Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
2.7
Journals
Animals
Special Issues
Monitoring of Cows: Management and Sustainability
share announcement

Monitoring of Cows: Management and Sustainability

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Cattle".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 24301

Special Issue Editors

Dr. Patrícia Ferreira Ponciano Ferraz

E-Mail Website
Guest Editor
Department of Agricultural Engineering, Universidade Federal de Lavras, Lavras, Brazil
Interests: precision zootechnics; handling; instrumentation; comfort and well-being of dairy cows in Free-Stall and Compost Barn system
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Matteo Barbari

E-Mail Website
Guest Editor
Department of Agriculture, Food, Environment and Forestry (DAGRI), Università degli Studi di Firenze, Florence, Italy
Interests: livestock housing; housing systems with respect to sustainability goals and technical innovations; freewalk dairy barns; climate control in livestock buildings; emissions of ammonia and greenhouse gases
Special Issues, Collections and Topics in MDPI journals
Dr. Tomas Norton

E-Mail Website
Guest Editor
Measure, Model, Manage Bio-Responses (M3-BIORES), Animal & Human Health Engineering, Department of Biosystems, KU Leuven, Kasteelpark Arenberg 30, 3001 Leuven, Belgium
Interests: precision livestock farming (PLF); modelling and management of animal responses; animal health and welfare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Dairy production systems must provide good productivity and profitability but also demonstrate a reduced impact on the environment, good animal welfare standards, and sustainability. Within this context, the ability of dairy cattle producers to monitor the productivity, behaviour, and welfare of their animals and environment plays an important role in the whole production. Introducing dairy technology for monitoring dairy cattle and the environment enable us to precisely describe the environment and discriminate cow activities and to avoid disturbing natural behavioral expression. However, for precision dairy-monitoring technologies to increase labor and production efficiency, they must easily and accurately quantify meaningful environmental, physiological, or behavioral parameters.

The aim of this Special Issue is to bring together the latest findings concerning the monitoring of cows grazing on pasture or in confinement systems. Original research papers as well as literature reviews from different research areas, such as monitoring of animal health, animal reproduction, animal welfare and animal behaviour, besides monitoring of the environment system and innovative techniques of data measurements, monitoring of gas emissions, monitor devices, analysis algorithms, precision technologies, mathematical modelling and building design, with a link to management and sustainability, are welcomed to this Special Issue. Additional topics and interdisciplinary studies regarding the environmental, economic, and social impact of sustainability and management of cows will also be considered.

Dr. Patrícia Ferreira Ponciano Ferraz
Prof. Dr. Matteo Barbari
Prof. Dr. Tomas Norton
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Animals is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

 

Keywords

  • monitoring of cows grazing on pasture or in confinement systems (productivity, health, behaviour, welfare, reproduction, environment, gases, milk quality, etc.)
  • sustainability of the system and production
  • social and economic impact
  • management of cattle system
  • precision livestock (technologies, mathematical modelling, techniques of data measurements, monitor devices, analysis algorithms, precision technologies, sensors, software, sound analysis, images analysis, etc.)
  • traceability

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (14 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

23 pages, 3517 KiB  
Article
Digital and Precision Technologies in Dairy Cattle Farming: A Bibliometric Analysis
by Franck Morais de Oliveira, Gabriel Araújo e Silva Ferraz, Ana Luíza Guimarães André, Lucas Santos Santana, Tomas Norton and Patrícia Ferreira Ponciano Ferraz
Animals 2024, 14(12), 1832; https://doi.org/10.3390/ani14121832 - 20 Jun 2024
Viewed by 3564
Abstract
The advancement of technology has significantly transformed the livestock landscape, particularly in the management of dairy cattle, through the incorporation of digital and precision approaches. This study presents a bibliometric analysis focused on these technologies involving dairy farming to explore and map the [...] Read more.
The advancement of technology has significantly transformed the livestock landscape, particularly in the management of dairy cattle, through the incorporation of digital and precision approaches. This study presents a bibliometric analysis focused on these technologies involving dairy farming to explore and map the extent of research in the scientific literature. Through this review, it was possible to investigate academic production related to digital and precision livestock farming and identify emerging patterns, main research themes, and author collaborations. To carry out this investigation in the literature, the entire timeline was considered, finding works from 2008 to November 2023 in the scientific databases Scopus and Web of Science. Next, the Bibliometrix (version 4.1.3) package in R (version 4.3.1) and its Biblioshiny software extension (version 4.1.3) were used as a graphical interface, in addition to the VOSviewer (version 1.6.19) software, focusing on filtering and creating graphs and thematic maps to analyze the temporal evolution of 198 works identified and classified for this research. The results indicate that the main journals of interest for publications with identified affiliations are “Computers and Electronics in Agriculture” and “Journal of Dairy Science”. It has been observed that the authors focus on emerging technologies such as machine learning, deep learning, and computer vision for behavioral monitoring, dairy cattle identification, and management of thermal stress in these animals. These technologies are crucial for making decisions that enhance health and efficiency in milk production, contributing to more sustainable practices. This work highlights the evolution of precision livestock farming and introduces the concept of digital livestock farming, demonstrating how the adoption of advanced digital tools can transform dairy herd management. Digital livestock farming not only boosts productivity but also redefines cattle management through technological innovations, emphasizing the significant impact of these trends on the sustainability and efficiency of dairy production. Full article
(This article belongs to the Special Issue Monitoring of Cows: Management and Sustainability)
►▼ Show Figures

Figure 1

20 pages, 11002 KiB  
Article
Monitoring Cattle Ruminating Behavior Based on an Improved Keypoint Detection Model
by Jinxing Li, Yanhong Liu, Wenxin Zheng, Xinwen Chen, Yabin Ma and Leifeng Guo
Animals 2024, 14(12), 1791; https://doi.org/10.3390/ani14121791 - 14 Jun 2024
Cited by 1 | Viewed by 1065
Abstract
Cattle rumination behavior is strongly correlated with its health. Current methods often rely on manual observation or wearable devices to monitor ruminating behavior. However, the manual monitoring of cattle rumination is labor-intensive, and wearable devices often harm animals. Therefore, this study proposes a [...] Read more.
Cattle rumination behavior is strongly correlated with its health. Current methods often rely on manual observation or wearable devices to monitor ruminating behavior. However, the manual monitoring of cattle rumination is labor-intensive, and wearable devices often harm animals. Therefore, this study proposes a non-contact method for monitoring cattle rumination behavior, utilizing an improved YOLOv8-pose keypoint detection algorithm combined with multi-condition threshold peak detection to automatically identify chewing counts. First, we tracked and recorded the cattle’s rumination behavior to build a dataset. Next, we used the improved model to capture keypoint information on the cattle. By constructing the rumination motion curve from the keypoint information and applying multi-condition threshold peak detection, we counted the chewing instances. Finally, we designed a comprehensive cattle rumination detection framework to track various rumination indicators, including chewing counts, rumination duration, and chewing frequency. In keypoint detection, our modified YOLOv8-pose achieved a 96% mAP, an improvement of 2.8%, with precision and recall increasing by 4.5% and 4.2%, enabling the more accurate capture of keypoint information. For rumination analysis, we tested ten video clips and compared the results with actual data. The experimental results showed an average chewing count error of 5.6% and a standard error of 2.23%, verifying the feasibility and effectiveness of using keypoint detection technology to analyze cattle rumination behavior. These physiological indicators of rumination behavior allow for the quicker detection of abnormalities in cattle’s rumination activities, helping managers make informed decisions. Ultimately, the proposed method not only accurately monitors cattle rumination behavior but also provides technical support for precision management in animal husbandry, promoting the development of modern livestock farming. Full article
(This article belongs to the Special Issue Monitoring of Cows: Management and Sustainability)
►▼ Show Figures

Figure 1

14 pages, 905 KiB  
Article
Analytical and Clinical Interference of Sample Hemolysis in Evaluating Blood Biochemical and Endocrine Parameters in Cows
by Dražen Kovačević, Marko Cincović, Mira Majkić, Jovan Spasojević, Radojica Djoković, Sandra Nikolić, Maja Došenović Marinković, Biljana Delić Vujanović, Nemanja Obradović, Ljiljana Anđušić, Aleksandar Čukić, Miloš Petrović, Jože Starič and Jožica Ježek
Animals 2024, 14(12), 1773; https://doi.org/10.3390/ani14121773 - 12 Jun 2024
Viewed by 1113
Abstract
Hemolysis is a common cause of errors in laboratory tests as it affects blood parameters and leads to a positive or negative bias. This study aims to examine the relationship between the level of hemolysis (expressed as cell-free hemoglobin concentration, g/L) and the [...] Read more.
Hemolysis is a common cause of errors in laboratory tests as it affects blood parameters and leads to a positive or negative bias. This study aims to examine the relationship between the level of hemolysis (expressed as cell-free hemoglobin concentration, g/L) and the variability of metabolic and endocrine parameters and to determine the threshold level of hemolysis that causes an analytically and clinically significant bias for the twenty most frequently examined blood parameters in cows. Paired blood samples of 10 mL each were obtained from 30 cows. One was subjected to mechanical trauma and plasma was extracted directly from the other. Hemolyzed and non-hemolyzed samples from the same animal were mixed to obtain final samples with cell-free hemoglobin concentrations of 0, 1, 2, 4, 6, 8, and 10 g/L. Metabolic and endocrine parameters were measured in the samples and their deviation and the linear equation between the level of hemolysis and the deviation were determined. The following threshold values of hemolysis were determined, which correspond to the acceptable analytical (lower value) and clinical (upper value) levels of parameter variability: BHB 0.96 and 4.81; NEFA 0.39 and 3.31; GLU 0.38 and 3.90; ALB 1.12 and 6.11; TPROT 1.40 and 6.80; UREA 6.62 and 20.1; TBIL 0.75 and 5.65; AST 0.11 and 2.18; GGT 1.71 and 8.90, LDH 0.01 and 0.11, ALP 0.97 and 2.95; TGC 1.56 and 15.5; CHOL 1.29 and 8.56; Ca 5.68 and 25.7; P 0.57 and 8.43; Mg 1.10 and 8.47; INS 1.15 and 3.89; T3 8.19 and 15.6; T4 8.97 and 18.5; and CORT 2.78 and 11.22 g/L cell-free hemoglobin. Three decision levels are available for each metabolic and endocrine parameter: if hemolysis is below the lower (analytical) threshold value, results can be reported without restriction; if hemolysis is between the lower and upper thresholds, the results can be issued with guidance in the form of corrective linear equations; and if hemolysis is above the upper (clinical) threshold, the results and sample must be discarded. This method contributes to an optimal approach to hemolysis interference with metabolic profile parameters in blood samples from cows. Full article
(This article belongs to the Special Issue Monitoring of Cows: Management and Sustainability)
►▼ Show Figures

Figure 1

13 pages, 1366 KiB  
Article
Determining the Association of the Dry Period Duration with Dystocia and Stillbirths in Dairy Cows by Considering Parity, Season, and Gestation Length
by Vigilijus Jukna, Edita Meškinytė, Ramūnas Antanatis, Algimantas Paulauskas and Vida Juozaitienė
Animals 2024, 14(10), 1444; https://doi.org/10.3390/ani14101444 - 13 May 2024
Viewed by 889
Abstract
This study aimed to assess the relationship between the length of the dry period (DL) and the risks of dystocia and stillbirths in Holstein cows. A total of 1072 healthy cows in lactations 1 and ≥2 were categorized based on the DL (mean: [...] Read more.
This study aimed to assess the relationship between the length of the dry period (DL) and the risks of dystocia and stillbirths in Holstein cows. A total of 1072 healthy cows in lactations 1 and ≥2 were categorized based on the DL (mean: 58.07 ± 0.33 days) into three groups: short DL (<40 days; 13.0% of cows), medium DL (40–70 days; 60.1%), and long DL (>70 days; 26.9%). Dystocia occurred in 12.4% of cows, while stillbirths accounted for 4.1% of calves. The medium DL group had the highest number of unassisted calvings. The dystocia rates were 11.4% for cows with gestation (GA) ≤ 274 days; 6.0% for cows with GA = 275–282 days; and 19.9% for cows with GA ≥ 283 days (p < 0.001). In season 1 (November–April), dystocia occurred in 15.0% of cases compared to 8.8% in season 2 (May–October) (p < 0.001). The stillbirth rates ranged from 3.6% to 4.0% for cows with medium and long DLs; while those with a short DL had 5.8% (p < 0.05). The stillbirth rates were 5.2% for cows with GA ≤ 274 days; 3.2% for cows with GA = 275–282 days; and 5.1% for cows with GA ≥ 283 days (p < 0.001). Season 1 had a stillbirth rate of 4.8%, while season 2 had a stillbirth rate of 3.3% (p < 0.001). This research provides insights that could assist the dairy industry in making informed decisions to reduce the incidence rates of dystocia and stillbirths in cows. Full article
(This article belongs to the Special Issue Monitoring of Cows: Management and Sustainability)
►▼ Show Figures

Figure 1

11 pages, 1191 KiB  
Article
Spectral Profiling (Fourier Transform Infrared Spectroscopy) and Machine Learning for the Recognition of Milk from Different Bovine Breeds
by Anna Antonella Spina, Carlotta Ceniti, Rosario De Fazio, Francesca Oppedisano, Ernesto Palma, Enrico Gugliandolo, Rosalia Crupi, Sayed Haidar Abbas Raza, Domenico Britti, Cristian Piras and Valeria Maria Morittu
Animals 2024, 14(9), 1271; https://doi.org/10.3390/ani14091271 - 24 Apr 2024
Viewed by 1042
Abstract
The Podolica cattle breed is widespread in southern Italy, and its productivity is characterized by low yields and an extraordinary quality of milk and meats. Most of the milk produced is transformed into “Caciocavallo Podolico” cheese, which is made with 100% Podolica milk. [...] Read more.
The Podolica cattle breed is widespread in southern Italy, and its productivity is characterized by low yields and an extraordinary quality of milk and meats. Most of the milk produced is transformed into “Caciocavallo Podolico” cheese, which is made with 100% Podolica milk. Fourier Transform Infrared Spectroscopy (FTIR) is the technique that, in this research work, was applied together with machine learning to discriminate 100% Podolica milk from contamination of other Calabrian cattle breeds. The analysis on the test set produced a misclassification percentage of 6.7%. Among the 15 non-Podolica samples in the test set, 2 were misclassified and recognized as Podolica milk even though the milk was from other species. The correct classification rate improved to 100% when the same method was applied to the recognition of Podolica and Pezzata Rossa milk produced by the same farm. Furthermore, this technique was tested for the recognition of Podolica milk mixed with milk from other bovine species. The multivariate model and the respective confusion matrices obtained showed that all the 14 Podolica samples (test set) mixed with 40% non-Podolica milk were correctly classified. In addition, Pezzata Rossa milk produced by the same farm was detected as a contaminant in Podolica milk from the same farm down to concentrations as little as 5% with a 100% correct classification rate in the test set. The method described yielded higher accuracy values when applied to the discrimination of milks from different breeds belonging to the same farm. One of the reasons for this phenomenon could be linked to the elimination of the environmental variable. However, the results obtained in this work demonstrate the possibility of using FTIR to discriminate between milks from different breeds. Full article
(This article belongs to the Special Issue Monitoring of Cows: Management and Sustainability)
►▼ Show Figures

Figure 1

14 pages, 2035 KiB  
Article
Application of Near-Infrared Reflectance Spectroscopy for Predicting Chemical Composition of Feces in Holstein Dairy Cows and Calves
by Yiming Xu, Tianyu Chen, Hongxing Zhang, Yiliyaer Nuermaimaiti, Siyuan Zhang, Fei Wang, Jianxin Xiao, Shuai Liu, Wei Shao, Zhijun Cao, Jingjun Wang and Yong Chen
Animals 2024, 14(1), 52; https://doi.org/10.3390/ani14010052 - 22 Dec 2023
Cited by 1 | Viewed by 1366
Abstract
Traditional methods for determining the chemical composition of cattle feces are uneconomical. In contrast, near-infrared reflectance spectroscopy (NIRS) has emerged as a successful technique for assessing chemical compositions. Therefore, in this study, the feasibility of NIRS in terms of predicting fecal chemical composition [...] Read more.
Traditional methods for determining the chemical composition of cattle feces are uneconomical. In contrast, near-infrared reflectance spectroscopy (NIRS) has emerged as a successful technique for assessing chemical compositions. Therefore, in this study, the feasibility of NIRS in terms of predicting fecal chemical composition was explored. Cattle fecal samples were subjected to chemical analysis using conventional wet chemistry techniques and a NIRS spectrometer. The resulting fecal spectra were used to construct predictive equations to estimate the chemical composition of the feces in both cows and calves. The coefficients of determination for calibration (RSQ) were employed to evaluate the calibration of the predictive equations. Calibration results for cows (dry matter [DM], RSQ = 0.98; crude protein [CP], RSQ = 0.93; ether extract [EE], RSQ = 0.91; neutral detergent fiber [NDF], RSQ = 0.82; acid detergent fiber [ADF], RSQ = 0.89; ash, RSQ = 0.84) and calves (DM, RSQ = 0.92; CP, RSQ = 0.89; EE, RSQ = 0.77; NDF, RSQ = 0.76; ADF, RSQ = 0.92; ash, RSQ = 0.97) demonstrated that NIRS is a cost-effective and efficient alternative for assessing the chemical composition of dairy cattle feces. This provides a new method for rapidly predicting fecal chemical content in cows and calves. Full article
(This article belongs to the Special Issue Monitoring of Cows: Management and Sustainability)
►▼ Show Figures

Figure 1

15 pages, 849 KiB  
Article
Associations of Grazing and Rumination Behaviours with Performance Parameters in Spring-Calving Dairy Cows in a Pasture-Based Grazing System
by Muhammad Wasim Iqbal, Ina Draganova, Patrick Charles Henry Morel and Stephen Todd Morris
Animals 2023, 13(24), 3831; https://doi.org/10.3390/ani13243831 - 12 Dec 2023
Viewed by 1680
Abstract
This study investigated the relationship of the length of time spent grazing and ruminating with the performance parameters of spring-calved grazing dairy cows (n = 162) over the lactation period for three lactation seasons (n = 54 per season). The cows were Holstein [...] Read more.
This study investigated the relationship of the length of time spent grazing and ruminating with the performance parameters of spring-calved grazing dairy cows (n = 162) over the lactation period for three lactation seasons (n = 54 per season). The cows were Holstein Friesian (HFR), Jersey (JE), and a crossbreed of Holstein Friesian/Jersey (KiwiCross), with 18 cows from each breed. The cows were either in their 1st, 2nd, 3rd, or 4th lactation year, and had different breeding worth (BW) index values (103 < BW > 151). The cows were managed through a rotational grazing scheme with once-a-day milking in the morning at 05:00 h. The cows were mainly fed on grazed pastures consisting of perennial ryegrass (Lolium perenne), red clover (Trifolium pretense), and white clover (Trifolium repens), and received additional feeds on various days in the summer and autumn seasons. This study used an automated AfiCollar device to continuously record the grazing time and rumination time (min/h) of the individual cows throughout the lactation period (~270 days) for three consecutive years (Year-1, Year-2, and Year-3). The milk yield, milk fat, milk protein, milk solids, liveweight, and body condition score data of the individual animals for the study years were provided by the farm. PROC CORR was used in SAS to determine the correlation coefficients (r) between the behaviour and production parameters. A general linear model fitted with breed × lactation year, individual cows, seasons, feed within the season, grazing time, rumination time, as well as their interactions, was assessed to test the differences in milk yield, milk fat, milk protein, milk solids, liveweight, and body condition score. The type I sum of squares values were used to quantify the magnitude of variance explained by each of the study factors and their interactions in the study variables. Grazing time exhibited positive associations with MY (r = 0.34), MF (r = 0.43), MP (r = 0.22), MS (r = 0.39), LW (r = −0.47), and BCS (r = −0.24) throughout the study years. Rumination time was associated with MY (r = 0.64), MF (r = 0.57), MP (r = 0.52), and MS (r = 0.57) in all study years, while there were no effects of rumination time on LW (r = 0.26) and BCS (r = −0.26). Grazing time explained up to 0.32%, 0.49%, 0.17%, 0.31%, 0.2%, and 0.02%, and rumination time explained up to 0.39%, 6.73%, 4.63%, 6.53%, 0.44%, and 0.17% of the variance in MY, MF, MP, MS, LW, and BCS, respectively. Full article
(This article belongs to the Special Issue Monitoring of Cows: Management and Sustainability)
►▼ Show Figures

Figure 1

28 pages, 6196 KiB  
Article
Health Status Classification for Cows Using Machine Learning and Data Management on AWS Cloud
by Kristina Dineva and Tatiana Atanasova
Animals 2023, 13(20), 3254; https://doi.org/10.3390/ani13203254 - 18 Oct 2023
Cited by 5 | Viewed by 2842
Abstract
The health and welfare of livestock are significant for ensuring the sustainability and profitability of the agricultural industry. Addressing efficient ways to monitor and report the health status of individual cows is critical to prevent outbreaks and maintain herd productivity. The purpose of [...] Read more.
The health and welfare of livestock are significant for ensuring the sustainability and profitability of the agricultural industry. Addressing efficient ways to monitor and report the health status of individual cows is critical to prevent outbreaks and maintain herd productivity. The purpose of the study is to develop a machine learning (ML) model to classify the health status of milk cows into three categories. In this research, data are collected from existing non-invasive IoT devices and tools in a dairy farm, monitoring the micro- and macroenvironment of the cow in combination with particular information on age, days in milk, lactation, and more. A workflow of various data-processing methods is systematized and presented to create a complete, efficient, and reusable roadmap for data processing, modeling, and real-world integration. Following the proposed workflow, the data were treated, and five different ML algorithms were trained and tested to select the most descriptive one to monitor the health status of individual cows. The highest result for health status assessment is obtained by random forest classifier (RFC) with an accuracy of 0.959, recall of 0.954, and precision of 0.97. To increase the security, speed, and reliability of the work process, a cloud architecture of services is presented to integrate the trained model as an additional functionality in the Amazon Web Services (AWS) environment. The classification results of the ML model are visualized in a newly created interface in the client application. Full article
(This article belongs to the Special Issue Monitoring of Cows: Management and Sustainability)
►▼ Show Figures

Figure 1

11 pages, 1589 KiB  
Article
Monitoring of Body Condition in Dairy Cows to Assess Disease Risk at the Individual and Herd Level
by Ramiro Rearte, Santiago Nicolas Lorenti, German Dominguez, Rodolfo Luzbel de la Sota, Isabel María Lacau-Mengido and Mauricio Javier Giuliodori
Animals 2023, 13(19), 3114; https://doi.org/10.3390/ani13193114 - 6 Oct 2023
Viewed by 1196
Abstract
A retrospective longitudinal study assessing the explanatory and predictive capacity of body condition score (BCS) in dairy cows on disease risk at the individual and herd level was carried out. Data from two commercial grazing herds from the Argentinean Pampa were gathered (Herd [...] Read more.
A retrospective longitudinal study assessing the explanatory and predictive capacity of body condition score (BCS) in dairy cows on disease risk at the individual and herd level was carried out. Data from two commercial grazing herds from the Argentinean Pampa were gathered (Herd A = 2100 and herd B = 2600 milking cows per year) for 4 years. Logistic models were used to assess the association of BCS indicators with the odds for anestrus at the cow and herd level. Population attributable fraction (AFP) was estimated to assess the anestrus rate due to BCS indicators. We found that anestrus risk decreased in cows calving with BCS ≥ 3 and losing ≤ 0.5 (OR: 0.07–0.41), and that anestrus rate decreased in cohorts with a high frequency of cows with proper BCS (OR: 0.22–0.45). Despite aggregated data having a good explanatory power, their predictive capacity for anestrus rate at the herd level is poor (AUC: 0.574–0.679). The AFP varied along the study in both herds and tended to decrease every time the anestrous rate peaked. We conclude that threshold-based models with BCS indicators as predictors are useful to understand disease risk (e.g., anestrus), but conversely, they are useless to predict such multicausal disease events at the herd level. Full article
(This article belongs to the Special Issue Monitoring of Cows: Management and Sustainability)
►▼ Show Figures

Figure 1

19 pages, 8270 KiB  
Article
A Study on Differential Biomarkers in the Milk of Holstein Cows with Different Somatic Cells Count Levels
by Yuanhang She, Jianying Liu, Minqiang Su, Yaokun Li, Yongqing Guo, Guangbin Liu, Ming Deng, Hongxian Qin, Baoli Sun, Jianchao Guo and Dewu Liu
Animals 2023, 13(15), 2446; https://doi.org/10.3390/ani13152446 - 28 Jul 2023
Cited by 3 | Viewed by 1508
Abstract
Dairy cow mastitis is one of the common diseases of dairy cows, which will not only endanger the health of dairy cows but also affect the quality of milk. Dairy cow mastitis is an inflammatory reaction caused by pathogenic microorganisms and physical and [...] Read more.
Dairy cow mastitis is one of the common diseases of dairy cows, which will not only endanger the health of dairy cows but also affect the quality of milk. Dairy cow mastitis is an inflammatory reaction caused by pathogenic microorganisms and physical and chemical factors in dairy cow mammary glands. The number of SCC in the milk of dairy cows with different degrees of mastitis will increase in varying degrees. The rapid diagnosis of dairy cow mastitis is of great significance for dairy cow health and farm economy. Based on the results of many studies on the relationship between mastitis and somatic cell count in dairy cows, microflora, and metabolites in the milk of Holstein cows with low somatic cell level (SCC less than 200,000), medium somatic cell level (SCC up to 200,000 but less than 500,000) and high somatic cell level (SCC up to 5000,00) were analyzed by microbiome and metabolic group techniques. The results showed that there were significant differences in milk microbiota and metabolites among the three groups (p < 0.05), and there was a significant correlation between microbiota and metabolites. Meanwhile, in this experiment, 75 differential metabolites were identified in the H group and L group, 40 differential metabolites were identified in the M group and L group, and six differential microorganisms with LDA scores more than four were found in the H group and L group. These differential metabolites and differential microorganisms may become new biomarkers for the diagnosis, prevention, and treatment of cow mastitis in the future. Full article
(This article belongs to the Special Issue Monitoring of Cows: Management and Sustainability)
►▼ Show Figures

Figure 1

16 pages, 1587 KiB  
Article
Influence of Technological Housing Conditions on the Concentration of Airborne Dust in Dairy Farms in the Summer: A Case Study
by Pavel Kic
Animals 2023, 13(14), 2322; https://doi.org/10.3390/ani13142322 - 16 Jul 2023
Cited by 1 | Viewed by 1280
Abstract
This research shows the size composition of airborne dust fractions in selected dairy barns down to the smallest particles, including factors that influence this composition. Measurements with a Dust-Track 8530 laser photometer took place in the summer at external temperatures of 29.5 to [...] Read more.
This research shows the size composition of airborne dust fractions in selected dairy barns down to the smallest particles, including factors that influence this composition. Measurements with a Dust-Track 8530 laser photometer took place in the summer at external temperatures of 29.5 to 36 °C. In barns with straw bedding, the average total dust concentration TDC was 66.98 ± 28.38 μg·m−3 (PM10 60.11 ± 19.93 μg·m−3, PM4 49.48 ± 13.76 μg·m−3, PM2.5 44.78 ± 10.18 μg·m−3, and PM1 38.43 ± 9.29 μg·m−3). In barns without straw bedding, the average TDC was 55.91 ± 36.6 μg·m−3, PM10 33.71 ± 13.86 μg·m−3, PM4 30.69 ± 15.29 μg·m−3, PM2.5 27.02 ± 13.38 μg·m−3, and PM1 22.93 ± 10.48 μg·m−3. The largest TDC of 108.09 ± 32.93 μg·m−3 (PM10 69.80 ± 18.70 μg·m−3, PM4 68.20 ± 18.41 μg·m−3, PM2.5 53.27 ± 14.73 μg·m−3, and PM1 38.46 ± 5.55 μg·m−3) was measured in an old cowshed with stanchion housing for 113 cows, straw bedding, and ventilation through windows. In a modern cowshed for loose housing of 440 lactating cows without straw bedding, with natural ventilation and 24 axial fans, TDC was 53.62 ± 49.52 μg·m−3, PM10 20.91 ± 5.24 μg·m−3, PM4 17.11 ± 3.23 μg·m−3, PM2.5 13.71 ± 0.92 μg·m−3, and PM1 12.69 ± 2.82 μg·m−3. In all investigated barns, a large proportion of airborne dust particles (54.38 ± 20.82% of TDC) consists of the smallest PM1 dust particles (from 12.69 ± 2.82 μg·m−3 to 48.48 ± 1.18 μg·m−3). Full article
(This article belongs to the Special Issue Monitoring of Cows: Management and Sustainability)
►▼ Show Figures

Figure 1

12 pages, 2545 KiB  
Article
Measurement of Ammonia and Hydrogen Sulfide Emission from Three Typical Dairy Barns and Estimation of Total Ammonia Emission for the Chinese Dairy Industry
by Zhifang Shi, Lei Xi and Xin Zhao
Animals 2023, 13(14), 2301; https://doi.org/10.3390/ani13142301 - 13 Jul 2023
Cited by 1 | Viewed by 1428
Abstract
There is an urgent need for accurate measurement for emissions of ammonia (NH3) and hydrogen sulfide (H2S) in dairy barns in order to obtain reliable emission inventories and to develop and evaluate abatement strategies. This experiment was performed on [...] Read more.
There is an urgent need for accurate measurement for emissions of ammonia (NH3) and hydrogen sulfide (H2S) in dairy barns in order to obtain reliable emission inventories and to develop and evaluate abatement strategies. This experiment was performed on three dairy farms in central China during 14 consecutive days in the winter 2020. Concentrations of NH3 and H2S were measured every two hours. The samples were taken inside and outside of barns from 7 sites at two heights (at floor and 1.5 over the floor). The results show that the average NH3 concentration was 2.47 mg/m3 with a maximum of 4.62 mg/m3, while the average H2S concentration was 0.179 mg/m3 with a maximum of 0.246 mg/m3. Lactating cows produced significantly more NH3 (3.73 mg/m3 versus 2.34 mg/m3) and H2S (0.24 mg/m3 versus 0.14 mg/m3) than non-lactating cows. NH3 and H2S concentrations were higher at 0 m than at 1.5 m, especially during the day. In addition, the average daily emission rates per animal unit (AU = 500 kg weight) were 23.5 g and 0.21 g for NH3 and H2S, respectively. The emission rate for NH3 was then used to extrapolate the NH3 emission from the Chinese dairy production. Our estimation for 2016 was 0.45 Tg, and it could reach 1.35 Tg by 2050. These numbers reflected our first attempt to calculate emission inventories for the Chinese dairy industry. Our results also suggest that more concrete measures must be taken to reduce the uncertainties of NH3 emissions from dairy cow production in China. Full article
(This article belongs to the Special Issue Monitoring of Cows: Management and Sustainability)
►▼ Show Figures

Figure 1

11 pages, 1062 KiB  
Article
A Longitudinal Study with a Laser Methane Detector (LMD) Highlighting Lactation Cycle-Related Differences in Methane Emissions from Dairy Cows
by Ana Margarida Pereira, Pedro Peixoto, Henrique J. D. Rosa, Carlos Vouzela, João S. Madruga and Alfredo E. S. Borba
Animals 2023, 13(6), 974; https://doi.org/10.3390/ani13060974 - 8 Mar 2023
Cited by 4 | Viewed by 2652
Abstract
Reversing climate change requires broad, cohesive, and strategic plans for the mitigation of greenhouse gas emissions from animal farming. The implementation and evaluation of such plans demand accurate and accessible methods for monitoring on-field CH4 concentration in eructating breath. Therefore, this paper [...] Read more.
Reversing climate change requires broad, cohesive, and strategic plans for the mitigation of greenhouse gas emissions from animal farming. The implementation and evaluation of such plans demand accurate and accessible methods for monitoring on-field CH4 concentration in eructating breath. Therefore, this paper describes a longitudinal study over six months, aiming to test a protocol using a laser methane detector (LMD) to monitor CH4 emissions in semi-extensive dairy farm systems. Over 10 time points, CH4 measurements were performed in dry (late gestation) and lactating cows at an Azorean dairy farm. Methane traits including CH4 concentration related to eructation (E_CH4) and respiration (R_CH4), and eructation events, were automatically computed from CH4 measured values using algorithms created for peak detection and analysis. Daily CH4 emission was estimated from each profile’s mean CH4 concentration (MEAN_CH4). Data were analyzed using a linear mixed model, including breed, lactation stage, and parity as fixed effects, and cow (subject) and time point as random effects. The results showed that Holsteins had higher E_CH4 than Jersey cows (p < 0.001). Although a breed-related trend was found in daily CH4 emission (p = 0.060), it was not significant when normalized to daily milk yield (p > 0.05). Methane emissions were lower in dry than in lactation cows (p < 0.05) and increased with the advancement of the lactation, even when normalizing it to daily milk yield (p < 0.05). Primiparous cows had lower daily CH4 emissions related to R_ CH4 compared to multiparous (p < 0.001). This allowed the identification of periods of higher CH4 emissions within the milk production cycle of dairy cows, and thus, the opportunity to tailor mitigation strategies accordingly. Full article
(This article belongs to the Special Issue Monitoring of Cows: Management and Sustainability)
►▼ Show Figures

Figure 1

Other

Jump to: Research

28 pages, 3878 KiB  
Systematic Review
Assessment of Ammonia Emissions and Greenhouse Gases in Dairy Cattle Facilities: A Bibliometric Analysis
by Patricia Ferreira Ponciano Ferraz, Gabriel Araújo e Silva Ferraz, Jacqueline Cardoso Ferreira, João Victor Aguiar, Lucas Santos Santana and Tomas Norton
Animals 2024, 14(12), 1721; https://doi.org/10.3390/ani14121721 - 7 Jun 2024
Cited by 1 | Viewed by 1215
Abstract
A deeper understanding of gas emissions in milk production is crucial for promoting productive efficiency, sustainable resource use, and animal welfare. This paper aims to analyze ammonia and greenhouse gas emissions in dairy farming using bibliometric methods. A total of 187 English-language articles [...] Read more.
A deeper understanding of gas emissions in milk production is crucial for promoting productive efficiency, sustainable resource use, and animal welfare. This paper aims to analyze ammonia and greenhouse gas emissions in dairy farming using bibliometric methods. A total of 187 English-language articles with experimental data from the Scopus and Web of Science databases (January 1987 to April 2024) were reviewed. Publications notably increased from 1997, with the highest number of papers published in 2022. Research mainly focuses on ammonia and methane emissions, including quantification, volatilization, and mitigation strategies. Other gases like carbon dioxide, nitrous oxide, and hydrogen sulfide were also studied. Key institutions include the University of California–Davis and Aarhus University. Bibliometric analysis revealed research evolution, identifying trends, gaps, and future research opportunities. This bibliometric analysis offers insights into emissions, air quality, sustainability, and animal welfare in dairy farming, highlighting areas for innovative mitigation strategies to enhance production sustainability. This research contributes to academia, enhancing agricultural practices, and informing environmental policies. It is possible to conclude that this research is a valuable tool for understanding the evolution of research on gas emissions in dairy cattle facilities, providing guidance for future studies and interventions to promote more sustainable production. Full article
(This article belongs to the Special Issue Monitoring of Cows: Management and Sustainability)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-14
Back to TopTop