@article{zhu_multi-stage_2026,

title = {A multi-stage analysis of automatic milking system data: Integrating descriptive, predictive, and prescriptive analytics for dairy health management},

author = {Junsheng Zhu and Elsa Vasseur and Kevin Wade},

url = {https://www.sciencedirect.com/science/article/pii/S1537511026000516?via%3Dihub},

doi = {https://doi.org/10.1016/j.biosystemseng.2026.104433},

year  = {2026},

date = {2026-05-01},

journal = {Biosystem Engineering},

volume = {256},

abstract = {This study utilised a comprehensive dataset from automatic milking system (AMS) records, containing detailed data on various milking parameters and cow health indicators. One of the key metrics analysed was Somatic Cell Count (SCC), a critical marker for detecting clinical mastitis in dairy cows. The research was structured into three stages: descriptive, predictive, and prescriptive analytics. In the descriptive stage, extreme and potentially erroneous values were thoroughly examined, basic correlations and trends were explored, and the underlying causes of erroneous values were investigated. In the predictive stage, the focus was on forecasting SCC surges of 2,000,000 cells ml−1. Stationary and time-series methods were compared, with time-series approaches yielding better results, achieving a 3-day area-under-the-curve (AUC) of 85%, compared to 72% from stationary models. This highlighted the importance of incorporating temporal dynamics into predictive models for improved SCC forecasts. In the prescriptive stage, reinforcement learning (RL) was explored as a decision-support tool. Three fixed methods – baseline, SCC testing, and bacteria testing – were tested alongside a Q-learning agent. Results indicated that optimal management strategies varied across herds, but the consistency between training and testing sets confirmed the reliability of different RL agents. Equipping RL agents with predictive models allows them to integrate forecasts, converting model accuracy into tangible economic returns.},

keywords = {Article de journal},

pubstate = {published},

tppubtype = {article}

}

@article{bahadi_evaluating_2025,

title = {Evaluating the impact of housing modifications on milk infrared spectra as indicators of dairy cow welfare status},

author = {M. Bahadi and D. Warner and A. A. Ismail and D. E. Santschi and D. M. Lefebvre and R. Duggavathi and E. Vasseur},

url = {https://www.nature.com/articles/s41598-025-28557-7},

doi = {https://doi.org/10.1038/s41598-025-28557-7},

year  = {2025},

date = {2025-12-01},

journal = {Scientific Reports},

volume = {15},

abstract = {The objective of this study was to explore whether milk mid-infrared (MIR) spectral patterns could reflect physiological responses to improved housing conditions aimed at enhancing dairy cow comfort and ease of movement. Three controlled animal trials were conducted to test the effects of housing modifications related to tie chain length (TCL), stall width (SW), and a combination of manger wall and stall length (MW/SL). A hybrid analytical approach combining principal component analysis (PCA) and mixed models was applied to identify spectral differences across treatments. In all three trials, housing modifications were associated with significant differences in spectral patterns, even in the absence of major shifts in traditional milk composition metrics. For example, cows with longer chains (TCL trial) showed spectral trends suggestive of changes in components such as milk non-protein nitrogen (NPN), trans fatty acids, fat, and protein, which aligned with patterns reported in association with changes in rumen pH. These results were consistent with concurrent behavioral observations indicating improved comfort. This study provides preliminary evidence that milk MIR spectra may be sensitive to subtle physiological changes linked to housing design, with differences observed between the most and least restrictive treatments, translating into improved or reduced animal welfare status. While not intended as a predictive tool for welfare status, the approach offers a non-invasive framework for investigating animal–environment interactions. Limitations related to sample size and scope are acknowledged, and further work is needed to validate these findings across larger and more diverse populations.},

keywords = {Article de journal},

pubstate = {published},

tppubtype = {article}

}

@article{bradtmueller_predicting_2025,

title = {Predicting dairy cow locomotion ability based on kinematic 3D coordinates},

author = {Anna Bradtmueller and M. Gabriel Dallago and Amir Nejati and Elise Shepley and Amanda Boatswain Jacques and Dylan Lebatteux and Abdoulay Baniré Diallo and Elsa Vasseur},

url = {https://www.sciencedirect.com/science/article/pii/S2772375525008767?via%3Dihub},

doi = {https://doi.org/10.1016/j.atech.2025.101645},

year  = {2025},

date = {2025-11-01},

urldate = {2025-11-01},

journal = {Smart Agricultural Technology},

abstract = {Our study addresses the challenge of early lameness detection in dairy cows. Traditional visual scoring methods, while non-invasive and cost-effective, require extensive training and are impractical for continuous monitoring. Our research proposes an automated alternative using kinematic data and machine learning. Kinematic data were collected multiple times from 12 Holstein dairy cows over four weeks. After data cleaning, a total of 73 passages were available for model training. A trained observer scored the gait of each passage using a numerical rating system (NRS) ranging from 1 (sound cow) to 5 (severe lame cow) with 0.5 intervals. Data augmentation was used to obtain balanced data sets by adding 1%, 2.5%, 5%, 7.5%, and 10% Gaussian noise along with random shifting and followed by two data normalization strategies. The augmented data was split into training (75%) and testing (25%) sets. A long short-term memory neural network was trained and evaluated. The highest accuracy, precision, recall, and F1 score achieved on the test set was 0.96 (SD = 0.03) for all metrics. Models trained with data normalized to a mean of zero and standard deviation of one outperformed those using normalization to a range between zero and one. Future research should focus on expanding the range of locomotion scores, particularly covering the early stages of locomotion changes. This is necessary to enable earlier identification and treatment of cows with impaired locomotion ability before they develop lameness.},

keywords = {Article de journal},

pubstate = {published},

tppubtype = {article}

}

@article{dallago_early-life_2025,

title = {Early-life management practices and their association with dairy herd longevity, productivity, and profitability},

author = {Gabriel M. Dallago and Daniel Warner and Elsa Vasseur},

url = {https://www.sciencedirect.com/science/article/pii/S0022030225003765?via%3Dihub},

doi = {https://doi.org/10.3168/jds.2024-26191},

year  = {2025},

date = {2025-08-01},

journal = {Journal of Dairy Science},

volume = {108},

number = {8},

abstract = {Although improving the management of lactating cows to reduce health and reproductive issues can enhance cow longevity, the long-term effects of early-life management practices are less understood. The objectives of this study were to characterize dairy farms based on their early-life management practices and analyze their associations with herd longevity, productivity, and profitability. In this cross-sectional observational study, early-life management practices regarding colostrum feeding, milk feeding, solid feed and weaning, and housing were collected from 1,658 dairy farms in Québec, Canada, using a questionnaire between February 2020 and February 2021. Length of productive life and the percentage of cows in their third or greater lactation, estimated from DHI testing data, were used as herd longevity indicators, whereas lifetime cumulative ECM production and lifetime cumulative milk value, also derived from DHI records, served as indicators of productivity and profitability, respectively. Cluster analysis was performed to characterize farms based on their early-life management practices. Cluster stability assessment was used to determine the best clustering algorithm and number of clusters. Associations between herd longevity, productivity, profitability, and early-life management practices were assessed using multivariate linear regression models. Due to missing data (ranging from 0.1% to 15.4% across variables), multiple imputation was employed, and significant practices were identified by iteratively applying likelihood ratio tests (α < 0.05) across the imputed datasets. Two clusters were identified and denominated as traditionally or modernly managed farms. The traditionally managed farms cluster (n = 600; 36.2%) was characterized by feeding nonpasteurized or nonacidified milk (whole or waste) with individual buckets, not measuring the concentration of IgG in the colostrum, and housing calves individually. Modernly managed farms (n = 1,058; 63.8%) were characterized by feeding calves powdered milk replacer through automated systems and group housing calves both before and after weaning. Practices adopted by traditionally managed farms were associated with increased longevity but lower productivity and profitability, whereas practices adopted by modernly managed farms were associated with lower longevity but increased productivity and profitability. Our results highlight that early-life management practices are linked with herd longevity, productivity, and profitability, but further research is needed to understand the underlying factors contributing to these associations and to guide dairy farmers in making informed management decisions.},

keywords = {Article de journal},

pubstate = {published},

tppubtype = {article}

}

@article{naghashi_should_2025,

title = {Should We Reconsider RNNs for Time-Series Forecasting?},

author = {Vahid Naghashi and Mounir Boukadoum and Abdoulaye Banire Diallo},

year  = {2025},

date = {2025-04-01},

journal = {AI},

abstract = {(1) Background: In recent years, Transformer-based models have dominated the time-series forecasting domain, overshadowing recurrent neural networks (RNNs) such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). While Transformers demonstrate superior performance, their high computational cost limits their practical application in resource-constrained settings. (2) Methods: In this paper, we reconsider RNNs—specifically the GRU architecture—as an efficient alternative to time-series forecasting by leveraging this architecture’s sequential representation capability to capture cross-channel dependencies effectively. Our model also utilizes a feed-forward layer right after the GRU module to represent temporal dependencies, and aggregates it with the GRU layers to predict future values of a given time-series. (3) Results and conclusions: Our extensive experiments conducted on different real-world datasets show that our inverted GRU (iGRU) model achieves promising results in terms of error metrics and memory efficiency, challenging or surpassing state-of-the-art models on various benchmarks.},

keywords = {Article de journal},

pubstate = {published},

tppubtype = {article}

}

@article{araujo_ai-powered_2025,

title = {AI-powered cow detection in complex farm environments},

author = {Voncarlos Araujo and Ines Rili and Thomas Gisiger and Sebastien Gambs and Elsa Vasseur and Marjorie Cellier and Abdoulaye Baniré Diallo},

url = {https://www.sciencedirect.com/science/article/pii/S2772375525000048?via%3Dihub},

doi = {doi.org/10.1016/j.atech.2025.100770},

issn = {2772-3755},

year  = {2025},

date = {2025-03-01},

urldate = {2025-03-01},

journal = {Smart Agricultural Technology},

volume = {10},

number = {100770},

abstract = {Animal welfare has become a critical issue in contemporary society, emphasizing our ethical responsibilities toward animals, particularly within livestock farming. In addition, the advent of Artificial Intelligence (AI) technologies, specifically computer vision, offers a innovative approach to monitoring and enhancing animal welfare. Cows, as essential contributors to sustainable agriculture and climate management, being a central part of it. However, existing cow detection algorithms face significant challenges in real-world farming environments, such as complex lighting, occlusions, pose variations and background interference, which hinder accurate and reliable detection. Additionally, the model generalization power is highly desirable as it enables the model to adapt and perform well across different contexts and conditions, beyond its training environment or dataset. This study addresses these challenges in diverse cow dataset composed of six different environments, including indoor and outdoor scenarios. More precisely, we propose a novel detection model that combines YOLOv8 with the CBAM (Convolutional Block Attention Module) and assess its performance against baseline models, including Mask R-CNN, YOLOv5 and YOLOv8. Our findings indicate that while baseline models show promise, their performance degrades in complex real-world conditions, which our approach improves using the CBAM attention module. Overall, YOLOv8-CBAM outperformed YOLOv8 by 2.3% in mAP across all camera types, achieving a precision of 95.2% and an mAP@0.5:0.95 of 82.6%, demonstrating superior generalization and enhanced detection accuracy in complex backgrounds. Thus, the primary contributions of this research are: (1) providing an in-depth analysis of current limitations in cow detection under challenging indoor and outdoor environments, (2) proposing a robust general model that effectively detects cows in complex real-world conditions and (3) evaluating and benchmarking state-of-the-art detection algorithms. Potential application scenarios of the model include automated health monitoring, behavioral analysis and tracking within smart farm management systems, enabling precise detection of individual cows, even in challenging environments. By addressing these critical challenges, this study paves the way for future innovations in AI-driven livestock monitoring, aiming to improve the welfare and management of farm animals while advancing smart agriculture.},

keywords = {Article de journal},

pubstate = {published},

tppubtype = {article}

}

@article{naghashi_multiscale_2025,

title = {A multiscale model for multivariate time series forecasting},

author = {Vahid Naghashi and Mounir Boukadoum and Abdoulaye Banire Diallo},

url = {https://www.nature.com/articles/s41598-024-82417-4},

doi = {10.1038/s41598-024-82417-4},

issn = {2045-2322},

year  = {2025},

date = {2025-01-01},

urldate = {2025-03-06},

journal = {Scientific Reports},

volume = {15},

number = {1},

pages = {1565},

abstract = {Transformer based models for time-series forecasting have shown promising performance and during the past few years different Transformer variants have been proposed in time-series forecasting domain. However, most of the existing methods, mainly represent the time-series from a single scale, making it challenging to capture various time granularities or ignore inter-series correlations between the series which might lead to inaccurate forecasts. In this paper, we address the above mentioned shortcomings and propose a Transformer based model which integrates multi-scale patch-wise temporal modeling and channel-wise representation. In the multi-scale temporal part, the input time-series is divided into patches of different resolutions to capture temporal correlations associated with various scales. The channel-wise encoder which comes after the temporal encoder, models the relations among the input series to capture the intricate interactions between them. In our framework, we further design a multi-step linear decoder to generate the final predictions for the purpose of reducing over-fitting and noise effects. Extensive experiments on seven real world datasets indicate that our model (MultiPatchFormer) achieves state-of-the-art results by surpassing other current baseline models in terms of error metrics and shows stronger generalizability.},

keywords = {Article de journal},

pubstate = {published},

tppubtype = {article}

}

@article{dallago_associations_2024,

title = {The associations of early-life health and performance with subsequent dairy cow longevity, productivity, and profitability},

author = {G. M. Dallago and I. Elsohaby and J. T. McClure and R. Lacroix and E. Vasseur},

url = {https://linkinghub.elsevier.com/retrieve/pii/S175173112400212X},

doi = {10.1016/j.animal.2024.101281},

issn = {17517311},

year  = {2024},

date = {2024-09-01},

urldate = {2024-09-05},

journal = {animal},

volume = {18},

number = {9},

pages = {101281},

keywords = {Article de journal},

pubstate = {published},

tppubtype = {article}

}

@article{nejati_investigating_2024,

title = {Investigating the impact of 1h daily outdoor access on the gait and hoof health of non-clinically lame cows housed in a movement restricted environment},

author = {A. Nejati and E. Shepley and G. M. Dallago and E. Vasseur},

url = {https://linkinghub.elsevier.com/retrieve/pii/S2666910224000437},

doi = {10.3168/jdsc.2023-0498},

issn = {26669102},

year  = {2024},

date = {2024-03-01},

urldate = {2024-06-05},

journal = {JDS Communications},

pages = {S2666910224000437},

keywords = {Article de journal},

pubstate = {published},

tppubtype = {article}

}

@article{belanger-naud_is_2024,

title = {Is there a right time for dairy Alpine goat kid weaning: How does the weaning age of dairy Alpine goat kids affect their growth and behavior?},

author = {Stéphanie Bélanger-Naud and Tania Wolfe and Athena Zambelis and Janie Lévesque and Carl Julien and Elsa Vasseur},

url = {https://academic.oup.com/jas/article/doi/10.1093/jas/skad413/7492733},

doi = {10.1093/jas/skad413},

issn = {0021-8812, 1525-3163},

year  = {2024},

date = {2024-01-01},

urldate = {2024-06-05},

journal = {Journal of Animal Science},

volume = {102},

pages = {skad413},

abstract = {Abstract In dairy goat kids, weaning is often associated with poor growth leading to a decline in welfare and performance; however, little is known about optimal weaning practices. This study aimed to determine the optimal weaning age for dairy goat kids to maximize outcome measures of welfare related to growth, feed intake, and behavior. Thirty-six newborn female Alpine kids were blocked by weight and birth date, paired with a similar male companion and randomly allocated to one of the three weaning age treatments: 6 (6W), 8 (8W), and 10 wk (10W). Kids had ad libitum access to acidified milk replacer refilled twice daily, concentrates, hay, and water. Milk consumption was measured daily, and concentrate consumption, weekly. Ten behaviors were live observed on days −8, −4, 0, 6, and 12 relative to weaning (i.e., weaning day = 0). Kruskal–Wallis tests were used to assess differences from baseline between the 6W, 8W, and 10W treatments. Post hoc analysis using the Dwass, Steel, Critchlow-Fligner (DSCF) multiple comparison analysis was used to evaluate pairwise treatment differences based on two-sample Wilcoxon comparisons. Kids weaned at 10 wk had the greatest increase compared to baseline in concentrate consumption (P = 0.0160), and greatest decrease compared to baseline in vocalization (P = 0.0008) while both 8- and 10- wk kid’s groups had the greatest increase compared to baseline in self-grooming time (P < 0.0001), and cross-sucking time (P = 0.0006). Kids weaned at 6 wk of age were found to have the smallest increase compared to baseline in concentrate consumption (P = 0.0160) and self-grooming time (P < 0.0001), and the greatest increase compared to baseline in allogrooming time (P = 0.0032) and in redirected behaviors aimed towards the environment (biting and licking time [P = 0.0173]; displacement at the nipple frequency [P = 0.0236]). No negative impact of weaning on growth of either group was identified. Overall, our results tend towards a higher degree of discomfort behaviors (allogrooming, biting/licking, displacement, and vocalizations) in kids weaned earlier compared to later weaning, while kids weaned later showed higher levels of positive behaviors (lying time and self-grooming).},

keywords = {Article de journal},

pubstate = {published},

tppubtype = {article}

}

@article{aigueperse_how_2023,

title = {How outing conditions relate to the motivation of movement-restricted cattle to access an outdoor exercise yard},

author = {Nadège Aigueperse and Véronique Boyer and Elsa Vasseur},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0376635723001390},

doi = {10.1016/j.beproc.2023.104957},

issn = {03766357},

year  = {2023},

date = {2023-11-01},

urldate = {2024-06-05},

journal = {Behavioural Processes},

volume = {213},

pages = {104957},

keywords = {Article de journal},

pubstate = {published},

tppubtype = {article}

}

@article{palacio_reassessing_2023,

title = {Reassessing cow comfort measures on Canadian dairy farms after a recommendation of improvements},

author = {S. Palacio and A. Zambelis and S. Adam and R. Bergeron and D. Pellerin and D. Haley and T. J. DeVries and E. Vasseur},

url = {https://cdnsciencepub.com/doi/10.1139/cjas-2022-0033},

doi = {10.1139/cjas-2022-0033},

issn = {0008-3984, 1918-1825},

year  = {2023},

date = {2023-06-01},

urldate = {2024-06-05},

journal = {Canadian Journal of Animal Science},

volume = {103},

number = {2},

pages = {174–184},

abstract = {In a previous study (Assessment 1), an on-farm assessment tool was used to establish a standard across 60 Quebec dairy farms using animal-, stall-, and management-based measures of cow comfort. The objective of the present follow-up study (Assessment 2) was to perform a reassessment using an identical cow comfort assessment tool on a subset of the original farms expected to benefit most from applying the recommended changes, and to determine the effects of the modifications on cow comfort. From the subsample of farms, 24 reported applying stall modifications based on recommendations from Assessment 1 with the aim of promoting cow comfort (Adopters), while the remaining 10 farms did not (Non-adopters). The assessment tool included 19 target areas, grouped into 9 critical areas based on measures of cow comfort that considered housing, feed-water, health, and welfare. The on-farm report of Assessment 1 was found to increase producer awareness of issues in cow comfort measures on farms, as shown by an improvement in stall design according to recommendations based on cow body dimensions and reduced injury prevalence. Routine reassessment of cow comfort measures may be important to use in supporting target achievement improvements as part of welfare improvement strategies.},

keywords = {Article de journal},

pubstate = {published},

tppubtype = {article}

}

@article{palacio_minor_2023,

title = {Minor stall modifications and outdoor access can help improve dairy cow welfare in tie-stalls},

author = {Santiago Palacio and Steve Adam and Renée Bergeron and Doris Pellerin and Anne Marie De Passillé and Jeff Rushen and Derek Haley and Trevor J. DeVries and Elsa Vasseur},

url = {https://cdnsciencepub.com/doi/10.1139/cjas-2022-0038},

doi = {10.1139/cjas-2022-0038},

issn = {0008-3984, 1918-1825},

year  = {2023},

date = {2023-03-01},

urldate = {2024-06-05},

journal = {Canadian Journal of Animal Science},

volume = {103},

number = {1},

pages = {1–14},

abstract = {Tie-stalls are the most confining method of housing for dairy cows and remain commonly used to house cows in many countries. Finding methods to improve animal welfare through housing modifications and alternative management strategies are of increasing importance. Twelve tie-stall dairy farms in Ontario and Quebec, Canada were visited four times in a 12 month period. Visits were spread to observe the effects of pasture (visit 1), short-term effects of stall modifications (visit 2), effects of winter confinement (visit 3), and final measures after 1 year (visit 4). Lameness, injuries, cow cleanliness, lying and rising behaviors, and production parameters were assessed at each visit. Outdoor access had the largest effect: cows with access to pasture had a lower prevalence of various outcome measures throughout all four visits. Also, cows in modified stalls had a higher prevalence of dirty udders compared to cows in unmodified stalls, but only on farms that provided outdoor access. Although applying minor stall modifications to tie-stalls can help improve aspects of animal welfare, providing year-round outdoor access may represent a more effective method; the continued provision of time outside the stall helps maintain the positive impact of outdoor/pasture access on the welfare of tie-stall dairy cows.},

keywords = {Article de journal},

pubstate = {published},

tppubtype = {article}

}

@article{bradtmueller_applications_2023,

title = {Applications of Technology to Record Locomotion Measurements in Dairy Cows: A Systematic Review},

author = {Anna Bradtmueller and Amir Nejati and Elise Shepley and Elsa Vasseur},

url = {https://www.mdpi.com/2076-2615/13/6/1121},

doi = {10.3390/ani13061121},

issn = {2076-2615},

year  = {2023},

date = {2023-03-01},

urldate = {2024-06-05},

journal = {Animals},

volume = {13},

number = {6},

pages = {1121},

abstract = {Lameness within the dairy industry is a concern because of its associated costs and welfare implications. Visual locomotion scoring has been commonly used for assessing cows’ locomotion quality, but it can have low reliability and is relatively subjective compared to automated methods of assessing locomotion. Kinematic, kinetic, and accelerometric technologies can provide a greater number of more detailed outcome measurements than visual scoring. The objective of this systematic review was to determine outcome measurements, and the relationships between them, that have been recorded using kinematic, kinetic, and accelerometric technologies, as well as other approaches to evaluating cow locomotion. Following PRISMA guidelines, two databases were searched for studies published from January 2000 to June 2022. Thirty-seven articles were retained after undergoing a screening process involving a title and abstract evaluation, followed by a full-text assessment. Locomotion measurements recorded using these technologies often overlapped, but inconsistencies in the types of technology, the arrangement of equipment, the terminology, and the measurement-recording approaches made it difficult to compare locomotion measurements across studies. Additional research would contribute to a better understanding of how factors regarding the health, environment, and management of dairy cows affect aspects of locomotion, as recorded through the detailed, objective outcome measurements provided by these technologies.},

keywords = {Article de journal},

pubstate = {published},

tppubtype = {article}

}

@article{nejati_technology_2023,

title = {Technology applications in bovine gait analysis: A scoping review},

author = {Amir Nejati and Anna Bradtmueller and Elise Shepley and Elsa Vasseur},

editor = {Yaodong Gu},

url = {https://dx.plos.org/10.1371/journal.pone.0266287},

doi = {10.1371/journal.pone.0266287},

issn = {1932-6203},

year  = {2023},

date = {2023-01-01},

urldate = {2024-06-05},

journal = {PLOS ONE},

volume = {18},

number = {1},

pages = {e0266287},

abstract = {Quantitative bovine gait analysis using technology has evolved significantly over the last two decades. However, subjective methods of gait assessment using visual locomotion scoring remain the primary on-farm and experimental approach. The objective of this review is to map research trends in quantitative bovine gait analysis and to explore the technologies that have been utilized to measure biomechanical parameters of gait. A scoping literature review was conducted according to PRISMA guidelines. A search algorithm based on PICO framework generated three components–bovine, gait, and technology–to address our objectives. Three online databases were searched for original work published from January 2000 to June 2020. A two-step screening process was then conducted, starting with the review of article titles and abstracts based on inclusion criteria. A remaining 125 articles then underwent a full-text assessment, resulting in 82 final articles. Thematic analysis of research aims resulted in four major themes among the studies: gait/claw biomechanics, lameness detection, intervention/comparison, and system development. Of the 4 themes, lameness detection (55% of studies) was the most common reason for technology use. Within the literature identified three main technologies were used: force and pressure platforms (FPP), vision-based systems (VB), and accelerometers. FPP were the first and most popular technologies to evaluate bovine gait and were used in 58.5% of studies. They include force platforms, pressure mapping systems, and weight distribution platforms. The second most applied technology was VB (34.1% of studies), which predominately consists of video analysis and image processing systems. Accelerometers, another technological method to measure gait characteristics, were used in 14.6% of studies. In sum, the strong demand for automatic lameness detection influenced the path of development for quantitative gait analysis technologies. Among emergent technologies, deep learning and wearable sensors (e.g., accelerometers) appear to be the most promising options. However, although progress has been made, more research is needed to develop more accurate, practical, and user-friendly technologies.},

keywords = {Article de journal},

pubstate = {published},

tppubtype = {article}

}

@article{habanabakize_typology_2022,

title = {A typology of smallholder livestock production systems reflecting the impact of the development of a local milk collection industry: Case study of Fatick region, Senegal},

author = {Ernest Habanabakize and Koki Ba and Christian Corniaux and Patrick Cortbaoui and Elsa Vasseur},

url = {https://pastoralismjournal.springeropen.com/articles/10.1186/s13570-022-00234-8},

doi = {10.1186/s13570-022-00234-8},

issn = {2041-7136},

year  = {2022},

date = {2022-12-01},

urldate = {2024-06-05},

journal = {Pastoralism},

volume = {12},

number = {1},

pages = {22},

abstract = {Abstract 

 Senegal is a country where millions of livelihoods directly depend on smallholder livestock production activities. Unfortunately, these farmers now face the effects of a changing climate and associated societal responses. In addition, the lack of understanding of the specific farmers’ problems in their respective production systems by assuming homogeneity has led to inappropriate planning of interventions and inefficient utilization of resources. The variations in individual farming systems and local contexts require to understand each system separately to inform decision-makers and relevant stakeholders about specific entries of intervention for farmers based on customized needs. Using a Fatick region case study, different statistical clustering techniques were adopted to develop a livestock typology that reflects the above realities. This typology confirmed the co-existence of three livestock production cluster systems with differences and similarities in their production targets and quantities, market access, animal breeds, and the livestock management system of the animals. The studied region represented a new format of smallholder livestock development, where the implementation of a dairy processor and its milk collection centre created a new dynamic in production activities. Farmers confirmed that the extension services and a consistent market opportunity provided by the dairy processor increased local productivity and offered an alternative source of income but they have also reiterated concerns like the dairy processor’s low milk buying prices which need to be addressed. To harness the growth and the regional opportunities, there is a need to establish robust and continuous collaboration structures between the Government, agro-industries, and producers. These public-private partnerships will help producers lower the cost of inputs and services and increase productivity while boosting local dairy production.},

keywords = {Article de journal},

pubstate = {published},

tppubtype = {article}

}

@article{warner_keeping_2022,

title = {Keeping profitable cows in the herd: A lifetime cost-benefit assessment to support culling decisions},

author = {D. Warner and G. M. Dallago and O. W. Dovoedo and R. Lacroix and H. A. Delgado and R. I. Cue and K. M. Wade and J. Dubuc and D. Pellerin and E. Vasseur},

url = {https://linkinghub.elsevier.com/retrieve/pii/S1751731122001859},

doi = {10.1016/j.animal.2022.100628},

issn = {17517311},

year  = {2022},

date = {2022-10-01},

urldate = {2024-06-05},

journal = {animal},

volume = {16},

number = {10},

pages = {100628},

keywords = {Article de journal},

pubstate = {published},

tppubtype = {article}

}

@article{dallago_using_2022,

title = {Using the Herd Status Index to remotely assess the welfare status of dairy herds based on prerecorded data},

author = {G. M. Dallago and N. Mauyenova and D. Warner and R. I. Cue and E. Vasseur},

url = {https://linkinghub.elsevier.com/retrieve/pii/S1751731122001987},

doi = {10.1016/j.animal.2022.100641},

issn = {17517311},

year  = {2022},

date = {2022-10-01},

urldate = {2024-06-05},

journal = {animal},

volume = {16},

number = {10},

pages = {100641},

keywords = {Article de journal},

pubstate = {published},

tppubtype = {article}

}

@article{tonooka_graduate_2022,

title = {Graduate Student Literature Review: What is known about the eliminative behaviors of dairy cattle?},

author = {J. M. Tonooka and E. Vasseur and M. Villettaz Robichaud},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0022030222003113},

doi = {10.3168/jds.2021-20651},

issn = {00220302},

year  = {2022},

date = {2022-07-01},

urldate = {2024-06-05},

journal = {Journal of Dairy Science},

volume = {105},

number = {7},

pages = {6307–6317},

keywords = {Article de journal},

pubstate = {published},

tppubtype = {article}

}