Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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).

Abstract 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.

Abstract 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.