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.

Spatiotemporal variability in human thermal comfort perception in open-air spaces: application to the state of Minas Gerais, Brazil.

The objective of this study was to propose bioclimatic zoning to classify human thermal comfort and discomfort in the state of Minas Gerais, Brazil; both historical and future scenarios are considered. Thus, historical series (1961 to 2017) of the effective temperature index as a function of the wind (ETW) were obtained as a function of the monthly average values of the minimum, mean, and maximum dry-bulb air temperatures (tdb,min, tdb,mean, and tdb,max, respectively), in addition to the mean relative humidity ([Formula: see text], %) and mean wind speed ([Formula: see text], m s -1). The data were obtained from 34 weather stations and subjected to trend analysis by using the nonparametric Mann-Kendall test, thus enabling the simulation of future scenarios (for 2028 and 2038). Then, to define the thermal ranges of the bioclimatic zoning, maps of ETWmin, ETWmean, and ETWmax were created from geostatistical analysis. Overall, the results show warming trends for the upcoming years in Minas Gerais municipalities. All climatic seasons showed an increase in the frequency of new classifications in the upper adjacent classes, which indicates climate warming. Therefore, when considering future scenarios for the autumn and winter seasons, attention should be given to changes in predicted thermal sensation, especially in the Central Minas Gerais, Belo Horizonte Metropolitan, South/Southwest Minas, Campo das Vertentes, and Zona da Mata.

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.

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.

Mapping of the Thermal Microenvironment for Dairy Cows in an Open Compost-Bedded Pack Barn System with Positive-Pressure Ventilation.

The objective of this study was to evaluate and characterize the dependence and the spatial and temporal distribution of variables and indices of the thermal environment in an open compost-bedded pack barn system with positive-pressure ventilation (CBPPV) during the winter period. The study was conducted in a CBPPV system located in the Zona da Mata region, Minas Gerais, Brazil. The indoor environment was divided into a mesh composed of 55 equidistant points, where data on dry-bulb air temperature (tdb) and relative humidity (RH) were collected. The collected data were divided into four periods-dawn, morning, afternoon, and night-and mean values were obtained. To evaluate the thermal microenvironment, the temperature and humidity index (THI) and the specific enthalpy of air (h) were used. For spatial dependence analysis, geostatistical techniques were applied. Through the results, a strong spatial dependence was verified for all variables evaluated. Through THI and h maps, conditions of thermal comfort were found for dairy cattle. The highest values of tdb, THI, and h were recorded in the afternoon period in the northwest region of the facility (tdb = 23.2 °C, THI = 69.7, and h = 50.9 kJ∙kg of dry air-1).

Spatial distribution of bed variables, animal welfare indicators, and milk production in a closed compost-bedded pack barn with a negative tunnel ventilation system.

This research aimed to characterize, evaluate and compare the spatial distribution of the leading bed variables, animal welfare indicators, and milk production in a closed compost-bedded pack barn (CBP) with a negative tunnel ventilation system, for summer and winter periods. The study was carried out in a CBP located in the Zona da Mata region, Minas Gerais, Brazil. The geostatistical modeling technique evaluated the variables of temperature, moisture content, and pH (on the surface and depth of 0.20m) across the length of the bed. Bed samples were characterized for carbon (C), nitrogen (N), and C:N ratio. Cows housed in the CBP were assessed for locomotion and hygiene scores and average milk production. To evaluate the thermoregulation of the cows, the respiratory rate (RR) and surface temperature (ST) were measured. Geostatistical analysis showed spatial dependence and the non-uniformity of the spatial distribution of bed variables. The worst levels of bed temperature and moisture were found in the regions close to the evaporative cooling plate, surrounding the feeding alley, and in the region with the highest cow stocking. The C:N ratio, obtained in both climatic seasons of the year, remained outside the recommended range for ideal composting. During the summer and winter, the bed variables' values suggest that the material was below levels for optimal composting; however, the aerated inner layer was biologically active. The high animal density significantly impacted the worsening of the bed moisture content and internal temperature. In general, dairy cows showed adequate hygiene (score of 1 and 2) and locomotion (score of 0 and 1) scores for the two climatic seasons evaluated, indicating good welfare conditions. In relation to RR and ST, the summer period presented less favorable environmental conditions. During winter, the average milk production was 28.1 ± 7.2 kg day-1, and during summer, it was 26.9 ± 6.7 kg day-1.

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.