Feeding dairy cows for improved metabolism and health

Martins, L.F.; Wasson, D.E.; Hristov, A.N.

doi:10.1093/af/vfac059

Cited by 4 publications

(2 citation statements)

References 59 publications

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“…Kazemi et al investigated a model for the relationship between cow feeding-behavior and cow yield, and the experimental results showed that cow feeding time was positively correlated with cow milk production [7]. Martins et al outlined the relationship of inflammation to metabolism and nutrition in dairy cows, noting that metabolic disorders are closely related to cow feeding-behavior [8]. Therefore, effective monitoring of cow feeding-behavior can provide a basis for evaluating cow health, which positively impacts the sustainability and economic efficiency of dairy farming [9,10].…”

Section: Introductionmentioning

confidence: 99%

Multi-Target Feeding-Behavior Recognition Method for Cows Based on Improved RefineMask

Li,

Gao,

et al. 2024

Sensors

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Within the current process of large-scale dairy-cattle breeding, to address the problems of low recognition-accuracy and significant recognition-error associated with existing visual methods, we propose a method for recognizing the feeding behavior of dairy cows, one based on an improved RefineMask instance-segmentation model, and using high-quality detection and segmentation results to realize the recognition of the feeding behavior of dairy cows. Firstly, the input features are better extracted by incorporating the convolutional block attention module into the residual module of the feature extraction network. Secondly, an efficient channel attention module is incorporated into the neck design to achieve efficient integration of feature extraction while avoiding the surge of parameter volume computation. Subsequently, the GIoU loss function is used to increase the area of the prediction frame to optimize the convergence speed of the loss function, thus improving the regression accuracy. Finally, the logic of using mask information to recognize foraging behavior was designed, and the accurate recognition of foraging behavior was achieved according to the segmentation results of the model. We constructed, trained, and tested a cow dataset consisting of 1000 images from 50 different individual cows at peak feeding times. The method’s effectiveness, robustness, and accuracy were verified by comparing it with example segmentation algorithms such as MSRCNN, Point_Rend, Cascade_Mask, and ConvNet_V2. The experimental results show that the accuracy of the improved RefineMask algorithm in recognizing the bounding box and accurately determining the segmentation mask is 98.3%, which is higher than that of the benchmark model by 0.7 percentage points; for this, the model parameter count size was 49.96 M, which meets the practical needs of local deployment. In addition, the technologies under study performed well in a variety of scenarios and adapted to various light environments; this research can provide technical support for the analysis of the relationship between cow feeding behavior and feed intake during peak feeding periods.

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Section: Introductionmentioning

confidence: 99%

Multi-Target Feeding-Behavior Recognition Method for Cows Based on Improved RefineMask

Li,

Gao,

et al. 2024

Sensors

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“…Dairy cows are challenged from the biological point of view since genetic and management improvements resulted in high levels of production. Leoni et al (2022) focus their review on how feeding cows properly may enhance transition from gestation to lactation through a control of inflammation, leading to a reduction of potential health issues and increasing milk production during the entire lactation. The authors clearly state the connection between gut barrier integrity and activation of the immune system.…”

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confidence: 99%

Immunometabolism in animal production: building efficiency from health

Palladino

Colombatto

2022

Animal Frontiers

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Modeling impact of improved forage cultivation on milk productivity and feed sufficiency in semiarid tropics of central India: A doubly robust analysis

Choudhary,

Sharma,

Singh

et al. 2024

Animal Science Journal

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The study using cross‐sectional data collected from 300 dairy farmers has analyzed the factors affecting adoption of improved forage technologies and its impact on milk yield and feed sufficiency in central region. We used inverse‐propensity‐weighting regression adjustment (IPWRA) method as main technique for impact evaluation and checked the robustness of the results using matching methods. Our findings suggest that education status, adult cattle unit, animal breed type, off‐farm income activities, farm size, and access to training and market significantly influence adoption of improved forage technologies and practices. Further, the adoption led to a significant increase in daily milk yield (1.07 to 1.34 L), total dry matter availability by over 27%, and green fodder availability by around 80%. Ration balancing has been identified as a significant concern in the study region. Consequently, the study suggests that adopting a comprehensive approach is necessary to address the issue of proper ration balancing and fully harness the production potential of dairy animals.

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Feeding dairy cows for improved metabolism and health

Cited by 4 publications

References 59 publications

Multi-Target Feeding-Behavior Recognition Method for Cows Based on Improved RefineMask

Multi-Target Feeding-Behavior Recognition Method for Cows Based on Improved RefineMask

Immunometabolism in animal production: building efficiency from health

Modeling impact of improved forage cultivation on milk productivity and feed sufficiency in semiarid tropics of central India: A doubly robust analysis

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