Hasil untuk "Dairying"

Uche Mbaka, Michelle Carey

Traditional Functional Principal Component Analysis typically focuses on densely observed univariate functional data, yet many applications, particularly in longitudinal studies, involve multivariate functional data observed sparsely and irregularly across subjects. A common approach for extracting multivariate functional principal components in such settings relies on an eigen decomposition of univariate functional principal component scores to capture cross-component correlations. We propose a new approach for the estimation of multivariate functional principal components by improving the univariate eigenanalysis through maximum likelihood estimation combined with a modified Gram-Schmidt orthonormalization. The performance of the proposed approach is evaluated against two established methods, and its practical utility is demonstrated through an application to longitudinal cognitive biomarker data from an Alzheimer's disease study and a collection of data on dairy milk yield and milk compositions from research dairy farms in Ireland.

Parul Tiwari, Malavika Smitha, Hammed Olawale Fatoyinbo

Highly pathogenic avian influenza (HPAI) has expanded its host range with recent detections in dairy cattle, raising critical concerns regarding within-herd persistence and cross-species spillover. This study develops a stochastic $SEI_sI_aR-B$ compartmental model to analyse HPAI transmission, explicitly accounting for environmental pathogen reservoirs and noise intensities through Wiener processes. The positivity and boundedness of solutions are established, and the disease-free and endemic equilibria are analytically derived. The basic reproduction number is determined using the next-generation matrix method. Numerical simulations confirm that the model dynamics are consistent with theoretical analysis and illustrate how stochastic fluctuations significantly influence disease persistence. Furthermore, sensitivity analysis using Latin Hypercube Sampling (LHS) and Partial Rank Correlation Coefficients (PRCC) identifies the transmission rate from asymptomatic infectious cattle ($β_a$) as the primary driver of transmission. The model effectively captures the dynamics of environmental variability affecting HPAI spread, suggesting that effective control strategies must prioritise the early detection and isolation of asymptomatic carriers alongside environmental management.

Mian Ibad Ali Shah, Marcos Eduardo Cruz Victorio, Maeve Duffy et al.

The integration of renewable energy resources in rural areas, such as dairy farming communities, enables decentralized energy management through Peer-to-Peer (P2P) energy trading. This research highlights the role of P2P trading in efficient energy distribution and its synergy with advanced optimization techniques. While traditional rule-based methods perform well under stable conditions, they struggle in dynamic environments. To address this, Multi-Agent Reinforcement Learning (MARL), specifically Proximal Policy Optimization (PPO) and Deep Q-Networks (DQN), is combined with community/distributed P2P trading mechanisms. By incorporating auction-based market clearing, a price advisor agent, and load and battery management, the approach achieves significant improvements. Results show that, compared to baseline models, DQN reduces electricity costs by 14.2% in Ireland and 5.16% in Finland, while increasing electricity revenue by 7.24% and 12.73%, respectively. PPO achieves the lowest peak hour demand, reducing it by 55.5% in Ireland, while DQN reduces peak hour demand by 50.0% in Ireland and 27.02% in Finland. These improvements are attributed to both MARL algorithms and P2P energy trading, which together results in electricity cost and peak hour demand reduction, and increase electricity selling revenue. This study highlights the complementary strengths of DQN, PPO, and P2P trading in achieving efficient, adaptable, and sustainable energy management in rural communities.

Sibi Parivendan, Kashfia Sailunaz, Suresh Neethirajan

Precision livestock farming requires objective assessment of social behavior to support herd welfare monitoring, yet most existing approaches infer interactions using static proximity thresholds that cannot distinguish affiliative from agonistic behaviors in complex barn environments. This limitation constrains the interpretability of automated social network analysis in commercial settings. We present a pose-based computational framework for interaction classification that moves beyond proximity heuristics by modeling the spatiotemporal geometry of anatomical keypoints. Rather than relying on pixel-level appearance or simple distance measures, the proposed method encodes interaction-specific motion signatures from keypoint trajectories, enabling differentiation of social interaction valence. The framework is implemented as an end-to-end computer vision pipeline integrating YOLOv11 for object detection (mAP@0.50: 96.24%), supervised individual identification (98.24% accuracy), ByteTrack for multi-object tracking (81.96% accuracy), ZebraPose for 27-point anatomical keypoint estimation, and a support vector machine classifier trained on pose-derived distance dynamics. On annotated interaction clips collected from a commercial dairy barn, the classifier achieved 77.51% accuracy in distinguishing affiliative and agonistic behaviors using pose information alone. Comparative evaluation against a proximity-only baseline shows substantial gains in behavioral discrimination, particularly for affiliative interactions. The results establish a proof-of-concept for automated, vision-based inference of social interactions suitable for constructing interaction-aware social networks, with near-real-time performance on commodity hardware.

S. Sanjuan, D. A. Méndez, R. Arnau et al.

Heat stress is one of the main welfare and productivity problems faced by dairy cattle in Mediterranean climates. In this study, we approach the prediction of the daily shade-seeking count as a non-linear multivariate regression problem and evaluate two soft computing algorithms -- Random Forests and Neural Networks -- trained on high-resolution behavioral and micro-climatic data collected in a commercial farm in Titaguas (Valencia, Spain) during the 2023 summer season. The raw dataset (6907 daytime observations, 5-10 min resolution) includes the number of cows in the shade, ambient temperature and relative humidity. From these we derive three features: current Temperature--Humidity Index (THI), accumulated daytime THI, and mean night-time THI. To evaluate the models' performance a 5-fold cross-validation is also used. Results show that both soft computing models outperform a single Decision Tree baseline. The best Neural Network (3 hidden layers, 16 neurons each, learning rate = 10e-3) reaches an average RMSE of 14.78, while a Random Forest (10 trees, depth = 5) achieves 14.97 and offers best interpretability. Daily error distributions reveal a median RMSE of 13.84 and confirm that predictions deviate less than one hour from observed shade-seeking peaks. These results demonstrate the suitability of soft computing, data-driven approaches embedded in an applied-mathematical feature framework for modeling noisy biological phenomena, demonstrating their value as low-cost, real-time decision-support tools for precision livestock farming under heat-stress conditions.

Enhong Liu, Haiyu Yang, Miel Hostens

Large Language Models (LLM) hold potential to support dairy scholars and farmers by supporting decision-making and broadening access to knowledge for stakeholders with limited technical expertise. However, the substantial computational demand restricts access to LLM almost exclusively through cloud-based service, which makes LLM-based decision support tools impractical for dairy farming. To address this gap, lightweight alternatives capable of running locally on farm hardware are required. In this work, we benchmarked 20 open-source Small Language Models (SLM) available on HuggingFace under farm-realistic computing constraints. Building on our prior work, we developed an agentic AI system that integrates five task-specific agents: literature search, web search, SQL database interaction, NoSQL database interaction, and graph generation following predictive models. Evaluation was conducted in two phases. In the first phase, five test questions were used for the initial screening to identify models capable of following basic dairy-related instructions and performing reliably in a compute-constrained environment. Models that passed this preliminary stage were then evaluated using 30 questions (five per task category mentioned above, plus one category addressing integrity and misconduct) in phase two. In results, Qwen-4B achieved superior performance across most of task categories, although showed unstable effectiveness in NoSQL database interactions through PySpark. To our knowledge, this is the first work explicitly evaluating the feasibility of SLM as engines for dairy farming decision-making, with central emphases on privacy and computational efficiency. While results highlight the promise of SLM-assisted tools for practical deployment in dairy farming, challenges remain, and fine-tuning is still needed to refine SLM performance in dairy-specific questions.

Jiawen Lyu, Manu Ramesh, Madison Simonds et al.

Few automated video systems are described in the open literature that enable hands-free cataloging and identification (ID) of cows in a dairy herd. In this work, we describe our system, composed of an AutoCattloger, which builds a Cattlog of dairy cows in a herd with a single input video clip per cow, an eidetic cow recognizer which uses no deep learning to ID cows, and a CowFinder, which IDs cows in a continuous stream of video. We demonstrate its value in finding individuals in unlabeled, unsegmented videos of cows walking unconstrained through the holding area of a milking parlor.

Oluwadurotimi Onibonoje, Vuong M. Ngo, Andrew McCarre et al.

Grasslands, constituting the world's second-largest terrestrial carbon sink, play a crucial role in biodiversity and the regulation of the carbon cycle. Currently, the Irish dairy sector, a significant economic contributor, grapples with challenges related to profitability and sustainability. Presently, grass growth forecasting relies on impractical mechanistic models. In response, we propose deep learning models tailored for univariate datasets, presenting cost-effective alternatives. Notably, a temporal convolutional network designed for forecasting Perennial Ryegrass growth in Cork exhibits high performance, leveraging historical grass height data with RMSE of 2.74 and MAE of 3.46. Validation across a comprehensive dataset spanning 1,757 weeks over 34 years provides insights into optimal model configurations. This study enhances our understanding of model behavior, thereby improving reliability in grass growth forecasting and contributing to the advancement of sustainable dairy farming practices.

Voraprapa Nakavachara, Chanon Thongtai, Thanarat Chalidabhongse et al.

This study examines how consumer attitudes toward animal welfare influence food selection and pricing using real-world market data from a Swiss supermarket. Our findings indicate that higher animal welfare standards are consistently associated with higher prices, suggesting that ethical considerations play a significant role in generating price premiums based on consumer preferences. On average, a one-point increase in the animal welfare score (ranging from 1 to 5, with 5 being the highest) corresponds to a 16.4% price increase, with the effect being most pronounced in Dairy & Eggs (25.3%), compared to Meat & Fish (14.3%). These results highlight the psychological and behavioral factors underlying consumer preferences for ethically produced foods. Additionally, we find limited evidence of a price premium for climate-friendly food products, observed only in Yogurts & Desserts, a subcategory within Dairy & Eggs. Our findings contribute to the understanding of how ethical food attributes influence consumer decision-making and pricing in retail settings.

R.B. Stephansen, J. Lassen, V.M. Thorup et al.

ABSTRACT: Genetic enhancement of feed efficiency can improve the economic sustainability and environmental responsibility of dairy farming. Although genetic selection holds promise for improving feed efficiency across the lifespan of dairy cows, comprehensive data spanning whole lactations or even a productive lifetime are currently limited. To address this, we used production data and data from a camera-based feed intake and BW recording system, along with records of production, feed intake, and weight on Holstein cows from a research herd. We aimed to estimate variance components for a multivariate, multiparity model of production, feed intake, and BW data to calculate genetic residual feed intake (gRFI) for each of the Nordic breeds (Holstein, Jersey, and Red Dairy Cattle). Our approach included investigating a new definition of energy balance (EBbody) calculated from changes in body reserves, serving as an energy sink in gRFI. The data in our analysis consisted of 4,751 Holstein cows (7,851 lactations), 2,068 Jersey cows (3,486 lactations), and 3,235 Red Dairy Cattle cows (5,419 lactations). We used Gibbs sampling to estimate posterior means and SD for all model parameters. Our findings revealed moderate lactation-wise heritability of gRFI (0.15–0.38) across all breeds and parities. Moreover, gRFI genetic correlations varied (−0.2 to 0.4) between early- and mid- to late-lactation stages across all breeds, and for lactation-wise gRFI, there were moderately high genetic correlations (0.39–0.59) between primi- and multiparous lactations across the 3 breeds. Those results suggest the importance of recording phenotypes in most time periods within and across lactations. Our analysis indicated that improving gRFI with one genetic SD unit corresponded to a 2% to 3% gain in net return profit per cow-year, with no or minimal impact on production and body reserve management. We demonstrated the feasibility of incorporating EBbody into gRFI. Comparing gRFI calculated with EBbody or changes in BW as an energy sink trait for body reserve management were highly genetically correlated (>0.95). This result shows that the choice of the energy sink trait for body reserve management in gRFI will yield limited reranking among cows and sires when based on BW records only. However, EBbody offers an opportunity to incorporate BCS information without increasing the number of genetic parameters to be estimated, but it relies on parameters estimated in experimental settings. In conclusion, our study demonstrates the feasibility of developing a model for gRFI over most of the productive lifetime of dairy cattle, offering significant economic benefits without compromising productivity or body reserve management. Moving forward, comprehensive recording schemes covering whole lactations and productive lifetimes are advantageous for accurate selection indices of gRFI.

Yang Wang, Rui Jiao, Xiyan Zhang et al.

ABSTRACT: Cronobacter (7 species) are prevalent foodborne pathogens with a remarkable capacity to adapt to acidic environments. This resilience enables them to persist in both food matrices and host organisms. Here we investigated the role of the 2-component system response regulator OmpR in the acid tolerance of Cronobacter. Under acid stress, Cronobacter malonaticus demonstrated significantly elevated expression of ompR and type VI secretion system (T6SS) genes, as well as a marked decrease in the survival of OmpR or T6SS structure gene mutants, indicating the pivotal role of OmpR and T6SS in acid tolerance. Notably, OmpR markedly enhanced the T6SS expression by binding specifically to its promoter, and the activated T6SS expedited adaptation to acidic environments and facilitated biofilm formation, thereby aiding Cronobacter's survival under acidic conditions. Moreover, knocking out ompR in 6 additional Cronobacter species resulted in decreased T6SS expression and tolerance to acid stress than their wild-type strains, which further solidifies the widespread nature of the acid tolerance mechanism predicated on the activation of T6SS by OmpR in Cronobacter spp. A comprehensive understanding of the adaptation mechanisms employed by Cronobacter spp. in acidic conditions will provide a theoretical foundation for managing their contamination in acidic food matrices and preventing infection outbreaks in the infant gastrointestinal tract.

Ruitong Zhang, Zihang Shi, Xiankang Fan et al.

ABSTRACT: In this study, the effect of luxS, a key gene involved in quorum sensing, on the characteristic flavor of yogurt and its molecular mechanisms during the cofermentation of yogurt with engineered probiotics was investigated. The luxS gene overexpression strain was constructed by the homologous recombination technique, and its effect on the expression of population sensing signaling molecules and luxS gene was determined by bioluminescence and quantitative real-time PCR, and finally, headspace solid-phase micro extraction-GC-MS (HS-SPME-GC-MS) and metabolomics were used to determine the mechanism of its effect on the characteristic flavor of yogurt. The results demonstrated that the overexpression strains of Lactobacillus acidophilus CICC 6074-pMG36e-luxS and Lactobacillus helveticus R0052-pMG36e-luxS were successfully constructed. The expression of the luxS gene was upregulated by 2.25-fold and 3.16-fold, respectively. Compared with the wild-type strains, yogurt fermented by the overexpression strains showed a significant increase in AI-2 content, acidity, viable bacterial count, and protein hydrolysis, whereas pH, water-holding capacity, and hardness were significantly reduced. The HS-SPME-GC-MS results revealed the presence of 31 volatile flavor substances in yogurt. Among them, benzaldehyde (almond and burned sugar flavors), 2,4-dimethyl- (almond, cherry, and naphthalene flavors), dibutyl phthalate (a faint aromatic odor), and n-decanoic acid (rancid and fatty notes) were identified as the key differential flavor substances mediated by the luxS gene. Metabolomics results showed that the luxS gene mediates the production of organic acids in yogurt through arginine and proline metabolism, phenylalanine metabolism, and tryptophan metabolism. This study provides a theoretical basis for a deeper understanding of the molecular mechanisms underlying yogurt flavor formation.

F. Desidera, S.B. Skeie, T.G. Devold et al.

ABSTRACT: Somatic cell count is used as an indicator of milk quality and udder health in dairy goats, although its interpretation is complicated by noninfectious causes, including seasonality, farm-specific practices, and physiological factors. This study analyzed 868 milk samples from 9 Norwegian dairy goat farms to investigate the interplay between SCC, individual bacterial count (IBC), and milk composition. Samples were collected on 3 occasions during the lactation period (early, mid, and late lactation). The results showed that SCC peaked in the pasture period and then decreased but remained elevated in late lactation. Individual bacterial count showed a positive correlation with higher SCC levels, although this correlation varied significantly across different farms and time periods. The presence of intramammary infections only partially explained the varying correlation between SCC and bacterial counts. This indicates that the relationship between SCC and IBC is influenced not only by infections, but also by management practices, environmental conditions, and other farm-level factors. The study revealed a covariation between SCC and other milk components according to the lactation stage and season. Furthermore, the investigation of factors influencing the interplay between SCC and IBC provides a deeper understanding of SCC as a milk quality indicator in dairy goats.

Alejandro Belanche, André Bannink, Jan Dijkstra et al.

ABSTRACT: This publication aims to provide guidelines of the knowledge required and the potential research to be conducted in order to understand the mode of action of antimethanogenic feed additives (AMFA). In the first part of the paper, we classify AMFA into 4 categories according to their mode of action: (1) lowering dihydrogen (H2) production; (2) inhibiting methanogens; (3) promoting alternative H2-incorporating pathways; and (4) oxidizing methane (CH4). The second part of the paper presents questions that guide the research to identify the mode of action of an AMFA on the rumen CH4 production from 5 different perspectives: (1) microbiology; (2) cell and molecular biochemistry; (3) microbial ecology; (4) animal metabolism; and (5) cross-cutting aspects. Recommendations are provided to address various research questions within each perspective, along with examples of how aspects of the mode of action of AMFA have been elucidated before. In summary, this paper offers timely and comprehensive guidelines to better understand and reveal the mode of action of current and emerging AMFA.

Heidi Jim, Eduardo S. Ribeiro, Bruna Mion et al.

ABSTRACT: The objective of this retrospective descriptive study was to characterize housing and herd management practices of Quebec dairy farms. Pre-existent survey data (housing, husbandry, and herd health; 36 questions) collected in person by Lactanet technicians (n = 116; March 2020 to February 2021) from 1,965 herds were used. Results were segregated by facility type (freestall [FS] vs. tiestall herds [TS]) and summarized using descriptive statistics. The average herd size was 76 ± 56 cows, with 65 ± 47 milking cows, peaking at 40 ± 5 kg/d, with most herds housed their milking cows in TS facilities (80%). Of the FS herds (20%), 36% transitioned from TS after 2016. Based on the Canadian Code of Practice and peer-reviewed literature, management strengths included frequent bedding in TS, feed reduction before dry-off (>59% FS; >75% TS), routine hoof trimming (≥2×/yr in >80%), and adequate lighting (>85% maintained >200 lx for 14–17 h/d). Areas needed improvement included the adoption of secondary ventilation systems (observed in <45% FS and <20% TS), implementation of targeted dry-off protocols (>80% of herds applied a single dry-off protocol, regardless of milk yield) and greater adoption of teat sealant use (45% reported using intramammary antibiotics without teat sealants). Deep-bedded lying surfaces were uncommon (30% of far-off and lactating groups in FS; <20% of dry and lactating groups in TS). In FS herds, horizontal bars were most frequent in lactating groups (40%). In TS herds, 58% of herds calved cows in tiestalls and >80% lacked pasture or exercise pens access. Regarding hoof health, footbaths and sprays were largely absent in TS herds (90%), whereas FS herds more often used footbaths, especially for lactating cows (70%). These findings establish benchmarks for Quebec dairy herds, highlighting well-adopted practices and identifying opportunities to increase the uptake of specific management practices across the province to further enhance herd health and welfare.

R.G. Skinner, W.E. Brown

ABSTRACT: Substitution of soybean meal with dried distillers grains in calf starter feeds (SF) suppresses ADG and feed efficiency. Through the use of fractionation technologies, the ethanol industry is able to produce high-protein corn coproducts (HPCC), which are greater in protein and contain less fiber than traditional distillers grains, and more closely resemble the chemical composition of soybean meal. To evaluate the suitability of an HPCC as a substitute for soybean meal in calf grain, 21 male and 21 female Holstein calves were blocked by sex and birthdate, and randomly assigned to 1 of 3 calf grain treatments, which were offered beginning at 14 d of age. Calf grain treatments were formulated for 0% (CTRL), 50% (50HPCC), or 100% (100HPCC) replacement of the soybean meal (16.6% of diet DM) by HPCC. Rumen-protected Met and Lys were supplemented to meet estimated requirements. Calves were individually housed, and during the data collection period, which occurred after weaning (56–84 d of age), assigned grain was the only feed offered. Dry matter intake was recorded daily, and growth and blood samples were obtained every 14 ± 1 d. Fecal samples were collected on the last 4 d of trial to determine apparent total-tract digestibility. Data were analyzed using PROC MIXED in SAS (v. 9.4) for fixed effects of treatment, sex, and week, when applicable. Preplanned contrasts were used. Treatment, sex, and week interacted on grain intake, whereby 100HPCC males increased DMI more rapidly than males on other treatments but within females only CTRL and 50HPCC increased DMI. Increasing HPCC inclusion linearly increased BW and ADG. Treatment and sex interacted on feed efficiency, whereby 100HPCC males were more efficient than 100HPCC females. Wither height was affected quadratically, as 50HPCC calves were shorter than 100HPCC calves. Inclusion of HPCC increased calf plasma insulin concentration both linearly and compared with CTRL, but contrasts were not detected for blood BHB, BUN, glucose, free fatty acid, or triglyceride concentrations. Apparent total-tract digestibility of DM and CP in diets containing HPCC tended to be greater than CTRL. Overall results indicate the HPCC can replace soybean meal in SF when balanced for the limiting AA Met and Lys.

Jessica Ji, David Yu

This paper examines the merger and acquisition (M&A) process between COFCO and Mengniu Dairy, exploring the motivations behind this strategic move and identifying its key aspects. By analyzing both the financial and non-financial contributions of Mengniu Dairy to COFCO, this study provides valuable insights and references for future corporate M&A activities. The theoretical significance of this research lies in its focus on the relatively underexplored area of M&A within the dairy industry, particularly in terms of M&A contributions. Using the COFCO-Mengniu case as a model, the study broadens current research perspectives by assessing the impact of M&A from financial and non-financial standpoints, enriching the body of literature on dairy industry M&As.

Abhijin Adiga, Ayush Chopra, Mandy L. Wilson et al.

One Health issues, such as the spread of highly pathogenic avian influenza~(HPAI), present significant challenges at the human-animal-environmental interface. Recent H5N1 outbreaks underscore the need for comprehensive modeling efforts that capture the complex interactions between various entities in these interconnected ecosystems. To support such efforts, we develop a methodology to construct a synthetic spatiotemporal gridded dataset of livestock production and processing, human population, and wild birds for the contiguous United States, called a \emph{digital similar}. This representation is a result of fusing diverse datasets using statistical and optimization techniques, followed by extensive verification and validation. The livestock component includes farm-level representations of four major livestock types -- cattle, poultry, swine, and sheep -- including further categorization into subtypes such as dairy cows, beef cows, chickens, turkeys, ducks, etc. Weekly abundance data for wild bird species identified in the transmission of avian influenza are included. Gridded distributions of the human population, along with demographic and occupational features, capture the placement of agricultural workers and the general population. We demonstrate how the digital similar can be applied to evaluate spillover risk to dairy cows and poultry from wild bird population, then validate these results using historical H5N1 incidences. The resulting subtype-specific spatiotemporal risk maps identify hotspots of high risk from H5N1 infected wild bird population to dairy cattle and poultry operations, thus guiding surveillance efforts.

J. Denis-Robichaud, N. Barbeau-Grégoire, M.-L. Gauthier et al.

ABSTRACT: The objective of this cross-sectional study was to estimate the validity of laboratory culture, Petrifilm and Tri-Plate on-farm culture systems, as well as luminometry to correctly identify IMI at dry-off in dairy cows, considering all tests to be imperfect. From September 2020 until December 2021, we collected composite milk samples from cows before dry-off and divided them into 4 aliquots for luminometry, Petrifilm (aerobic count), Tri-Plate, and laboratory culture tests. We assessed multiple thresholds of relative light units (RLU) for luminometry, and we used thresholds of ≥100 cfu/mL for the laboratory culture, ≥50 cfu/mL for Petrifilm, and ≥1 cfu for Tri-Plate tests. We fitted Bayesian latent class analysis models to estimate the sensitivity (Se) and specificity (Sp) for each test to identify IMI, with 95% credibility interval (BCI). Using different prevalence measures (0.30, 0.50, and 0.70), we calculated the predictive values (PV) and misclassification cost terms (MCT) at different false negative-to-false-positive ratios (FN:FP). A total of 333 cows were enrolled in the study from one commercial Holstein herd. The validity of the luminometry was poor for all thresholds, with an Se of 0.51 (95% BCI = 0.43–0.59) and Sp of 0.38 (95% BCI = 0.26–0.50) when using a threshold of ≥150 RLU. The laboratory culture had an Se of 0.93 (95% BCI = 0.85–0.98) and Sp of 0.69 (95% BCI = 0.49–0.89); the Petrifilm had an Se of 0.91 (95% BCI = 0.80–0.98) and Sp of 0.71 (95% BCI = 0.51–0.90); and the Tri-Plate had an Se of 0.65 (95% BCI = 0.53–0.82) and Sp of 0.85 (95% BCI = 0.66–0.97). Bacteriological tests had good PV, with comparable positive PV for all 3 tests, but lower negative PV for the Tri-Plate compared with the laboratory culture and the Petrifilm. For a prevalence of IMI of 0.30, all 3 tests had similar MCT, but for prevalence of 0.50 and 0.70, the Tri-Plate had higher MCT in scenarios where leaving a cow with IMI untreated is considered to have greater detrimental effects than treating a healthy cow (i.e., FN:FP of 3:1). Our results showed that the bacteriological tests have adequate validity to diagnose IMI at dry-off, but luminometry does not. We concluded that although luminometry is not useful to identify IMI at dry-off, the Petrifilm and Tri-Plate tests performed similarly to laboratory culture, depending on the prevalence and importance of the FP and FN results.

Teddy Lazebnik, Orr Spiegel

Zoonotic disease transmission between animals and humans is a growing risk and the agricultural context acts as a likely point of transition, with individual heterogeneity acting as an important contributor. Thus, understanding the dynamics of disease spread in the wildlife-livestock interface is crucial for mitigating these risks of transmission. Specifically, the interactions between pigeons and in-door cows at dairy farms can lead to significant disease transmission and economic losses for farmers; putting livestock, adjacent human populations, and other wildlife species at risk. In this paper, we propose a novel spatio-temporal multi-pathogen model with continuous spatial movement. The model expands on the Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) framework and accounts for both within-species and cross-species transmission of pathogens, as well as the exploration-exploitation movement dynamics of pigeons, which play a critical role in the spread of infection agents. In addition to model formulation, we also implement it as an agent-based simulation approach and use empirical field data to investigate different biologically realistic scenarios, evaluating the effect of various parameters on the epidemic spread. Namely, in agreement with theoretical expectations, the model predicts that the heterogeneity of the pigeons' movement dynamics can drastically affect both the magnitude and stability of outbreaks. In addition, joint infection by multiple pathogens can have an interactive effect unobservable in single-pathogen SIR models, reflecting a non-intuitive inhibition of the outbreak. Our findings highlight the impact of heterogeneity in host behavior on their pathogens and allow realistic predictions of outbreak dynamics in the multi-pathogen wildlife-livestock interface with consequences to zoonotic diseases in various systems.