Precision technologies for the management of reproduction in dairy cows

Das, Surajit; Shaji, Arsha; Nain, D; Singha, Shubham; Karunakaran, M.; Baithalu, Rubina Kumari

doi:10.1007/s11250-023-03704-2

Cited by 4 publications

(3 citation statements)

References 70 publications

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“…was only measured in the repeat breeder dairy cow group. BCS values of <3.0 or >3.5 are strongly associated with reduced reproductive performance in dairy cows [ 42 ]; however, we primarily aimed to investigate the hair mineral characteristics of repeat breeder dairy cows rather than focusing on finding a direct relationship between BCS and repeat breeding.…”

Section: Discussionmentioning

confidence: 99%

Mineral patterns in hair: A decisive factor between reproducible and repeat breeder dairy cows

Lim,

Lee,

Park

et al. 2024

PLoS ONE

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Reproduction, especially impregnation, is a critical aspect of dairy cow management that directly influences herd milk productivity. We conducted a noninvasive hair mineral assay to compare the mineral profiles of two dairy cow groups: reproducible and repeat breeder, by investigating the levels of 11 essential minerals (Ca, Mg, Na, K, Fe, Cu, Mn, Zn, Cr, Se, and P) and 6 toxic elements (Hg, Pb, Cd, Al, As, and Ni) in both groups. We also conducted principal component and correlation matrix analyses to compare hair mineral patterns between the groups. Compared to their reproducible counterparts, repeat breeder cows had lower levels of Na, K, and Se. However, Fe, Cd, Al, and As levels were higher in repeat breeders than in their reproducible counterparts. The correlation matrix showed notable correlation patterns for each group. Ca, K, and Na levels were positively correlated in reproducible cows, whereas repeat breeder cows showed positive correlations only between Ca and K levels. Se showed positive correlations with Zn only in the reproducible cow group. Negative correlations were not found in the reproducible group, whereas the repeat breeder group exhibited 7 negative correlations. Despite the limitations of hair mineral analysis, this study provided useful insights into the reproductive potential of dairy cows. These findings aid in easing the prediction of repeat breeder occurrences in herds and are expected to facilitate timely mineral supplementation and other interventions to improve overall herd reproduction in dairy farms.

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Section: Discussionmentioning

confidence: 99%

Mineral patterns in hair: A decisive factor between reproducible and repeat breeder dairy cows

Lim,

Lee,

Park

et al. 2024

PLoS ONE

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“…Estrus detection mainly depends on visual observations of behavioral shifts such as standing, additional walking, and restlessness [ 11 , 12 ]. These methods necessitate frequent labor-intensive observations, performed by skilled and experienced personnel, 2–5 times a day [ 13 , 14 ]. High-yielding dairy cows demonstrate less intense and shorter estrus behaviors, complicating identification through visual observation [ 13 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Smart sensors in Thai dairy reproduction: A case study

Kaewbang,

Lohanawakul,

Ketnuam

et al. 2024

Vet World

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Background and Aim: Movement activity sensors are known for their potential to boost the reproductive performance of dairy cows. This study evaluated the effectiveness of these sensors on three Thai dairy farms (MK, NF, and CC), each using different sensor brands. We focused on reproductive performance at these farms and expanded our evaluation to include farmer satisfaction with sensor technology on five farms (MK, NF, CC, AP, and IP), allowing for a thorough analysis of both operational outcomes and user feedback. Materials and Methods: A total of 298 lactation records and interviewing five experienced farm owners with over a year of sensor usage were our research methods. To measure the effect on the first service timing and post-parturition pregnancy rates, Cox regression models were utilized for sensor usage. Results: Biosensors’ implementation enhanced data precision while quickening the first service within 100 days and pregnancy within 200 days. The MK and NF farms showed significant progress. Within 100 and 200 days post-implementation, the overall improvement was 30%–34% in the first service rate and 39%–67% in the conception rate across all assessed farms. Farmers acknowledged improved reproductive performance from the sensors, overcoming language barriers. Conclusion: The study highlighted the advantages of using movement activity sensors in enhancing both cattle reproductive success and farmers’ satisfaction on Thai dairy farms. These sensors led to more accurate management decisions, increasing overall farm productivity. Keywords: dairy cattle, movement activity sensors, reproductive performance.

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“…Additionally, bio-metric and physiological data analysis, such as heart rate and rumination patterns, offers valuable indicators (Alipio et al, 2022). Contextual data integration, real-time processing, and ensemble methods have also been explored for enhanced accuracy (García et al, 2020;Das et al, 2023;Ali et al, 2020).…”

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confidence: 99%

Investigation of Bovine Disease and Events through Machine Learning Models

Nadeem,

Anis

2024

PJAR

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T he dairy industry plays a pivotal role in global food security by providing essential nutrition through milk production, relies heavily on the health and productivity of dairy cows. However, ensuring the health and well-being of dairy cows is of paramount importance for sustaining a thriving dairy industry, making bovine diseases a top concern for farmers worldwide. Bovine diseases, such as calving complications, mastitis, estrus, acidosis, and lameness, pose significant challenges. Detecting and diagnosing Abstract | Bovine disease identification utilizing multiple information sources and methods is widely applicate in the field of bovine disease prevention and health monitoring. Bovine disease detection is an emerging subject today to accomplish the farms demands of individuals across the globe. This research delves into the realm of bovine disease and event detection using advanced Machine Learning (ML) techniques. Focusing on the critical events of estrus, acidosis, mastitis, lameness, and calving, our study aims to revolutionize disease identification and timely intervention within the dairy industry. By leveraging four distinct ML models-Random Forest, XGBoost, Logistic Regression, and Single Perceptron we meticulously analyze four diverse data-sets to uncover intricate patterns and unveil hidden insights. The efficiency of random sampling in resolving the class imbalance issue is tested along with the validity and adaptability of these models utilizing GridSearchCV optimum parameter modification. The performance evaluation is based on accuracy, precision, recall, F1 score, and Area Under the Curve (AUC) metrics. With a resounding highest accuracy metric, the Random Forest model achieves a notable accuracy of up to 98.25%, while the recall score of 100, and Precision up to 97% affirming its supremacy in classifying bovine events. This achievement underscores the efficacy of employing ML algorithms for accurate and timely disease identification. This ground-breaking fusion of ML techniques with bovine disease detection holds trans-formative potential, promising to elevate animal welfare standards, optimize dairy productivity, and usher in a new era of datadriven dairy management.

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Precision technologies for the management of reproduction in dairy cows

Cited by 4 publications

References 70 publications

Mineral patterns in hair: A decisive factor between reproducible and repeat breeder dairy cows

Mineral patterns in hair: A decisive factor between reproducible and repeat breeder dairy cows

Smart sensors in Thai dairy reproduction: A case study

Investigation of Bovine Disease and Events through Machine Learning Models

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