Foodwaste (hereinafter, FW) is the most voluminous solid waste and its amount is growing rapidly all over the world. The turning of FW into biogas via anaerobic fermentation is widely recognized as an environmentally responsible and economically reasonable option. Based on the knowledge obtained from agricultural biogas stations, the current methods of FW fermentation management are based on balancing the ratio of total carbon and nitrogen. However, it was repeatedly and independently reported that the stability of this process is low, resulting in many concessions in terms of prolonged hydraulic retention time or reduced biogas yield. Hence, biochemical as well as economic performance of the process is balanced by mixing of FW with agricultural residues. FW samples of various origin were collected and biochemically analyzed. The data indicate that FW originating from homes and luxury restaurants tends to be lignocellulose-based, whereas the levels of crude fiber (25% up to 27%) are higher than those from agricultural feedstock (18%). In contrast, FW from school canteens and inexpensive restaurants tends to be starch-based with high levels of amyloids (21% up to 23%) and fat (5% up to 7%). A novel method better reflecting the bioavailability of carbon and nitrogen to anaerobic consortia is proposed. It is demonstrated that the previous optimization methods could somehow reflect the availability of nutrients in agricultural feedstock, as carbonaceous and nitrogen sources are relatively equally biodegradable. Nevertheless, the biodegradability of FW is considerably different, which is why higher amounts of proteins and lipids lead to increased levels of ammonia and sulfide, resulting in an inhibitory effect on the metabolism of anaerobic consortia. Optimizing the anaerobic fermentation of FW by the new method outperforms the previous technique and makes it possible to process FW more intensively, or, more precisely, with higher profitability and lower proportion of ballast agricultural feedstock.
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.
Infrared thermography (IRT) is a noninvasive and safe method of displaying the temperature map of objects that can be used to detect hoof diseases and lameness to reduce significant financial costs and physically stress animals. A qualitative bibliometric method based on the analysis of publications by the authors themselves using sophisticated tools of scientific databases was applied in this work. This review presents the fundamentals of IRT as well as recent developments in IRT detection in dairy science, including preprocessing, segmentation, and classification of objects in IRT images. In addition, recent studies dealing with the detection of hoof diseases and lameness using IRT are reviewed. As a result of this study, select previous studies are confronted in terms of technical aspects of IRT measurements such as emissivity, distance, temperature range, and reflected air temperature. Subsequently, recommendations for future IRT measurements are discussed.
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