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.

NMDA Receptor Activation and Ca2+/PKC Signaling in Nicotine-Induced GABA Transport Shift in Embryonic Chick Retina.

Nicotinic receptors are present in the retina of different vertebrates, and in the chick retina, it is present during early development throughout to post-hatching. These receptors are activated by nicotine, an alkaloid with addictive and neurotransmitter release modulation properties, such as GABA signaling. Here we evaluated the mechanisms of nicotine signaling in the avian retina during the development of neuron-glia cells at a stage where synapses are peaking. Nicotine almost halved [3H]-GABA uptake, reducing it by 45% whilst increasing more than two-fold [3H]-GABA release in E12 embryonic chick retinas. Additionally, nicotine mediated a 33% increase in [3H]-D-aspartate release. MK-801 50 µM blocked 66% of nicotine-induced [3H]-GABA release and Gö 6983 100 nM prevented the nicotine-induced reduction in [3H]-GABA uptake by rescuing 40% of this neurotransmitter uptake, implicating NMDAR and PKC (respectively) in the nicotinic responses. In addition, NO-711 prevented [3H]-GABA uptake and release induced by nicotine. Furthermore, the relevance of calcium influx for PKC activation was evidenced through fura-2 imaging. We conclude that the shift of GABA transport mediated by nicotine promotes GABA release by inducing transporter reversal via nicotine-induced EAA release through EAATs, or by a direct effect of nicotine in activating nicotinic receptors permeable to calcium and promoting PKC pathway activation and shifting GAT-1 activity, both prompting calcium influx, and activation of the PKC pathway and shifting GAT-1 activity.

Classifiers based on artificial intelligence in the prediction of recently planted coffee cultivars using a Remotely Piloted Aircraft System.

The classification and prediction methods through artificial intelligence algorithms are applied in different sectors to assist and promote intelligent decision-making. In this sense, due to the great importance in the cultivation, consumption and export of coffee in Brazil and the technological application of the Remotely Piloted Aircraft System (RPAS) this study aimed to compare and select models based on different data classification techniques by different classification algorithms for the prediction of different coffee cultivars (Coffea arabica L.) recently planted. The attributes evaluated were height, crown diameter, total chlorophyll content, chlorophyll A and chlorophyll B, Foliar Area Index (LAI) and vegetation indexes NDVI, NDRE, MCARI1, GVI, and CI in six months. The data were prepared programming language Python using algorithms of Decision Trees, Random Forest, Support Vector Machine and Neural Networks. It was evaluated through cross-validation in all methods, the distribution by FreeViz, the hit rate, sensitivity, specificity, F1 score, and area under the ROC curve and percentage and predictive performance difference. All algorithms showed good hits and predictions for coffee cultivars (0.768% Decision Tree, 0.836% Random Forest, 0.886 Support Vector Machine and 0.899 Neural Networks) and the Neural Networks algorithm produced more accurate predictions than other tested algorithm models, with a higher percentage of hits for the classes considered.

Air velocity spatial variability in open Compost-Bedded Pack Barn system with positive pressure ventilation.

In Compost-Bedded Pack Barn (CBP) systems, air velocity is linked with the thermal comfort of housed dairy cattle and bedding quality and, therefore, assessing ventilation efficiency is essential. In this context, the objective of this study was to evaluate and characterize dependence and spatial distribution of air velocity at the 1.5 m height (vair,M) and at bedding level (vair,B) in an open CBP system with positive pressure ventilation. The study was conducted in 2021, in a facility located in the Zona da Mata region, Minas Gerais, Brazil. The facility area was divided into a mesh composed of 55 equidistant points, where vair,M and vair,B data were collected in the morning (09:00 a.m.) and afternoon (03:00 p.m.) periods, during three weeks in Brazilian winter. Geostatistics techniques were used to assess dependence and spatial distribution. In both periods evaluated, there were a strong occurrence of spatial dependence and non-uniform vair,M and vair,B distributions. The vair,M and vair,B values were lower than recommended (1.8 m∙s-1) in more than 65.0% of the area. Adequate ventilation levels were observed only in the first 20.0 m of the facility, from Southeast to Northwest, because of the fan lines present.

Spatial variability of soil physical properties in longitudinal profiles.

The spatial variability of physical properties, such as bulk density, penetration resistance and gravimetric moisture, obtained by applying geostatistics in precision agriculture, can effectively indicate the physical behavior of agricultural soils in longitudinal profiles. In this way, the spatial dependence of physical properties in streets of coffee plantations with different lengths was evaluated in the southern Minas Gerais, Brazil. For this purpose, five longitudinal profiles were measured in streets, each one with depths ranging from 0 to 0.60 m, in six layers of 0.10 m, being the database composed of 432 property, 144 by property, submitted to the ordinary kriging geostatistical method in order to obtain spatial variability maps using the R software. They were evaluated by the lower mean cross-validation error of theoretical models fitted by ordinary least squares (OLS), being detected in higher superficial layers, from 0 to 0.30 m, lower bulk density and lower penetration resistance, with variable gravimetric moisture in the length direction of some streets of coffee plantations, being that these properties presented different structures of spatial dependence for each street.

Spatial analysis of microclimatic variables in compost-bedded pack barn with evaporative tunnel cooling.

In this study, we aimed to assess the spatial variability of microclimate inside a closed compost-bedded pack barn (CBP) with a negative ventilation system during summer and winter. The research was carried out in a CBP located in the Zona da Mata region, Minas Gerais, Brazil. For each of the stations analyzed, the following environmental mean variables observed inside a CBP were measured: air dry-bulb temperature (tdb), air relative humidity (RH), and windspeed, Temperature-Humidity index, and specific enthalpy. The kriging maps showed that the most critical housing conditions in the thermal environment were found, mainly, from the central part of the CBP, close to the exhaust fans. The analyses also pointed out that the system presented temperature gradients along the length, up to 3°C. During the summer afternoon, the entire region of the CBP was in a discomfort situation (tdb>26°C; RH>75%). During the winter, the measured environmental data remained within the comfort zone throughout the facility. However, probably due to the lack of thermal insulation of the material used to close the sides of the CBP, it did not allow spatial thermal uniformity for both seasons. It was also inefficient to keep the animals within the comfort zone for lactating cattle during the critical summer period.

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.

Assessment of spatial variability of bedding variables in compost bedded pack barns with climate control system.

The aim of this research was to evaluate the spatial distribution of bedding variables in a climate-controlled compost bedded pack barn (CBP) equipped with an evaporative cooling system associated with a tunnel ventilation mode. The study was conducted on a farm in the West Mesoregion of Minas, MG, Brazil. The interior of the animal facility was divided into a mesh of 120 equidistant points, where the bedding surface temperature (tb-sur), the bedding temperature at 0.20 m depth (tb-20) and the bedding penetration resistance (PRb) of layer 0 to 0.20 m depth were measured. Bedding samples were collected to obtain the moisture (Mb) and pH in the surface and at 0.20 m depth. Geostatistics technique was used to evaluate the dependence and spatial distribution. Through the bedding area, the tb-sur presented low variability, with 6 ºC of amplitude, and tb-20 presented highest values (up to 55 °C). The spatial distribution of Mb-20 was similar to that observed on the surface and its highest levels occurred in the region near the feed alley (> 40%). The distribution of pH was similar in both layers. The tendency of high PRb occurred in the layer between 0.15 and 0.20 m (0 and 1500 kPa).

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.

Comparative economic analysis of soil sampling methods used in precision agriculture.

Precision agriculture is an alternative for reducing costs. This study evaluated and economically compared three sampling methods used in precision agriculture with respect to the acquisition of inputs and machines and equipment. The sampling methods used were zone management by elevation (ZME), grid sampling (GS) and sampling guided by apparent electrical conductivity of the soil (OS). Soil samples for the ZME were collected after the definition of zones according to the elevations of the plots. The sample mesh was in a georeferenced mesh of 100 x 100 m. The targeted sampling was performed after a ground proximity sensor was used to identify the apparent electrical conductivity of the soil to define the management areas. From the results of the laboratory tests, the application costs were calculated for lime, phosphorus, potassium and nitrogen to allow a comparison between the methods, volumes and costs. This approach considered the costs of depreciation, insurance, interest, operating costs, labor, maintenance and fuel. With this study, it was possible to compare the volumes of the recommended fertilizers and estimate the overall economic cost of using the technology via sensor. Taking the GS as a reference, the ZME presented as the best alternative compared to other methods.

Characterization of noise emitted by a low-profile tractor and its influence on the health of rural workers.

Noise is one of the main physical agents in the rural work environment and it can be harmful to the health of workers exposed to it. Thus, research on the spatialization of noise emitted by agricultural machinery can help minimize harmful health effects. This study sets out to evaluate the spatial distribution of the noise emitted by an agricultural tractor with and without the activation of a rotary cutter. A wave model geostatistical analysis was used to identify healthy working zones according to current legislation. The experiment was carried out using a 75 hp tractor at 2000 rpm with the rotary cutter on the on and off modes. A digital sound level meter was used to register noise in a regular 2.0 x 2.0 m sample mesh within a 10-meter radius. The semivariogram was adjusted using the weighted least squares method, wave model and kriging interpolation to obtain the maps. The magnitude and spatial structure of the noise emitted by the tractor were identified. The results show that the equipment produced noise levels above the limits recommended by Brazilian regulatory standard NR 15. Thus, both the machine operator and the workers involved in the operation should use Personal Protective Equipment.

Spin Chain Network Construction of Chiral Spin Liquids.

We show that a honeycomb lattice of Heisenberg spin-1/2 chains with three-spin junction interactions allows for controlled analytical studies of chiral spin liquids (CSLs). Tuning these interactions to a chiral fixed point, we find a Kalmeyer-Laughlin CSL phase which here is connected to the critical point of a boundary conformal field theory. Our construction directly yields a quantized spin Hall conductance and localized spinons with semionic statistics as elementary excitations. We also outline the phase diagram away from the chiral point where spinons may condense. Generalizations of our approach can provide microscopic realizations for many other CSLs.

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.

How natural and anthropogenic factors should drive microplastic behavior and fate: The scenario of Brazilian urban freshwater.

Brazil maintains its position at the top of the global ranking of plastic producers, yet recycling efforts have been incipient. Recent data reveals an annual production of approximately 14 million tons of plastic waste, not accounting for the surge in the usage of plastic masks and related materials due to the COVID-19 pandemic. However, what remains largely unreported is that over half of post-consumer plastic packaging in Brazil is managed without any monitoring, and it remains unclear how this will contribute to the occurrence of plastic waste and microplastics in Brazilian freshwaters. This scenario requires the consideration of several other crucial factors. Studies have been carried out mainly in marine and estuarine waters, while data on freshwaters are lacking. Brazil has continental dimensions and the highest water availability on the planet, yet the demand for water is greatest in regions with medium to low supply. Many densely populated Brazilian urban areas face chronic flood problems, possess inadequate levels of wastewater treatment, and display inadequate solid waste management practices. Consequently, urban freshwater with tropical characteristics in Brazil presents an intriguing scenario and is complementary to the most commonly studied marine environments. In this study, we explore the nuances of pollution in Brazilian urban freshwater and discuss how various parameters, such as organic matter, suspended solids, temperature, and pH, among others, influence the behavior of microplastics and their interactions with organic and inorganic contaminants. Furthermore, we address how microplastic conditions, such as biofouling, the type of plastic, or degradation level, may impact their behavior. By analyzing how these conditions change, we propose priority themes for investigating the occurrence of microplastics in Brazilian urban freshwater systems under different degrees of human impact. Ultimately, this study aims to establish a network dedicated to standardized monitoring of microplastic pollution in Brazilian urban freshwaters.

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).

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.

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.

Evaluation of the Physical Properties of Bedding Materials for Dairy Cattle Using Fuzzy Clustering Analysis.

The bedding materials used in dairy cow housing systems are extremely important for animal welfare and performance. A wide range of materials can be used as bedding for dairy cattle, but their physical properties must be analysed to evaluate their potential. In the present study, the physical properties of various bedding materials for dairy cattle were investigated, and different fuzzy clustering algorithms were employed to cluster these materials based on their physical properties. A total of 51 different bedding materials from various places in Europe were collected and tested. Physical analyses were carried out for the following parameters: bulk density (BD), water holding capacity (WHC), air-filled porosity (AFP), global density (GD), container capacity (CC), total effective porosity (TEP), saturated humidity (SH), humidity (H), and average particle size (APS). These data were analysed by principal components analysis (PCA) to reduce the amount of data and, subsequently, by fuzzy clustering analysis. Three clustering algorithms were tested: k-means (KM), fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms. Furthermore, different numbers of clusters (2-8) were evaluated and subsequently compared using five validation indexes. The GK clustering algorithm with eight clusters fit better regarding the division of materials according to their properties. From this clustering analysis, it was possible to understand how the physical properties of the bedding materials may influence their behaviour. Among the materials that fit better as bedding materials for dairy cows, Posidonia oceanica (Cluster 6) can be considered an alternative material.