Assessment of Ammonia Emissions and Greenhouse Gases in Dairy Cattle Facilities: A Bibliometric Analysis.

A deeper understanding of gas emissions in milk production is crucial for promoting productive efficiency, sustainable resource use, and animal welfare. This paper aims to analyze ammonia and greenhouse gas emissions in dairy farming using bibliometric methods. A total of 187 English-language articles with experimental data from the Scopus and Web of Science databases (January 1987 to April 2024) were reviewed. Publications notably increased from 1997, with the highest number of papers published in 2022. Research mainly focuses on ammonia and methane emissions, including quantification, volatilization, and mitigation strategies. Other gases like carbon dioxide, nitrous oxide, and hydrogen sulfide were also studied. Key institutions include the University of California-Davis and Aarhus University. Bibliometric analysis revealed research evolution, identifying trends, gaps, and future research opportunities. This bibliometric analysis offers insights into emissions, air quality, sustainability, and animal welfare in dairy farming, highlighting areas for innovative mitigation strategies to enhance production sustainability. This research contributes to academia, enhancing agricultural practices, and informing environmental policies. It is possible to conclude that this research is a valuable tool for understanding the evolution of research on gas emissions in dairy cattle facilities, providing guidance for future studies and interventions to promote more sustainable production.

Digital and Precision Technologies in Dairy Cattle Farming: A Bibliometric Analysis.

The advancement of technology has significantly transformed the livestock landscape, particularly in the management of dairy cattle, through the incorporation of digital and precision approaches. This study presents a bibliometric analysis focused on these technologies involving dairy farming to explore and map the extent of research in the scientific literature. Through this review, it was possible to investigate academic production related to digital and precision livestock farming and identify emerging patterns, main research themes, and author collaborations. To carry out this investigation in the literature, the entire timeline was considered, finding works from 2008 to November 2023 in the scientific databases Scopus and Web of Science. Next, the Bibliometrix (version 4.1.3) package in R (version 4.3.1) and its Biblioshiny software extension (version 4.1.3) were used as a graphical interface, in addition to the VOSviewer (version 1.6.19) software, focusing on filtering and creating graphs and thematic maps to analyze the temporal evolution of 198 works identified and classified for this research. The results indicate that the main journals of interest for publications with identified affiliations are "Computers and Electronics in Agriculture" and "Journal of Dairy Science". It has been observed that the authors focus on emerging technologies such as machine learning, deep learning, and computer vision for behavioral monitoring, dairy cattle identification, and management of thermal stress in these animals. These technologies are crucial for making decisions that enhance health and efficiency in milk production, contributing to more sustainable practices. This work highlights the evolution of precision livestock farming and introduces the concept of digital livestock farming, demonstrating how the adoption of advanced digital tools can transform dairy herd management. Digital livestock farming not only boosts productivity but also redefines cattle management through technological innovations, emphasizing the significant impact of these trends on the sustainability and efficiency of dairy production.

An Interactive Feeder to Induce and Assess Emotions from Vocalisations of Chickens.

Understanding the emotional states of animals is a long-standing research endeavour that has clear applications in animal welfare. Vocalisations are emerging as a promising way to assess both positive and negative emotional states. However, the vocal expression of emotions in birds is a relatively unexplored research area. The goal of this study was to develop an interactive feeding system that would elicit positive and negative emotional states, and collect recordings of the vocal expression of these emotions without human interference. In this paper, the mechatronic design and development of the feeder is described. Design choices were motivated by the desire for the hens to voluntarily interact with the feeder and experience the different stimuli that were designed to induce (1) positive low-arousal, (2) positive high-arousal, (3) negative low-arousal, and (4) negative high-arousal states. The results showed that hens were motivated to engage with the feeder despite the risk of receiving negative stimuli and that this motivation was sustained for at least 1 week. The potential of using the interactive feeder to analyse chicken vocalisations related to emotional valence and arousal is being explored, offering a novel proof of concept in animal welfare research. Preliminary findings suggest that hens vocalised in response to all four stimulus types, with the number of vocalisations, but not the probability of vocalising, distinguishing between low- and high-arousal states. Thus, the proposed animal-computer interaction design has potential to be used as an enrichment device and for future experiments on vocal emotions in birds.

Value of simplified lung lesions scoring systems to inform future codes for routine meat inspection in pigs.

BACKGROUND: Across the European Union (EU), efforts are being made to achieve modernisation and harmonisation of meat inspection (MI) code systems. Lung lesions were prioritised as important animal based measures at slaughter, but existing standardized protocols are difficult to implement for routine MI. This study aimed to compare the informative value and feasibility of simplified lung lesion scoring systems to inform future codes for routine post mortem MI. RESULTS: Data on lung lesions in finisher pigs were collected at slaughter targeting 83 Irish pig farms, with 201 batches assessed, comprising 31,655 pairs of lungs. Lungs were scored for cranioventral pulmonary consolidations (CVPC) and pleurisy lesions using detailed scoring systems, which were considered the gold standard. Using the data collected, scenarios for possible simplified scoring systems to record CVPC (n = 4) and pleurisy (n = 4) lesions were defined. The measurable outcomes were the prevalence and (if possible) severity scoring at batch level for CVPC and pleurisy. An arbitrary threshold was set to the upper quartile (i.e., the top 25% of batches with high prevalence/severity of CVPC or pleurisy, n = 50). Each pair of measurable outcomes was compared by calculating Spearman rank correlations and assessing if batches above the threshold for one measurable outcome were also above it for their pairwise comparison. All scenarios showed perfect agreement (k = 1) when compared among themselves and the gold standard for the prevalence of CVPC. The agreement among severity outcomes and the gold standard showed moderate to perfect agreement (k = [0.66, 1]). The changes in ranking were negligible for all measurable outcomes of pleurisy for scenarios 1, 2 and 3 when compared with the gold standard (rs ≥ 0.98), but these changes amounted to 50% for scenario 4. CONCLUSIONS: The best simplified CVPC scoring system is to simply count the number of lung lobes affected excluding the intermediate lobe, which provides the best trade-off between value of information and feasibility, by incorporating information on CVPC prevalence and severity. While for pleurisy evaluation, scenario 3 is recommended. This simplified scoring system provides information on the prevalence of cranial and moderate and severe dorsocaudal pleurisy. Further validation of the scoring systems at slaughter and by private veterinarians and farmers is needed.

Environmental Risk Factors Influence the Frequency of Coughing and Sneezing Episodes in Finisher Pigs on a Farm Free of Respiratory Disease.

This study aimed to assess baseline levels of coughing on a farm free of respiratory disease, and to identify relationships between environmental conditions and coughing frequency in finisher pigs. Six replicates were conducted (690 pigs in total). A cross-correlation analysis was performed and lags of the predictor variables were carried forward for multivariable regression analysis when significant and showing r > 0.25. Results show that coughing frequency was overall low. In the first replicate, coughing was best predicted by exposure to higher ammonia concentrations that occurred with a lag of 1, 7, and 15 days (p = 0.003, p = 0.001, and p < 0.001, respectively), while in the sixth replicate coughing frequency was best predicted by the exposure to lower relative humidity and higher ventilation rates with a lag of 7 and 15 days (p < 0.001 and p = 0.003, respectively). Ammonia concentrations varied according to ventilation rates recorded on the same day (r > −0.70). In conclusion, guidelines on coughing levels in healthy pigs and calibration of the alarm systems of tools that measure coughing frequency can be extrapolated from this study. Environmental risk factors are associated with the respiratory health of finisher pigs.

Influence of temperature during incubation on the mRNA levels of temperature sensitive ion channels in the brain of broiler chicken embryos.

Thermosensation is crucial for the survival of any organism. In animals, changes in brain temperature are detected via sensory neurons, their cell bodies are located in the trigeminal ganglia. Transient receptor potential (TRP) ion channels are the largest temperature sensing family. In mammals, 11 thermoTRPs are known, as in poultry, there are only three. This research further elucidates TRP mRNA expression in the brain of broiler embryo's. Three incubation treatments were conducted on 400 eggs each: the control (C) at 37.6 °C; T1 deviating from C by providing a + 1 °C heat stimuli during embryonic day (ED) 15-20 for 8 h a day; and T2, imposing a + 2 °C heat stimuli. After each heat stimuli, 12 eggs per treatment were taken for blood sampling from the chorioallantoic membrane and brain harvesting. Incubation parameters such has residual yolk (free embryonic) weight, chick quality and hatch percentage were collected. After primer optimization, 22 target genes (13 TRPs and 9 non-TRPs) were measured on mRNA of the brain using a nanofluidic biochip (Fluidigm Corporation). Four target genes (ANO2, TRPV1, SCN5A, TRAAK) have a significant treatment effect - independent of ED. Another four (TRPM8, TRPA1, TRPM2, TRPC3) have a significant treatment effect visible on one or more ED. Heat sensitive channels were increased in T2 and to a lesser degree in T1, which could be part of an acclimatisation process resulting in later life heat tolerance by increased heat sensitivity. T2, however, resulted in a lower hatch weight, quality and hatchability. No hormonal differences were detected.

A Systematic Review of the Use of Technology to Monitor Welfare in Zoo Animals: Is There Space for Improvement?

A top priority of modern zoos is to ensure good animal welfare (AW), thus, efforts towards improving AW monitoring are increasing. Welfare assessments are performed through more traditional approaches by employing direct observations and time-consuming data collection that require trained specialists. These limitations may be overcome through automated monitoring using wearable or remotely placed sensors. However, in this fast-developing field, the level of automated AW monitoring used in zoos is unclear. Hence, the aim of this systematic literature review was to investigate research conducted on the use of technology for AW assessment in zoos with a focus on real-time automated monitoring systems. The search led to 19 publications with 18 of them published in the last six years. Studies focused on mammals (89.5%) with elephant as the most studied species followed by primates. The most used technologies were camera (52.6%) and wearable sensors (31.6%) mainly used to measure behaviour, while the use of algorithms was reported in two publications only. This research area is still young in zoos and mainly focused on large mammals. Despite an increase in publications employing automated AW monitoring in the last years, the potential for this to become an extra useful tool needs further research.

Adding value to food chain information: using data on pig welfare and antimicrobial use on-farm to predict meat inspection outcomes.

BACKGROUND: Using Food Chain Information data to objectively identify high-risk animals entering abattoirs can represent an important step forward towards improving on-farm animal welfare. We aimed to develop and evaluate the performance of classification models, using Gradient Boosting Machine algorithms that utilise accurate longitudinal on-farm data on pig health and welfare to predict condemnations, pluck lesions and low cold carcass weight at slaughter. RESULTS: The accuracy of the models was assessed using the area under the receiver operating characteristics (ROC) curve (AUC). The AUC for the prediction models for pneumonia, dorsocaudal pleurisy, cranial pleurisy, pericarditis, partial and total condemnations, and low cold carcass weight varied from 0.54 for pneumonia and 0.67 for low cold carcass weight. For dorsocaudal pleurisy, ear lesions assessed on pigs aged 12 weeks and antimicrobial treatments (AMT) were the most important prediction variables. Similarly, the most important variable for the prediction of cranial pleurisy was the number of AMT. In the case of pericarditis, ear lesions assessed both at week 12 and 14 were the most important variables and accounted for 33% of the Bernoulli loss reduction. For predicting partial and total condemnations, the presence of hernias on week 18 and lameness on week 12 accounted for 27% and 14% of the Bernoulli loss reduction, respectively. Finally, AMT (37%) and ear lesions assessed on week 12 (15%) were the most important variables for predicting pigs with low cold carcass weight. CONCLUSIONS: The findings from our study show that on farm assessments of animal-based welfare outcomes and information on antimicrobial treatments have a modest predictive power in relation to the different meat inspection outcomes assessed. New research following the same group of pigs longitudinally from a larger number of farms supplying different slaughterhouses is required to confirm that on farm assessments can add value to Food Chain Information reports.

Renal and Extra Renal Manifestations in Adult Zebrafish Model of Cystinosis.

Cystinosis is a rare, incurable, autosomal recessive disease caused by mutations in the CTNS gene. This gene encodes the lysosomal cystine transporter cystinosin, leading to lysosomal cystine accumulation in all cells of the body, with kidneys being the first affected organs. The current treatment with cysteamine decreases cystine accumulation, but does not reverse the proximal tubular dysfunction, glomerular injury or loss of renal function. In our previous study, we have developed a zebrafish model of cystinosis through a nonsense mutation in the CTNS gene and have shown that zebrafish larvae recapitulate the kidney phenotype described in humans. In the current study, we characterized the adult cystinosis zebrafish model and evaluated the long-term effects of the disease on kidney and extra renal organs through biochemical, histological, fertility and locomotor activity studies. We found that the adult cystinosis zebrafish presents cystine accumulation in various organs, altered kidney morphology, impaired skin pigmentation, decreased fertility, altered locomotor activity and ocular anomalies. Overall, our data indicate that the adult cystinosis zebrafish model reproduces several human phenotypes of cystinosis and may be useful for studying pathophysiology and long-term effects of novel therapies.

Contactless Video-Based Heart Rate Monitoring of a Resting and an Anesthetized Pig.

Heart rate (HR) is a vital bio-signal that is relatively easy to monitor with contact sensors and is related to a living organism's state of health, stress and well-being. The objective of this study was to develop an algorithm to extract HR (in beats per minute) of an anesthetized and a resting pig from raw video data as a first step towards continuous monitoring of health and welfare of pigs. Data were obtained from two experiments, wherein the pigs were video recorded whilst wearing an electrocardiography (ECG) monitoring system as gold standard (GS). In order to develop the algorithm, this study used a bandpass filter to remove noise. Then, a short-time Fourier transform (STFT) method was tested by evaluating different window sizes and window functions to accurately identify the HR. The resulting algorithm was first tested on videos of an anesthetized pig that maintained a relatively constant HR. The GS HR measurements for the anesthetized pig had a mean value of 71.76 bpm and standard deviation (SD) of 3.57 bpm. The developed algorithm had 2.33 bpm in mean absolute error (MAE), 3.09 bpm in root mean square error (RMSE) and 67% in HR estimation error below 3.5 bpm. The sensitivity of the algorithm was then tested on the video of a non-anaesthetized resting pig, as an animal in this state has more fluctuations in HR than an anaesthetized pig, while motion artefacts are still minimized due to resting. The GS HR measurements for the resting pig had a mean value of 161.43 bpm and SD of 10.11 bpm. The video-extracted HR showed a performance of 4.69 bpm in MAE, 6.43 bpm in RMSE and 57% in . The results showed that HR monitoring using only the green channel of the video signal was better than using three color channels, which reduces computing complexity. By comparing different regions of interest (ROI), the region around the abdomen was found physiologically better than the face and front leg parts. In summary, the developed algorithm based on video data has potential to be used for contactless HR measurement and may be applied on resting pigs for real-time monitoring of their health and welfare status, which is of significant interest for veterinarians and farmers.

Managing respiratory disease in finisher pigs: Combining quantitative assessments of clinical signs and the prevalence of lung lesions at slaughter.

This study aimed to assess the relationship between quantitative assessments of clinical signs of respiratory disease (recorded manually and automatically) and the prevalence of lung lesions at slaughter to validate the use of both in the management of respiratory disease on farm. This was an observational study where pigs (n = 1573) were monitored from 25 ± 5.3 kg (week 12) to slaughter at 114 ± 15.4 kg (week 24). Pigs were housed in eight rooms divided into six pens on a wean-to-finish farm. A manual pen-based coughing (CF) and sneezing (SF) frequency was recorded weekly, for ten consecutive weeks, and a SOMO box (SoundTalks®) was installed in each room, issuing a daily respiratory distress index (RDI) for 13 weeks. Lungs were individually scored for pneumonia, scarring and dorsocaudal (DC) and cranial (CP) pleurisy lesions at slaughter. Relationship between prevalence of lung lesions and weekly RDI and CF and SF was assessed using Spearman's rank correlations and multivariable linear and logit-normal models. Both coughing and lung lesions were largely pen-specific, which fit the disease presentation of Mycoplasma hyopneumoniae. Results showed agreement between RDI and CF (rs = 0.5, P < 0.001), measuring higher levels of coughing at the beginning (weeks 13-14) and end (weeks 21-24, and weeks 21-22, respectively) of the finisher period. Positive associations were found between the prevalence of pneumonia and CF on week 21 and 22 (P < 0.001 and P = 0.011, respectively) and RDI on week 21-24 (rs > 0.70; P < 0.050); the prevalence of DC and CP, and CF on week 22 (P < 0.001); and prevalence of scar lesions and CF on week 17 and 21 (P = 0.013 and P = 0.004, respectively), and RDI on week 21-24 (rs > 0.70; P < 0.050). In the earlier weeks of the finisher stage, coughing was recorded but was not reflected in a higher prevalence of lung lesions at slaughter. These findings highlight the benefit of including measurements of coughing frequency to complement post mortem findings, to improve the management of respiratory disease on farm.

Possibilities for IPM Strategies in European Laying Hen Farms for Improved Control of the Poultry Red Mite (Dermanyssus gallinae): Details and State of Affairs.

The Poultry Red Mite (PRM), Dermanyssus gallinae, is a major threat to the poultry industry worldwide, causing serious problems to animal health and welfare, and huge economic losses. Controlling PRM infestations is very challenging. Conventionally, D. gallinae is treated with synthetic acaricides, but the particular lifestyle of the mite (most of the time spent off the host) makes the efficacy of acaracide sprays often unsatisfactory, as sprays reach only a small part of the population. Moreover, many acaricides have been unlicensed due to human consumer and safety regulations and mites have become resistant to them. A promising course of action is Integrated Pest Management (IPM), which is sustainable for animals, humans and the environment. It combines eight different steps, in which prevention of introduction and monitoring of the pest are key. Further, it focusses on non-chemical treatments, with chemicals only being used as a last resort. Whereas IPM is already widely applied in horticulture, its application is still in its infancy to control D. gallinae in layer houses. This review presents the currently-available possibilities for control of D. gallinae in layer houses for each of the eight IPM steps, including monitoring techniques, established and emerging non-chemical treatments, and the strategic use of chemicals. As such, it provides a needed baseline for future development of specific IPM strategies, which will allow efficient and sustainable control of D. gallinae in poultry farms.

Non-Invasive PPG-Based System for Continuous Heart Rate Monitoring of Incubated Avian Embryo.

The chicken embryo is a widely used experimental animal model in many studies, including in the field of developmental biology, of the physiological responses and adaptation to altered environments, and for cancer and neurobiology research. The embryonic heart rate is an important physiological variable used as an index reflecting the embryo's natural activity and is considered one of the most difficult parameters to measure. An acceptable measurement technique of embryonic heart rate should provide a reliable cardiac signal quality while maintaining adequate gas exchange through the eggshell during the incubation and embryonic developmental period. In this paper, we present a detailed design and methodology for a non-invasive photoplethysmography (PPG)-based prototype (Egg-PPG) for real-time and continuous monitoring of embryonic heart rate during incubation. An automatic embryonic cardiac wave detection algorithm, based on normalised spectral entropy, is described. The developed algorithm successfully estimated the embryonic heart rate with 98.7% accuracy. We believe that the system presented in this paper is a promising solution for non-invasive, real-time monitoring of the embryonic cardiac signal. The proposed system can be used in both experimental studies (e.g., developmental embryology and cardiovascular research) and in industrial incubation applications.

An Approach towards Motion-Tolerant PPG-Based Algorithm for Real-Time Heart Rate Monitoring of Moving Pigs.

Animal welfare remains a very important issue in the livestock sector, but monitoring animal welfare in an objective and continuous way remains a serious challenge. Monitoring animal welfare, based upon physiological measurements instead of the audio-visual scoring of behaviour, would be a step forward. One of the obvious physiological signals related to welfare and stress is heart rate. The objective of this research was to measure heart rate (beat per minutes) in pigs with technology that soon will be affordable. Affordable heart rate monitoring is done today at large scale on humans using the Photo Plethysmography (PPG) technology. We used PPG sensors on a pig's body to test whether it allows the retrieval of a reliable heart rate signal. A continuous wavelet transform (CWT)-based algorithm is developed to decouple the cardiac pulse waves from the pig. Three different wavelets, namely second, fourth and sixth order Derivative of Gaussian (DOG), are tested. We show the results of the developed PPG-based algorithm, against electrocardiograms (ECG) as a reference measure for heart rate, and this for an anaesthetised versus a non-anaesthetised animal. We tested three different anatomical body positions (ear, leg and tail) and give results for each body position of the sensor. In summary, it can be concluded that the agreement between the PPG-based heart rate technique and the reference sensor is between 91% and 95%. In this paper, we showed the potential of using the PPG-based technology to assess the pig's heart rate.

Assessment of Laying Hens' Thermal Comfort Using Sound Technology.

Heat stress is one of the most important environmental stressors facing poultry production and welfare worldwide. The detrimental effects of heat stress on poultry range from reduced growth and egg production to impaired health. Animal vocalisations are associated with different animal responses and can be used as useful indicators of the state of animal welfare. It is already known that specific chicken vocalisations such as alarm, squawk, and gakel calls are correlated with stressful events, and therefore, could be used as stress indicators in poultry monitoring systems. In this study, we focused on developing a hen vocalisation detection method based on machine learning to assess their thermal comfort condition. For extraction of the vocalisations, nine source-filter theory related temporal and spectral features were chosen, and a support vector machine (SVM) based classifier was developed. As a result, the classification performance of the optimal SVM model was 95.1 ± 4.3% (the sensitivity parameter) and 97.6 ± 1.9% (the precision parameter). Based on the developed algorithm, the study illustrated that a significant correlation existed between specific vocalisations (alarm and squawk call) and thermal comfort indices (temperature-humidity index, THI) (alarm-THI, R = -0.414, P = 0.01; squawk-THI, R = 0.594, P = 0.01). This work represents the first step towards the further development of technology to monitor flock vocalisations with the intent of providing producers an additional tool to help them actively manage the welfare of their flock.

An Automatic Head Surface Temperature Extraction Method for Top-View Thermal Image with Individual Broiler.

Surface temperature variation in a broiler's head can be used as an indicator of its health status. Surface temperatures in the existing thermograph based animal health assessment studies were mostly obtained manually. 2185 thermal images, each of which had an individual broiler, were captured from 20 broilers. Where 15 broilers served as the experimental group, they were injected with 0.1mL of pasteurella inoculum. The rest, 5 broilers, served as the control group. An algorithm was developed to extract head surface temperature automatically from the top-view broiler thermal image. Adaptive K-means clustering and ellipse fitting were applied to locate the broiler's head region. The maximum temperature inside the head region was extracted as the head surface temperature. The developed algorithm was tested in Matlab® (R2016a) and the testing results indicated that the head region in 92.77% of the broiler thermal images could be located correctly. The maximum error of the extracted head surface temperatures was not greater than 0.1 °C. Different trend features were observed in the smoothed head surface temperature time series of the broilers in experimental and control groups. Head surface temperature extracted by the presented algorithm lays a foundation for the development of an automatic system for febrile broiler identification.

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review.

Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced.

Review of Sensor Technologies in Animal Breeding: Phenotyping Behaviors of Laying Hens to Select Against Feather Pecking.

Damaging behaviors, like feather pecking (FP), have large economic and welfare consequences in the commercial laying hen industry. Selective breeding can be used to obtain animals that are less likely to perform damaging behavior on their pen-mates. However, with the growing tendency to keep birds in large groups, identifying specific birds that are performing or receiving FP is difficult. With current developments in sensor technologies, it may now be possible to identify laying hens in large groups that show less FP behavior and select them for breeding. We propose using a combination of sensor technology and genomic methods to identify feather peckers and victims in groups. In this review, we will describe the use of "-omics" approaches to understand FP and give an overview of sensor technologies that can be used for animal monitoring, such as ultra-wideband, radio frequency identification, and computer vision. We will then discuss the identification of indicator traits from both sensor technologies and genomics approaches that can be used to select animals for breeding against damaging behavior.

Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling.

Sustainable freshwater management is underpinned by technologies which improve the efficiency of agricultural irrigation systems. Irrigation scheduling has the potential to incorporate real-time feedback from soil moisture and climatic sensors. However, for robust closed-loop decision support, models of the soil moisture dynamics are essential in order to predict crop water needs while adapting to external perturbation and disturbances. This paper presents a Dynamic Neural Network approach for modelling of the temporal soil moisture fluxes. The models are trained to generate a one-day-ahead prediction of the volumetric soil moisture content based on past soil moisture, precipitation, and climatic measurements. Using field data from three sites, a R 2 value above 0.94 was obtained during model evaluation in all sites. The models were also able to generate robust soil moisture predictions for independent sites which were not used in training the models. The application of the Dynamic Neural Network models in a predictive irrigation scheduling system was demonstrated using AQUACROP simulations of the potato-growing season. The predictive irrigation scheduling system was evaluated against a rule-based system that applies irrigation based on predefined thresholds. Results indicate that the predictive system achieves a water saving ranging between 20 and 46% while realizing a yield and water use efficiency similar to that of the rule-based system.

Methodologies for Assessing Disease Tolerance in Pigs.

Features of intensive farming can seriously threaten pig homeostasis, well-being and productivity. Disease tolerance of an organism is the adaptive ability in preserving homeostasis and at the same time limiting the detrimental impact that infection can inflict on its health and performance without affecting pathogen burden per se. While disease resistance (DRs) can be assessed measuring appropriately the pathogen burden within the host, the tolerance cannot be quantified easily. Indeed, it requires the assessment of the changes in performance as well as the changes in pathogen burden. In this paper, special attention is given to criteria required to standardize methodologies for assessing disease tolerance (DT) in respect of infectious diseases in pigs. The concept is applied to different areas of expertise and specific examples are given. The basic physiological mechanisms of DT are reviewed. Disease tolerance pathways, genetics of the tolerance-related traits, stress and disease tolerance, and role of metabolic stress in DT are described. In addition, methodologies based on monitoring of growth and reproductive performance, welfare, emotional affective states, sickness behavior for assessment of disease tolerance, and methodologies based on the relationship between environmental challenges and disease tolerance are considered. Automated Precision Livestock Farming technologies available for monitoring performance, health and welfare-related measures in pig farms, and their limitations regarding DT in pigs are also presented. Since defining standardized methodologies for assessing DT is a serious challenge for biologists, animal scientists and veterinarians, this work should contribute to improvement of health, welfare and production in pigs.