Contribution of Precision Livestock Farming Systems to the Improvement of Welfare Status and Productivity of Dairy Animals

Simitzis, Panagiotis; Tzanidakis, Christos; Tzamaloukas, Ouranios; Sossidou, E.

doi:10.3390/dairy3010002

Cited by 19 publications

(9 citation statements)

References 183 publications

(180 reference statements)

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“…Human–animal interactions in dairy species are frequent, as milking is routinely performed more than once per day, while being strongly correlated with the animals’ performance and welfare status [ 5 ]. Behavioral reactivity (temperament) is commonly assessed by visual scores that consider the frequency and intensity of animal movements, which subsequently reflect fear levels and responsiveness [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Water Buffalo Responsiveness during Milking: Implications for Production Outputs, Reproduction Fitness, and Animal Welfare

Mincu

Gavojdian

Nicolae

et al. 2022

Animals

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The overall objective of this study was to evaluate the effects that milking temperament (MT) of water buffaloes has on milking traits, welfare indicators, and reproductive outputs. The study was performed on 60 multiparous buffalo cows (6.7 ± 0.6 lactations) at the beginning of their lactation (100 days in milk, DIM). Each buffalo cow was scored by two independent observers using a temperament scoring system (1: extremely calm, 2: calm, 3: alert, 4: reactive, and 5: aggressive), and then grouped as ‘calm’ (scores 1, 2, and 3; n = 42) or ‘nervous’ (scores 4 and 5; n = 18). Additionally, the milk yield at 100 DIM (MY), milking speed (MS), calving interval (CI), age at first calving (AFC), body condition score (BCS), animal-based welfare parameters, and infrared thermography data (IRT) were evaluated. The MT significantly influenced the MY (p = 0.0082), with calmer cows outperforming their nervous counterparts. The MS was significantly influenced by the MT (p = 0.0015), with calmer animals having a higher milk ejection rate. The MT of the cows had no influence on the CI, AFC, or BCS. The evidence from this study suggests that the responsiveness of buffalo cows during milking affects their milk yield and milking speed, with no associations being found for reproduction efficiency indicators or animal-based welfare indicators.

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Section: Introductionmentioning

confidence: 99%

Water Buffalo Responsiveness during Milking: Implications for Production Outputs, Reproduction Fitness, and Animal Welfare

Mincu

Gavojdian

Nicolae

et al. 2022

Animals

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“…For adult dairy cows, large amounts of individual animal data are already being collected for various purposes, leading the way to precision dairy farming [2,3]. This includes the objective recording and utilization of feed and water intake [2,[4][5][6], rumination activity [5,7], movement activity [8,9], evaluation of animal welfare [10,11], detecting general or specific changes in behavior [12,13], and indicating changes in the animal's health [2,[14][15][16]. This data collection can be largely automated, such as during the milk test directly after milking [17], camera systems evaluating lameness [18,19] and body condition [20], or algorithms detecting behavioral changes.…”

Section: Current State Of Art For Adult Dairy Cowsmentioning

confidence: 99%

Standardized Decision-Making for the Selection of Calf and Heifer Rearing Using a Digital Evaluation System

Regler,

Bernhardt

2024

Agriculture

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This study addresses the challenge of subjective remounting decisions in calf and heifer rearing, typically driven by the animal caretaker’s feelings and experience, lacking a robust data foundation. Key factors such as developmental delays, diseases, or rearing problems often go unnoticed or are forgotten due to the number of animals. To address this gap, an established state-of-the-art sensor network captures behavioral data during rearing, which is supplemented by manually collected data. This facilitates a novel decision network providing well-founded recommendations to the animal owner regarding whether to retain or cull an animal. The approach focuses on four key areas: colostrum supply, milk intake, weight development, and disease history during the rearing time of each individual, offering a transparent decision path for the use of each future cow. Introducing a standardized decision-making approach, the proposed approach enables an efficient, transparent, and targeted management strategy, contributing to the sustainable enhancement of the health and performance of calves and heifers. Additionally, it allows for the comparison of the growth trajectories of different animals over time. Notably, individual and transparent decisions can be made at each growth stage, enhancing the overall decision-making process in calf and heifer rearing.

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“…As the scale of dairy operations expands, the task of monitoring cows grows more intricate and requires enhanced management skills. Adopting automation and sensor systems, known as precision technology, allows farmers to reduce labor requirements, improve resource efficiency, and improve cows’ health and welfare [ 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

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

Abdanan Mehdizadeh,

Sari,

Orak

et al. 2023

Animals

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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.

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Contribution of Precision Livestock Farming Systems to the Improvement of Welfare Status and Productivity of Dairy Animals

Cited by 19 publications

References 183 publications

Water Buffalo Responsiveness during Milking: Implications for Production Outputs, Reproduction Fitness, and Animal Welfare

Water Buffalo Responsiveness during Milking: Implications for Production Outputs, Reproduction Fitness, and Animal Welfare

Standardized Decision-Making for the Selection of Calf and Heifer Rearing Using a Digital Evaluation System

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

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