of the scheme and salp swarm algorithm (SSA) optimization is also used for the fair comparison of the proposed approach. The results show that the proposed framework is having superior performance compared to all the existing methods, both qualitatively and quantitatively, in terms of contrast, information content, edge details, and structure similarity. [ABSTRACT FROM AUTHOR]

In this paper, we estimate an agent-based model (ABM) to investigate herding behaviors in the formation of investor sentiment. We formalize a simple opinion dynamics model in a social network framework and rely on a numerical method to estimate its parameters. We derive a sentiment proxy from the weekly aggregation of online messages concerning 15 US stocks and 5 cryptocurrencies. Our empirical results suggest a strong impact of herding behavior on the formation of sentiment toward highly volatile assets. For such assets, we simultaneously find limited impacts of financial returns and investor attention on the opinion formation process, suggesting that investor sentiment is explained by social interactions. On the other hand, we find a limited influence of social interactions on sentiment regarding less volatile assets, whose formation process is instead explained by the strong influence of financial returns and investor attention. In particular, we find that herding behavior was significantly higher and played a major role in the sentiment formation process regarding cryptocurrencies when the bubble occurred. • We formalize a simple ABM of opinion formation in social networks. • We estimate the herding parameters of the ABM with respect to 20 financial assets. • We link the intensity of herding behavior with the volatility of assets. [ABSTRACT FROM AUTHOR]