Submit to Special Issue Submit Abstract to Special Issue Review for Animals Propose a Special Issue

Journal Browser

► Journal Browser

Monitoring of Cows: Management and Sustainability

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers
Planned Papers

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

Deadline for manuscript submissions: 10 June 2024 | Viewed by 10875

Share This Special Issue

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

Published Papers (9 papers)

Download All Papers

Research

11 pages, 1191 KiB

Open AccessArticle

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 339

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. 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

Open AccessArticle

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

Viewed by 833

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 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

Open AccessArticle

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 1058

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 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

Open AccessArticle

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 1 | Viewed by 1821

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 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

Open AccessArticle

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 781

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 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 (AF_P) 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 AF_P 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

Open AccessArticle

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

Viewed by 1012

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 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

Open AccessArticle

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 925

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 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, PM_2.5 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, PM_2.5 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, PM_2.5 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, PM_2.5 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

Open AccessArticle

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

Viewed by 1009

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 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

Open AccessArticle

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 2 | Viewed by 2035

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 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

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Assessment of Ammonia Emissions and Greenhouse Gases in Dairy Cattle Facilities: A Bibliometric Analysis
Authors: Patricia Ferreira Ponciano Ferraz; Gabriel Araújo e Silva Ferraz; Jacqueline Cardoso Ferreira; João Victor Aguiar; Lucas Santos Santana; Tomas Norton
Affiliation: Universidade Federal de Lavras
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 NH3 and greenhouse gas emissions in dairy farming using bibliometric methods. 183 English-language arti-cles with experimental data from Scopus and Web of Science databases (January 1987 to April 2024) were reviewed. Publications notably increased from 1997, with the highest number of papers pub-lished in 2022. Research mainly focuses on NH3 and CH4 emissions, including quantification, vo-latilization, and mitigation strategies. Other gases like CO2, N2O, and H2S were also studied. In-tensive Free Stall installations are common, with the U.S. leading research. Key institutions include University of California-Davis and Aarhus University. Bibliometric analysis revealed research evolution, identifying trends, gaps, and future research opportunities. The study offers insights into emissions, air quality, sustainability, and animal welfare in dairy farming, highlighting areas for innovative mitigation strategies to enhance production sustainability.

Title: Digital and Precision Technologies in Dairy Cattle Farming: A Bibliometric Analysis
Authors: Franck Morais de Oliveira; Gabriel Araújo e Silva Ferraz; Ana Luíza Guimarães André; Lucas Santos Santana; Tomas Norton; Patrícia Ferreira Ponciano Ferraz
Affiliation: Department of Agricultural Engineering, Federal University of Lavras (UFLA), Lavras 37200-900, Brazil
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 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 onwards and considering the end date of the search in November 2023, the date of the research, in the scientific databases Scopus and Web of Science. Next, the Bibliometrix package in R and its Biblioshiny software extension were used as a graphical interface, in addition to the VosViewer 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 was analyzed that the authors currently focus on technologies such as machine learning, deep learning and computer vision for behavioral monitoring, identification of livestock and management of thermal stress caused to these animals, to make important decisions to promote health and efficiency in the production of dairy cattle, contributing to more sustainable practices focused on animal welfare.

Journal Menu

Journal Browser

Monitoring of Cows: Management and Sustainability

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (9 papers)

Research

Planned Papers

Further Information

Guidelines

MDPI Initiatives

Follow MDPI