Assessment of Ammonia Emissions and Greenhouse Gases in Dairy Cattle Facilities: A Bibliometric Analysis.

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.

Digital and Precision Technologies in Dairy Cattle Farming: A Bibliometric Analysis.

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.

Thermoregulation and Performance of Dairy Cows Subjected to Different Evaporative Cooling Regimens, with or without Pepper Extract Supplementation.

The objective of this study was to evaluate whether there is an interaction between pepper extract (PE) supplementation and evaporative cooling regimens on the performance, thermal comfort, and metabolism of lactating cows. The experiment was designed as a double 4 × 4 Latin square using eight multiparous Holstein cows (days in milk 147 ± 43.8 days). Treatments were a 2 × 2 factorial arrangement; two evaporative cooling regimens [sprinkler cycles of 30 s on and 4.5 min off (R5) and 30 s on and 9.5 min off (R10)] were combined with no inclusion of pepper extract (CT) or 800 mg/cow daily of PE. The inclusion of PE allowed a greater reduction in the surface temperature of the coat, and the cows remained for longer periods in the feeding area. There was an interaction between the use of PE and the climate regimen for surface temperature, which was lower for PER5. Cows experiencing greater intermittency in the spraying cycles (R10) spent 35% more time at the feeding area. A significant interaction was observed for milk production, with greater production for CTR5 compared to the other treatments. The feed efficiency for R5 was higher than that for R10. The R5 treatment combined with PE reduced water intake. There was no significant difference in serum parameters other than gamma-glutamyl transferase, with an interaction between treatments and greater activity for PER10, and total protein and albumin, which had cooling effects that were higher for R10. The two factors tested interfered in different and dissociated ways. The evaporative cooling strategies were effective, and the R5 treatment resulted in higher feed efficiency and milk production. The inclusion of PE enhanced heat reduction in cows when they were subjected to a cooling regime.

Compost Barns: A Bibliometric Analysis.

The objective of this study was to evaluate the main scientific studies addressing the topic of compost barns in recent years, highlighting the main journals, authors, countries, organizations, and keywords associated with the publications and trends in this type of research through a bibliometric analysis. For this analysis, publications (articles and literature reviews) addressing compost barns were obtained from the Scopus and Web of Science databases. A total of 108 studies published between 2007 and April 2022 were included. A greater number of publications was observed starting in 2017, with 2021 having the largest number of publications. The Journal of Dairy Science was the most highly cited journal. Marcia I. Endres was the author with the greatest academic impact. The most influential country was the United States, followed by Brazil. Among the organizations that have published studies on compost barns, the Federal University of Lavras, and the University of Florence had the largest numbers of publications. In conclusion, this bibliometric analysis allowed us to evaluate the scientific evolution, research, and publishing tendencies of studies on the compost barn animal housing system, and the results make it possible to deduce current trends in scientific research and publications.

Thermal Environment and Behavior Analysis of Confined Cows in a Compost Barn.

The compost barn system has become popular in recent years for providing greater animal well-being and quality of life, favoring productivity and longevity. With the increase in the use of compost barn in dairy farms, studies related to the thermal environment and behavior are of paramount importance to assess the well-being of animals and improve management, if necessary. This work aimed to characterize the thermal environment inside a compost barn during the four seasons of a year and to evaluate the standing and lying behavior of the cows through images. The experiment was carried out during March (summer), June (autumn), August (winter), and November (spring). Dry bulb temperature (tdb, °C), dew point temperature (tdp, °C), and relative humidity (RH,%) data were collected every 10 minutes during all analyzed days, and the temperature and humidity index (THI) was subsequently calculated. In order to analyze the behavior of the cows, filming of the barn interior was carried out during the evaluated days. Subsequently, these films were analyzed visually, and in an automated way to evaluate the behavior of these animals. For the automated analysis, an algorithm was developed using artificial intelligence tools, YOLOv3, so that the evaluation process could be automated and fast. It was observed that during the experimental period, the highest mean values of THI were observed during the afternoon and the autumn. The animals' preference to lie down on the bed for most of the day was verified. It was observed that the algorithm was able to detect cow behavior (lying down or standing). It can be concluded that the behavior of the cows was defined, and the artificial intelligence was successfully applied and can be recommended for such use.

The Welfare of Dairy Cows in Pasture, Free Stall, and Compost Barn Management Systems in a Brazilian Subtropical Region.

The effect of milk production systems on the welfare of dairy cows has been studied worldwide, but studies that compare pasture-based, compost barn, and free stall systems, according to animal welfare, are more scarce. In this work, the welfare of 51 dairy herds, including 17 from each management system, was investigated through the application of the Welfare Quality® protocol. Descriptive statistics and the Kruskal-Wallis non-parametric method were used to analyze variables. In the present work, the welfare of the evaluated herds was significantly better in the pasture-based system than in the confinement systems. However, the pasture-based system presented weaknesses in providing water resources. The compost barn had fewer animal welfare critical points than the free stall system, as well as it was better than the free stall in measures related to comfort and health. The free stall did not present better scores than the other systems. It is concluded that the welfare of dairy cows is affected by the rearing system, with better scores, in most measures, in the pasture-based system, followed by the compost barn and, finally, the free stall.

Mapping Soil and Pasture Attributes for Buffalo Management through Remote Sensing and Geostatistics in Amazon Biome.

The mapping of pastures can serve to increase productivity and reduce deforestation, especially in Amazon Biome regions. Therefore, in this study, we aimed to explore precision agriculture technologies for assessing the spatial variations of soil pH and biomass indicators (i.e., Dry Matter, DM; and Green Matter, GM). An experiment was conducted in an area cultivated with Panicum maximum (Jacq.) cv. Mombaça in a rotational grazing system for dairy buffaloes in the eastern Amazon. Biomass and soil samples were collected in a 10 m × 10 m grid, with a total of 196 georeferenced points. The data were analyzed by semivariogram and then mapped by Kriging interpolation. In addition, a variability analysis was performed, applying both the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) derived from satellite remote sensing data. The Kriging mapping between DM and pH at 0.30 m depth demonstrated the best correlation. The vegetative index mapping showed that the NDVI presented a better performance in pastures with DM production above 5.42 ton/ha-1. In contrast, DM and GM showed low correlations with the NDWI. The possibility of applying a variable rate within the paddocks was evidenced through geostatistical mapping of soil pH. With this study, we contribute to understanding the necessary premises for utilizing remote sensing data for pasture variable analysis.

Three-Dimensional Simulation of the Temperature Distribution in a Commercial Broiler House.

The aim of this paper was to analyze, using computational fluid dynamics (CFD), a heating system in a commercial broiler house. Data were collected in a broiler house located in the western mesoregion of Minas Gerais, Brazil. The data were collected at 10 a.m. on the seventh day of chicks' life in 16 points inside the house. A tetrahedral mesh was adopted for the simulation, and testing of the mesh yielded a geometry of 485,691 nodes. The proposed model was developed in a permanent state condition to simulate the temperature air inside the broiler house, and all other input variables were considered constant. The applied CFD technique resulted in satisfactory fitting of the air temperature variable along the broiler facility as a function of the input data. The results indicated that the model predicted the environmental conditions inside the broiler house very accurately. The mean error of the CFD model was 1.49%, indicating that the model is effective and therefore that it can be used in other applications. The results showed that the heating system provided favorable thermoneutral conditions for chicks in the biggest part of the broiler house. However, there were some areas with air temperature above and below the thermoneutral zone.

Effect of the Spatial Distribution of the Temperature and Humidity Index in a New Zealand White Rabbit House on Respiratory Frequency and Ear Surface Temperature.

The objective of this study was to characterize and evaluate the temperature and humidity index (THI) of New Zealand white (NZW) rabbits kept in a rabbit house using geostatistical techniques. Furthermore, we sought to evaluate its relationship with respiratory frequency (RF) and ear surface temperature (EST). The experiment was conducted at the Federal University of Lavras, Brazil. A total of 52 NZW rabbits were used. For the characterization of the thermal environment, the dry bulb temperature (tdb, °C), relative humidity (RH, %), and dew point temperature (tdp, °C) were collected at 48 points in the rabbit house at 6:00 a.m., 12:00 p.m., and 6:00 p.m. for seven days. The RF and EST of the animals was monitored. Subsequently, the THI was calculated and the data were analyzed using geostatistical tools and kriging interpolation. In addition, the RF and EST data were superimposed on the rabbit house's THI data maps. The magnitude of the variability and structure of the THI inside the rabbit house were characterized and the heterogeneity was visualized. Critical THI points inside the rabbit house and in locations where animals with high RF and ESTs were housed were identified, thus providing information about improving the production environment.

Properties of conventional and alternative bedding materials for dairy cattle.

The bedding material used in barns for dairy cows has a significant effect on animal welfare and performance. Bedding influences the duration in which animals remain lying down and, consequently, the processes of rumination and milk production. It is crucial to have a complete understanding of the properties of bedding materials and the effects of alternative bedding materials on dairy cattle. This paper aims to evaluate the physical, chemical, and biological properties of various alternative and conventional bedding materials for dairy cattle for use in compost bedded pack or freestall barn systems. We analyzed 50 samples of 17 bedding materials produced in 3 European countries. We analyzed physical properties including the water holding capacity, porosity, moisture content, bulk density, dry bulk density, and particle size. Chemical analyses were performed to determine the total N, total organic C, and C:N ratio. In the biological analyses, the Escherichia coli count, total bacteria count, coliform count, and Klebsiella spp. count were assessed. The results demonstrated how the physical properties of the bedding materials may influence the chemical and biological properties. All of the materials presented adequate chemical properties to be used as bedding material. The physical properties of the bedding materials differed widely among the materials except for the dry bulk density, which presented no difference. Moreover, the contamination of each studied microorganism was observed for each bedding material to determine which material had the lowest level of contamination. Posidonia oceanica, Miscanthus grass, and spelt husks could be considered as a potential alternative material for use as bedding material for dairy cows in both systems (i.e., composted bedded pack and freestall). This experiment illustrated the importance of performing thorough physical, chemical, and biological analyses before implementing a material as bedding for dairy cattle.

Decision Trees for Predicting the Physiological Responses of Rabbits.

The thermal environment inside a rabbit house affects the physiological responses and consequently the production of the animals. Thus, models are needed to assist rabbit producers in decision-making to maintain the production environment within the zone of thermoneutrality for the animals. The aim of this paper is to develop decision trees to predict the physiological responses of rabbits based on environmental variables. The experiment was performed in a rabbit house with 26 rabbits at eight weeks of age. The experimental database is composed of 546 observed data points. Sixty decision tree models for the prediction of respiratory rate (RR, mov.min-1) and ear temperature (ET, °C) of rabbits exposed to different combinations of dry bulb temperature (tdb, °C) and relative humidity (RH, %) were developed. The ET model exhibited better statistical indices than the RR model. The developed decision trees can be used in practical situations to provide a rapid evaluation of rabbit welfare conditions based on environmental variables and physiological responses. This information can be obtained in real time and may help rabbit breeders in decision-making to provide satisfactory environmental conditions for rabbits.