Comparison of Machine Learning Tree-Based Algorithms to Predict Future Paratuberculosis ELISA Results Using Repeat Milk Tests.

Machine learning algorithms have been applied to various animal husbandry and veterinary-related problems; however, its use in Johne's disease diagnosis and control is still in its infancy. The following proof-of-concept study explores the application of tree-based (decision trees and random forest) algorithms to analyze repeat milk testing data from 1197 Canadian dairy cows and the algorithms' ability to predict future Johne's test results. The random forest models using milk component testing results alongside past Johne's results demonstrated a good predictive performance for a future Johne's ELISA result with a dichotomous outcome (positive vs. negative). The final random forest model yielded a kappa of 0.626, a roc AUC of 0.915, a sensitivity of 72%, and a specificity of 98%. The positive predictive and negative predictive values were 0.81 and 0.97, respectively. The decision tree models provided an interpretable alternative to the random forest algorithms with a slight decrease in model sensitivity. The results of this research suggest a promising avenue for future targeted Johne's testing schemes. Further research is needed to validate these techniques in real-world settings and explore their incorporation in prevention and control programs.

A qualitative survey approach to investigating beef and dairy veterinarians' needs in relation to technologies on farms.

Globally, farmers are being increasingly encouraged to use technologies. Consequently, veterinarians often use farm data and technologies to provide farmers with advice. Yet very few studies have sought to understand veterinarians' perceptions of data and technologies on farms. The aim of this study was to understand veterinarians' experiences and opinions on data and technology on beef and dairy farms. An online qualitative survey was conducted with a convenience sample of 36 and 24 veterinarians from the United Kingdom and Ireland, respectively. The data were analysed using reflexive thematic analysis to generate four themes: (1) Improving veterinary advice through data; (2) Ensuring stock person skills are retained; (3) Longevity of technology; and (4) Solving social problems on farms. We show that technologies and data can make veterinarians feel more confident in the advice they give to farmers. However, the quality and quantity of data collected on cattle farms were highly variable. Furthermore, veterinarians were concerned that farmers can become over-reliant on technologies by not using their stockperson skills. As herd sizes increase, technologies can help to improve working conditions on farms with multiple employees of various skillsets. Veterinarians would like innovations that can help them to demonstrate their competence, influence farmers' behaviour, and ensure sustainability of the beef and dairy industries.

The Relation between Milk Lactose Concentration and the Rumination, Feeding, and Locomotion Behavior of Early-Lactation Dairy Cows.

This study hypothesizes that higher in-line milk lactose concentrations are indicative of enhanced dairy cow behaviors-including increased rumination, feeding, and locomotion activities-reflecting superior overall health and well-being. It posits that fluctuations in milk lactose levels have a substantial impact on the physiological and behavioral responses of dairy cows, thereby affecting their milk yields and compositions. Each cow's milk lactose, fat, protein, and fat-to-protein ratio were continuously monitored using the BROLIS HerdLine in-line milk analyzer (Brolis Sensor Technology, Vilnius, Lithuania). The RumiWatch noseband sensor (RWS; ITIN + HOCH GmbH, Fütterungstechnik, Liestal, Switzerland) was employed to measure the biomarkers of the rumination, feeding, and locomotion behavior. The measurements were recorded over 5 days at the same time (during morning milking). A total of 502 cows were examined. During these 5 days, 2510 measurements were taken. Based on the lactose content in their milk, the cows were divided into two categories: the first group consisted of cows with milk lactose levels below 4.70%, while the second group included cows with milk lactose levels of 4.70% or higher. Our study showed that cows with higher milk lactose concentrations (≥4.70%) produced significantly more milk (16.14% increase) but had a lower milk protein concentration (5.05% decrease) compared to cows with lower lactose levels. These cows also exhibited changes in rumination and feeding behaviors, as recorded by the RWS: there was an increase in the mastication and rumination behaviors, evidenced by a 14.09% rise in other chews and a 13.84% increase in rumination chews, along with a 16.70% boost in bolus activity. However, there was a notable 16.18% reduction in their physical activity, as measured by the change in time spent walking.

Alterations in Rumination, Eating, Drinking and Locomotion Behavior in Dairy Cows Affected by Subclinical Ketosis and Subclinical Acidosis.

This study delves into the effects of subclinical ketosis (SCK) and subclinical acidosis (SCA) on various parameters related to dairy cow rumination, eating, drinking and locomotion behavior. The research hypothesized that these subclinical metabolic disorders could affect behaviors such as rumination, feeding, and locomotion. A total of 320 dairy cows, with a focus on those in their second or subsequent lactation, producing an average of 12,000 kg/year milk in their previous lactation, were examined. These cows were classified into three groups: those with SCK, those with SCA, and healthy cows. The health status of the cows was determined based on the milk fat-protein ratio, blood beta-hydroxybutyrate, and the results of clinical examinations performed by a veterinarian. The data collected during the study included parameters from the RumiWatch sensors. The results revealed significant differences between the cows affected by SCK and the healthy cows, with reductions observed in the rumination time (17.47%) and various eating and chewing behaviors. These changes indicated that SCK had a substantial impact on the cows' behavior. In the context of SCA, the study found significant reductions in Eating Time 2 (ET2) of 36.84% when compared to the healthy cows. Additionally, Eating Chews 2 (EC2) exhibited a significant reduction in the SCA group, with an average of 312.06 units (±17.93), compared to the healthy group's average of 504.20 units (±18.87). These findings emphasize that SCA influences feeding behaviors and chewing activity, which can have implications for nutrient intake and overall cow health. The study also highlights the considerable impact of SCK on locomotion parameters, as the cows with SCK exhibited a 27.36% reduction in the walking time levels. These cows also displayed reductions in the Walking Time (WT), Other Activity Time (OAT), and Activity Change (AC). In conclusion, this research underscores the critical need for advanced strategies to prevent and manage subclinical metabolic disorders within the dairy farming industry. The study findings have far-reaching implications for enhancing the well-being and performance of dairy cattle. Effective management practices and detection methods are essential to mitigate the impact of SCK and SCA on dairy cow health and productivity, ultimately benefiting the dairy farming sector.

Research on Bacterial Diversity and Antibiotic Resistance in the Dairy Farm Environment in a Part of Shandong Province.

Antimicrobials are extensively utilized in dairy farms to prevent and control diseases in cattle. However, their use contributes to the emergence of antimicrobial-resistant bacteria (ARB) and antimicrobial-resistant genes (ARG), and these can be transmitted to the environment. Regular monitoring of antimicrobial resistance (AMR) is crucial for implementing effective mitigation strategies. This research aimed to assess the environmental microbial species present on dairy farms in Shandong Province and characterize the antimicrobial resistance profiles of the isolates. Five dairy farms located in Shandong Province were selected, representing the prevalent large-scale farming patterns in the area. Sampling took place from April to June 2022, with a total of 223 isolates collected from various environmental locations within each farm (bedding, sports field, and milking parlor). Matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) was employed to identify the species of the clinical isolates. The main pathogens isolated were Aerococcus viridans (5.38%, n = 12), Corynebacterium xerosis (4.93%, n = 11), and Acinetobacter lwoffii (4.03%, n = 9). Among the bacterial isolates, resistance to lincomycin was highest at 91%, and 88% were resistant to sulfadiazine. Antimicrobial resistance genes were detected in only a small proportion of the isolates, the most common of which was sul1. These findings highlight the necessity for careful evaluation of antimicrobial usage in maintaining their effectiveness in human medicine. Understanding the microbial species present and their antimicrobial resistance profiles aids in focusing efforts toward sustainable antimicrobial use and safeguarding human health.

Data Driven Identification of Injury Risk Factors During Expansion on Irish Dairy Farms.

OBJECTIVES: This paper sought to determine risk factors of occupational injury in the Irish dairy farming sector and to estimate the roles of both dairying expansion and the discipline of agricultural extension in influencing reducing injury occurrence. METHODS: Data for this study was obtained via the Irish National Farm Survey (NFS). In total, 260 farm (83.6% of NFS dairy farm sample) workplace injury survey questionnaires were completed by NFS recorders by interviewing principal farm operators for year 2017. Injury survey data was analysed for 48 variables in conjunction with NFS core farm socio-economic, physical and financial data. Additionally, core data from 2010 for 78.5% of farms surveyed in 2017 was included in the study. Data were analysed using a three-step statistical testing process which met all Binary Logistic Regression assumptions. RESULTS: The study found that dairy farms had a higher farm workplace occupational injury level compared to a previous study. The study data indicates occurrence of elevated injury levels on farms which expanded and which were intensively managed from a milk production perspective. Farm expansion was associated with increased labor units used and increased investment per hectare. The study also found that use of extension services and farm discussion group participation were not associated with injury occurrence. CONCLUSIONS: This study demonstrates how a retrospective farm workplace occupational injury survey of Irish dairy farms, analysed in combination with farm business data can be used to identify injury risk factors, including those associated with production expansion. Irish dairy farms have a heightened farm workplace occupation injury level while dairy production expansion increased injury levels. Extension engagement did not influence on injury levels. Approaches to improve farm safety on dairy farms are outlined.

Functional analysis of the GPAT4 gene mutation predicted to affect splicing.

The GPAT4 gene is considered as a potential functional candidate for single nucleotide polymorphism (SNP) studies in dairy cattle breeding due to its association with dairy performance in cattle by encoding an enzyme responsible for the presence of diacylglycerols and triacylglycerols in milk. Using the example of the GPAT4 gene, we applied the minigene splicing assay to analyze the functional consequences of its variant that was predicted to affect normal splicing. The results of functional analysis revealed the sequence variations (rs442541537), transfection experiments in a wild type and mutant cell line model system demonstrated that the investigated mutation in the second intron of the GPAT4 gene was responsible for the presence of a second exon in mature messenger RNA (mRNA). The cases of its absence in the spliced mature mRNA transcript resulted in a truncated dysfunctional protein due to the appearance of a stop codon. Thus, the discovered SNP led to alternative splicing in pre-mRNA by the 'cassette exon' ('exon skipping') mechanism. The studied mutation can potentially be a molecular genetic marker for alternative splicing for the GPAT4 gene and, therefore contributes to economic benefits in cattle breeding programs.

Enteric methane emission factors of smallholder dairy farming systems across intensification gradients in the central highlands of Ethiopia.

BACKGROUND: Following global pledges to reduce greenhouse gas (GHG) emissions by 30% by 2030 compared to the baseline level of 2020, improved quantification of GHG emissions from developing countries has become crucial. However, national GHG inventories in most Sub-Saharan African countries use default (Tier I) emission factors (EFS) generated by the Intergovernmental Panel on Climate Change (IPCC) to estimate enteric CH4 emissions from animal agriculture. The present study provides an improved enteric CH4 emission estimate (Tier II) based on animal energy requirements derived from animal characteristics and performance data collected from about 2500 cattle in 480 households from three smallholder farming systems to represent the common dairy farming in the central highlands of Ethiopia. Using average seasonal feed digestibility data, we estimated daily methane production by class of animal and farming system and subsequently generated improved EF. RESULTS: Our findings revealed that the estimated average EF and emission intensities (EI) vary significantly across farming systems. The estimated value of EF for adult dairy cows was 73, 69, and 34 kg CH4/cow/year for urban, peri-urban, and rural farming systems, respectively. Rural dairy farming had significantly higher emission intensity (EI) estimated at 1.78 CO2-eq per kg of fat protein-corrected milk (FPCM) than peri-urban and urban 0.71 and 0.64 CO2-eq kg-1 FPCM dairy farming systems, respectively. The EF estimates in this study are lower than the IPCC's (2019) default value for both stall-fed high-productive and dual-purpose low-productive cows. CONCLUSIONS: The current findings can be used as a baseline for the national emission inventory, which can be used to quantify the effects of future interventions, potentially improving the country's commitment to reducing GHG emissions. Similarly, this study suggests that increased animal productivity through improved feed has a considerable mitigation potential for reducing enteric methane emissions in smallholder dairy farming systems in the study area.

Genetic parameters for calf feeding traits derived from automated milk feeding machines and number of bovine respiratory disease treatments in North American Holstein calves.

Precision Livestock Farming technologies have increased the availability of on-farm data collected from dairy operations, such as automatic milk feeding machines. We analyzed feeding records from AMF to evaluate the genetic background of milk feeding traits and bovine respiratory disease (BRD) in North American Holstein calves. Data from 10,076 pre-weaned female Holstein calves were collected daily over a period of 6 years (3 years included per-visit data) and daily milk consumption (DMC) and per-visit milk consumption (PVMC), daily sum of drinking duration (DSDD), drinking duration per-visit (DDPV), daily number of rewarded visits (DNRV), and total number of visits per day (TNV) were recorded over a 60-d pre-weaning period. Additional traits were derived from these variables, including total consumption and duration variance (TDC and TDV), feeding interval, drinking speed (DS), and pre-weaning stayability. A single BRD-related trait was evaluated, which was the number of times a calf was treated for BRD (NTT). NTT was determined by counting the number of BRD incidences before 60 d of age. All traits were analyzed using single-step GBLUP mixed-model equations and fitting either repeatability or random regression models in the BLUPF90+ suite of programs. A total of 10,076 calves with phenotypic records and genotypic information for 57,019 single nucleotide polymorphisms after the quality control were included in the analyses. Feeding traits had low heritability estimates based on repeatability models [0.006 ± 0.0009 to 0.08 ± 0.004]. However, total variance traits using an animal model had greater heritabilities of 0.21 ± 0.023 and 0.23 ± 0.024, for TCV and TDV, respectively. The heritability estimates increased with the repeatability model when using only the first 32 d pre-weaning (e.g., PVMC = 0.040 ± 0.003, DMC = 0.090 ± 0.009, DSDD = 0.100 ± 0.005, DS = 0.150 ± 0.007, DNRV = 0.020 ± 0.002). When fitting random regression models (RRM) using the full data set (60-d period), greater heritability estimates were obtained (e.g., PVMC = 0.070 [range: 0.020, 0.110], DMC = 0.460 [range: 0.050, 0.680], DSDD = 0.180 [range: 0.010, 0.340], DS = 0.19 [range: 0.070, 0.430], DNRV = 0.120 [range: 0.030, 0.450]) for the majority of the traits, suggesting that random regression models capture more genetic variability than the repeatability model with better fit being found for RRM. Moderate negative genetic correlations of -0.59 between DMC and NTT were observed, suggesting that automatic milk feeding machines records have the potential to be used for genetically improving disease resilience in Holstein calves. The results from this study provide key insights of the genetic background of early in-life traits in dairy cattle, which can be used for selecting animals with improved health outcomes and performance.

In-Line Registered Milk Fat-to-Protein Ratio for the Assessment of Metabolic Status in Dairy Cows.

This study endeavors to ascertain alterations in the in-line registered milk fat-to-protein ratio as a potential indicator for evaluating the metabolic status of dairy cows. Over the study period, farm visits occurred biweekly on consistent days, during which milk composition (specifically fat and protein) was measured using a BROLIS HerdLine in-line milk analyzer (Brolis Sensor Technology, Vilnius, Lithuania). Clinical examinations were performed at the same time as the farm visits. Blood was drawn into anticoagulant-free evacuated tubes to measure the activities of GGT and AST and albumin concentrations. NEFA levels were assessed using a wet chemistry analyzer. Using the MediSense and FreeStyle Optium H systems, blood samples from the ear were used to measure the levels of BHBA and glucose in plasma. Daily blood samples were collected for BHBA concentration assessment. All samples were procured during the clinical evaluations. The cows were categorized into distinct groups: subclinical ketosis (SCK; n = 62), exhibiting elevated milk F/P ratios without concurrent clinical signs of other post-calving diseases; subclinical acidosis (SCA; n = 14), characterized by low F/P ratios (<1.2), severe diarrhea, and nondigestive food remnants in feces, while being free of other post-calving ailments; and a healthy group (H; n = 20), comprising cows with no clinical indications of illness and an average milk F/P ratio of 1.2. The milk fat-to-protein ratios were notably higher in SCK cows, averaging 1.66 (±0.29; p < 0.01), compared to SCA cows (0.93 ± 0.1; p < 0.01) and healthy cows (1.22). A 36% increase in milk fat-to-protein ratio was observed in SCK cows, while SCA cows displayed a 23.77% decrease. Significant differences emerged in AST activity, with SCA cows presenting a 26.66% elevation (p < 0.05) compared to healthy cows. Moreover, SCK cows exhibited a 40.38% higher NEFA concentration (p < 0.001). A positive correlation was identified between blood BHBA and NEFA levels (r = 0.321, p < 0.01), as well as a negative association between BHBA and glucose concentrations (r = -0.330, p < 0.01). Notably, AST displayed a robust positive correlation with GGT (r = 0.623, p < 0.01). In light of these findings, this study posits that milk fat-to-protein ratio comparisons could serve as a non-invasive indicator of metabolic health in cows. The connections between milk characteristics and blood biochemical markers of lipolysis and ketogenesis suggest that these markers can be used to check the metabolic status of dairy cows on a regular basis.

Classifying Chewing and Rumination in Dairy Cows Using Sound Signals and Machine Learning.

This research paper introduces a novel methodology for classifying jaw movements in dairy cattle into four distinct categories: bites, exclusive chews, chew-bite combinations, and exclusive sorting, under conditions of tall and short particle sizes in wheat straw and Alfalfa hay feeding. Sound signals were recorded and transformed into images using a short-time Fourier transform. A total of 31 texture features were extracted using the gray level co-occurrence matrix, spatial gray level dependence method, gray level run length method, and gray level difference method. Genetic Algorithm (GA) was applied to the data to select the most important features. Six distinct classifiers were employed to classify the jaw movements. The total precision found was 91.62%, 94.48%, 95.9%, 92.8%, 94.18%, and 89.62% for Naive Bayes, k-nearest neighbor, support vector machine, decision tree, multi-layer perceptron, and k-means clustering, respectively. The results of this study provide valuable insights into the nutritional behavior and dietary patterns of dairy cattle. The understanding of how cows consume different types of feed and the identification of any potential health issues or deficiencies in their diets are enhanced by the accurate classification of jaw movements. This information can be used to improve feeding practices, reduce waste, and ensure the well-being and productivity of the cows. The methodology introduced in this study can serve as a valuable tool for livestock managers to evaluate the nutrition of their dairy cattle and make informed decisions about their feeding practices.

Assessment of the prevalence, serotype, and antibiotic resistance pattern of Salmonella enterica in integrated farming systems in the Maryland-DC area.

Implementation of organic/pasture farming practices has been increasing in the USA regardless of official certification. These practices have created an increasingly growing demand for marketing safe products which are produced through these systems. Products from these farming systems have been reported to be at greater risk of transmitting foodborne pathogens because of current trends in their practices. Salmonella enterica (SE) is a ubiquitous foodborne pathogen that remains a public health issue given its prevalence in various food products, but also in the environment and as part of the microbial flora of many domestic animals. Monitoring antibiotic resistance and identifying potential sources contamination are increasingly important given the growing trend of organic/pasture markets. This study aimed to quantify prevalence of SE at the pre- and post-harvest levels of various integrated farms and sites in Maryland-Washington D.C. area, as well as identify the most prevalent serovars and antibiotic resistance patterns. Samples from various elements within the farm environment were collected and screened for SE through culture and molecular techniques, which served to identify and serotype SE, using species and serovar-specific primers, while antibiotic resistance was evaluated using an antibiogram assay. Results showed a prevalence of 7.80% of SE pre-harvest and 1.91% post-harvest. These results also showed the main sources of contamination to be soil (2.17%), grass (1.28%), feces (1.42%) and unprocessed produce (1.48%). The most commonly identified serovar was Typhimurium (11.32%) at the pre-harvest level, while the only identified serovar from post-harvest samples was Montevideo (4.35%). With respect to antibiotic resistance, out of the 13 clinically relevant antibiotics tested, gentamycin and kanamycin were the most effective, demonstrating 78.93 and 76.40% of isolates, respectively, to be susceptible. However, ampicillin, amoxicillin and cephradine had the lowest number of susceptible isolates with them being 10.95, 12.36, and 9.83%, respectively. These results help inform farms striving to implement organic practices on how to produce safer products by recognizing areas that pose greater risks as potential sources of contamination, in addition to identifying serotypes of interest, while also showcasing the current state of antibiotic efficacy and how this can influence antibiotic resistance trends in the future.

Shaping cow-calf contact systems: Farmers' motivations and considerations behind a range of different cow-calf contact systems.

Most dairy farms rear calves artificially by separating the newly born calf from the dam and feeding the calf milk from a bucket. However, the general public and scientific community have begun to question the effects of artificial rearing on animal welfare. Research so far has focused mainly on dam-calf contact, where each cow takes care of her own calf. However, previous studies show that Danish and other European farmers are using and showing interest in a variety of different cow-calf contact (CCC) systems. In the present study, we used qualitative research methods to explore the perspectives of Danish farmers who either had or had tried to establish a version of a CCC system. Farmers were asked about their motivation for establishing the system, what had shaped the system to its current form, and how they perceived the calves to benefit from the system. Practical considerations was the theme most commonly brought up and related to both why farmers chose to have CCC in the first place and in what way they had chosen to organize their CCC system. Practical considerations included a sense of ease, flexibility, and a more natural and therefore rational approach. The economy was also a repeated theme, but although the economy to a large degree shapes the type of CCC chosen (dam-calf contact or foster cow contact), it was rarely mentioned in relation to choosing a CCC system in the first place. Ethical considerations were a strong motivator for farmers with dam-calf contact systems, although less so for farmers with foster cow contact. The farm's image as seen by the consumer was an important motivation for farmers with many on-farm visitors, and with the farm's image in the eyes of the farming community potentially also influencing farmers. Farmers generally perceived the calves to benefit from the care of the cow and no difference was seen in the importance attributed to care, between farmers choosing dam-calf contact and foster cow systems.

Geographical trends for automatic milking systems research in non-pasture-based dairy farms: A scoping review.

Automatic milking system (AMS) adoption in the United States is trending upward, with issues such as lower availability and increased cost of labor being factors frequently listed as motives for AMS implementation. In addition, more interest in precision dairy farming by the new generation of farmers may also help increase AMS adoption. The objective of this scoping review was to characterize the nature of the literature investigating non-pasture-based AMS and the opportunities and challenges for future research. The eligibility criteria included studies published in or after the year 2000, with full text in English, of at least 500 words, examining various outcomes related to AMS in non-pasture-based dairy farms. Six electronic databases were searched: Biosis (Web of Science), CAB Abstracts (CAB Direct), Medline (PubMed), PubAg, AGRIS (FAO), and Scopus (Elsevier). The review focused on studies with objectives, characteristics, farms, and AMS information. A total of 4,292 titles and abstracts were screened, and 536 studies were finally included. Most of the studies were conducted in Europe (73.5%), among commercial herds (67.9%), comprising Holstein cows (57.7%), using Lely and DeLaval brands (45.4% vs. 39.7%), with free-flow traffic (52.7%). The main research topics investigated were milk production, milk composition, and AMS efficiency, followed by behavior and welfare, health disorders (especially mastitis), and nutrition in Europe and other regions. At the same time, in the United States, trends were similar, except for nutrition. Since 2016, there has been an increased interest in studies on energy and water consumption, technological development, environment (enteric emissions), reproduction, genetics, and longevity or culling. However, the small number of studies and unclear characterization of what is optimum for reproductive management, other health disorders, economics, and water and energy consumption suggest a need for future research.

Perceptions of farming stakeholders towards automating dairy cattle mobility and body condition scoring in farm assurance schemes.

Animal welfare standards are used within the food industry to demonstrate efforts in reaching higher welfare on farms. To verify compliance with those standards, inspectors conduct regular on-farm animal welfare assessments. Conducting these welfare assessments can, however, be time-consuming and prone to human bias. The emergence of Digital Livestock Technologies (DLTs) offers new ways of monitoring farm animal welfare and can alleviate some of the challenges related to animal welfare assessments by collecting data automatically and more frequently. Whilst automating welfare assessments with DLTs may be promising, little attention has been paid to farmers' perceptions of the challenges that could prevent successful implementation. This study aims to address this gap by focusing on the trial of a DLT (a 3D machinelearning camera) to automate mobility and body condition scoring on 11 dairy cattle farms. Semi-structured, in-depth interviews were conducted with farmers, technology developers and a stakeholder involved in a farm assurance scheme (N14). Findings suggest that stakeholders perceived important benefits to the use of the camera in this context, from building consumer trust by increasing transparency to improved management efficiency. There was also a potential for greater consistency in data collection and thus for enhanced fairness across the UK dairy sector, particularly on the issue of lameness prevalence. However, stakeholders also raised important concerns, such as a lack of clarity around data ownership, reliability, and use, and the possibility of some farmers being penalised (e.g., if the technology failed to work). More clarity should thus be given to farmers in relation to data governance and evidence provided in terms of technical performance and accuracy. The findings of this study highlighted the need for more inclusive approaches to ensure farmers' concerns are adequately identified and addressed. These approaches can help minimise negative consequences to farmers and animal welfare, whilst maximising the potential benefits of automating welfare-related data collection.

Ruminating, Eating, and Locomotion Behavior Registered by Innovative Technologies around Calving in Dairy Cows.

The hypothesis for this study was that there are correlations between ruminating, eating, and locomotion behavior parameters registered by the RumiWatch sensors (RWS) before and after calving. The aim was to identify correlations between registered indicators, namely, rumination, eating, and locomotion behavior around the calving period. Some 54 multiparous cows were chosen from the entire herd without previous calving or other health problems. The RWS system recorded a variety of parameters such as rumination time, eating time, drinking time, drinking gulps, bolus, chews per minute, chews per bolus, activity up and down time, temp average, temp minimum, temp maximum, activity change, other chews, ruminate chews, and eating chews. The RWS sensors were placed on the cattle one month before expected calving based on service data and removed ten days after calving. Data were registered 10 days before and 10 days after calving. We found that using the RumiWatch system, rumination time was not the predictor of calving outlined in the literature; rather, drinking time, downtime, and rumen chews gave the most clearcut correlation with the calving period. We suggest that using RumiWatch to combine rumination time, eating time, drinking, activity, and down time characteristics from ten days before calving, it would be possible to construct a sensitive calving alarm; however, considerably more data are needed, not least from primiparous cows not examined here.

Innovations in Cattle Farming: Application of Innovative Technologies and Sensors in the Diagnosis of Diseases.

Precision livestock farming has a crucial function as farming grows in significance. It will help farmers make better decisions, alter their roles and perspectives as farmers and managers, and allow for the tracking and monitoring of product quality and animal welfare as mandated by the government and industry. Farmers can improve productivity, sustainability, and animal care by gaining a deeper understanding of their farm systems as a result of the increased use of data generated by smart farming equipment. Automation and robots in agriculture have the potential to play a significant role in helping society fulfill its future demands for food supply. These technologies have already enabled significant cost reductions in production, as well as reductions in the amount of intensive manual labor, improvements in product quality, and enhancements in environmental management. Wearable sensors can monitor eating, rumination, rumen pH, rumen temperature, body temperature, laying behavior, animal activity, and animal position or placement. Detachable or imprinted biosensors that are adaptable and enable remote data transfer might be highly important in this quickly growing industry. There are already multiple gadgets to evaluate illnesses such as ketosis or mastitis in cattle. The objective evaluation of sensor methods and systems employed on the farm is one of the difficulties presented by the implementation of modern technologies on dairy farms. The availability of sensors and high-precision technology for real-time monitoring of cattle raises the question of how to objectively evaluate the contribution of these technologies to the long-term viability of farms (productivity, health monitoring, welfare evaluation, and environmental effects). This review focuses on biosensing technologies that have the potential to change early illness diagnosis, management, and operations for livestock.

Production in peatlands: Comparing ecosystem services of different land use options following conventional farming.

Majority of Dutch peatlands are drained and used intensively as grasslands for dairy farming. This delivers high productivity but causes severe damage to ecosystem services supply. Peatland rewetting is the best way to reverse the damage, but high water levels do not fit with intensive dairy production. Paludiculture, defined as crop production under wet conditions, provides viable land use alternatives. However, performance of paludiculture is rarely compared to drainage-based agriculture. Here, we compared the performances of six land use options on peatland following a gradient of low, medium, and high water levels, including conventional and organic drainage-based dairy farming, low-input grasslands for grazing and mowing, and high-input paludiculture with reed and Sphagnum cultivation. For each land use option, we conducted environmental system analysis on model farm system defined by a literature based inventory analysis. The analysis used five ecosystem services as indicators of environmental impacts with a functional unit of 1-ha peat soil. Ecosystem services included biomass provisioning, climate, water, and nutrient regulation, and maintenance of habitat. Results showed that drainage-based dairy farming systems support high provisioning services but low regulation and maintenance services. Organic farming provides higher climate and nutrient regulation services than its conventional counterpart, but limited overall improvement due to the persistent drainage. Low-intensity grassland and paludiculture systems have high regulation and maintenance services value, but do not supply biomass provisioning comparable to the drainage-based systems. Without capitalizing the co-benefits of regulation and maintenance services, and accounting for the societal costs from ecosystem disservices including greenhouse gas emission and nitrogen pollution, it is not likely that the farmers will be incentivized to change the current farming system towards the wetter alternatives. Sustainable use of peatlands urges fundamental changes in land and water management along with the financial and policy support required.

Mixtures of Mycotoxins, Phytoestrogens, and Other Secondary Metabolites in Whole-Plant Corn Silages and Total Mixed Rations of Dairy Farms in Central and Northern Mexico.

Mycotoxins and endocrine disruptors such as phytoestrogens can affect cattle health, reproduction, and productivity. Most studies of mycotoxins in dairy feeds in Mexico and worldwide have been focused on a few (regulated) mycotoxins. In contrast, less known fungal toxins, phytoestrogens, and other metabolites have been neglected and underestimated. This study analyzed a broad spectrum (>800) of mycotoxins, phytoestrogens, and fungal, plant, and unspecific secondary metabolites in whole-plant corn silages (WPCSs) and total mixed rations (TMRs) collected from 19 Mexican dairy farms. A validated multi-metabolite liquid chromatography/electrospray ionization-tandem mass spectrometric (LC/ESI-MS/MS) method was used. Our results revealed 125 of >800 tested (potentially toxic) secondary metabolites. WPCSs/TMRs in Mexico presented ubiquitous contamination with mycotoxins, phytoestrogens, and other metabolites. The average number of mycotoxins per TMR was 24, ranging from 9 to 31. Fusarium-derived secondary metabolites showed the highest frequencies, concentrations, and diversity among the detected fungal compounds. The most frequently detected mycotoxins in TMRs were zearalenone (ZEN) (100%), fumonisin B1 (FB1) (84%), and deoxynivalenol (84%). Aflatoxin B1 (AFB1) and ochratoxin A (OTA), previously reported in Mexico, were not detected. All TMR samples tested positive for phytoestrogens. Among the investigated dietary ingredients, corn stover, sorghum silage, and concentrate proportions were the most correlated with levels of total mycotoxins, fumonisins (Fs), and ergot alkaloids, respectively.

Factors Affecting Fatty Acid Composition of Holstein Cow's Milk.

Milk fatty acid composition has gained the interest of both manufacturers and consumers during recent years. The present paper aimed to perform an analysis of C14:0, C16:0, C18:0, C18:1, saturated (SFA), monounsaturated (MUFA), polyunsaturated (PUFA) and short chain fatty acid (SCFA) concentration in cow's milk in relation to the type of ration, parity, lactation phase and season. Cows' milk from animals being fed total mixed rations, including corn silage, had higher C14:0, C16:0 and SFA concentrations than those being fed pasture-based rations but lower concentrations of C18:0 and PUFA. Comparing to 1st parity cows, 2nd and 3rd parity animals had higher SFA and SCFA concentrations in milk. With respect to spring, C14:0, C16:0 and SFA concentrations increased in summer, autumn and winter while MUFA, PUFA and SCFA concentrations decreased. Considering the lactation phase, C14:0, C16:0 and SFA concentrations decreased in fresh cows with ketosis comparing to healthy fresh cows and increased in peak, mid and late lactation. C18:0, C18:1 and MUFA follow the opposite trend. The milk fatty acid profile varies significantly through the studied effects. The fact that the fatty acid profile is associated with animal health, organoleptic properties of milk or even methane production highlights the importance of studying factors that affect its variation.