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This paper considers the evolution of processes applied in agriculture for field operations developed from non-organized handmade activities into very specialized and organized production processes. A set of new approaches based on the application of metaheuristic optimization methods and smart automatization known as Agriculture 4.0 has enabled a rapid increase in in-field operations’ productivity and offered unprecedented economic benefits. The aim of this paper is to review modern approaches to agriculture machinery movement optimization with applications in sugarcane production. Approaches based on algorithms for the division of spatial configuration, route planning or path planning, as well as approaches using cost parameters, e.g., energy, fuel and time consumption, are presented. The combination of algorithmic and economic methodologies including evaluation of the savings and investments and their cost/benefit relation is discussed.

Determination of ammonia (NH3) emissions for intensive livestock facilities (pork, poultry) is important from both a regulatory and a research point of view. Buildings housing livestock are a large source of ammonia emissions from the agriculture sector. However, measurements to determine emissions can be time-consuming and costly. Therefore, it is essential to find a suitable methodology for monitoring NH3. The methodology for determining NH3 emissions is legislatively unified in terms of sampling methodology, including sampling time (24 h), sampling points (input/output), number of sampling days, and their distribution during the year, and to determine only a general calculation of the annual average NH3 emissions. For this reason, the researchers chose different approaches for the calculation of NH3 emissions, and these approaches are not unified. Based on accurate monitoring and created models, the authors proposed a methodology for calculation of NH3 emissions, which divides the 24 h measurement into time windows (30 min), from which the arithmetic mean and standard deviation are determined, and the total emissions for one year is determined. The chosen time windows for the partial calculation are important from the point of view of reflecting the microclimatic conditions inside the stable and the device limits for sampling the NH3 concentration and airflow.

Abstract. The aim of this study was to prove the hypothesis that the noise emissions from pig housing varies according to the time of day and the season. The measurements were performed in a building for 1150 fattening pigs with a slatted floor during summer and winter. The pigs (average weight 95 kg) were kept in pens under a batch management system. Nine places were the focus of sound intensity measurements (one inside the stable in section 7; eight places outside the building). The measurements were performed during three sets of 5 consecutive days in summer and three sets in winter. On each day the data were obtained during three 30 min periods (before feeding, during feeding and after feeding). The measurement was made inside and outside the building at the same time. The level of noise depends very significantly upon the period of measurement (before feeding, during feeding, after feeding). The following values were recorded inside (place 1): 65.5 ± 1.6 dB before feeding, 72.0 ± 1.4 dB during feeding and 63.4 ± 0.7 dB after feeding (P <0.001). The effect of seasonal noise levels can be seen only in outside measurements (P <0.05; P <0.01). The comparison of measurement place 1 (inside, pen with pigs) with the other places outdoors showed significant differences in both observed factors (P <0.001). We can conclude that the noise in the pig housing depends significantly on the time of day. The season influences the noise outside the building, in particular.