The aim of the study was to investigate the effect of using various by-products (orange and apple pomace, tomato peel, pepper peel, prickly pear peel, and prickly pear seed peel) on the dough rheology and properties of gluten-free bread. The by-products were incorporated into a gluten-free bread formulation based on corn and chickpea flours (2/1 w/w). Different levels of each by-product (0, 2.5, 5, and 7.5% in the basic replacement) were tested. Wheat bread and gluten-free bread without the addition of by-products were used as controls. The results indicated that the by-products increased the maximum dough height, the total CO₂ production, and CO₂ retention coefficient compared to unenriched gluten-free dough. The highest K-value consistency coefficient was observed for the dough enriched with the prickly pear peel. The addition of by-products significantly improved (p < 0.0001) the specific volume of gluten-free bread, with values increasing from 1.48 to 2.50 cm³/g. The hierarchical cluster analysis and the constellation plot showed four groups: the wheat bread group, the second group containing the gluten-free control bread, the group with bread enriched by pomace, and the group with bread enriched with peels, exhibit the same effect on gluten-free bread and the peels exhibit the same effect on gluten-free bread.

Sweet corn remains at its optimum harvest maturity for a very short period, and quality changes rapidly following its peak. This study investigates the changes in carbohydrate concentration of three genotypes of sweet corn: sugary (su-1), sugar enhanced (se) and supersweet or shrunken (sh2) at three stages of maturity. The analysis of variance revealed differences between genotypes for carbohydrates (sugars and starch) and between stages of maturity (moisture content). Average sugar concentration of su-1 corn was 5% higher than that of se corn and 52% higher than that of sh2 corn. The average starch concentration of su-1 corn was 27% lower than that of se corn and 66% lower than that of sh2 corn. The average moisture content of su-1 corn was 3% lower than that of se corn and 1% lower than that of sh2 corn

The aim of the study was to determine the impact of red light (RL) and effective microorganisms (EMs) on the wheat yield of grain and straw, as well as the quality (protein, carbohydrates, gluten, index of sedimentation (SDS index), germination capacity). Moreover, the experiments measured the granulometric composition and pH of soil, as well as its content of microelements and major nutrients, including heavy metals. The field experiment was conducted in 2017, 2018, and 2019 at the experimental station (Felin) of the University of Life Sciences in Lublin (Poland). The best results in terms of overall yield, protein content, and SDS index were obtained after the red light treatment (RL). The three-year application of effective microorganisms (EMs) in the soil had a positive impact, relative to the control, on the grain yield, straw yield, starch, SDS index, and germination capacity. A slight decrease was observed in terms of the protein content. After the application of effective microorganisms (EMs) in soil, changes were observed in the soil’s granulometric composition, pH, humus, and microelements.

The objective of this work is to determine the influence of blanching time on moisture content, sugars, protein, and processing recovery (degree of cut corn) of sweet corn kernels. Sweetcorn cobs of the Jubilee variety were blanched in the water at the temperature 85 °C during the period of 2, 4, 6, and 8 min. Non-blanched cobs were used as control samples. Blanched cobs were made subject to a mechanical process of cutting-off the kernels. It was stated that the blanching time has significant statistical influence on the content of moisture, sugars, and protein in kernel as well as on the quantity of the cut-off kernel mass.

Statistics show that the travel of agricultural machinery to a work area and their movement during labour is the source of many serious accidents. The most dangerous in consequences prove to be those that occur during transport and associated with maneuvering tractors and machinery (about 30% of all fatal accidents). It can be assumed that at least some of these accidents were caused indirectly by the specific design features of agricultural machines which adversely affect the driveability. The single- and multi-loop structures of the driver-vehicle system models are formulated to study the contributions of various preview and prediction strategies to the path tracking and dynamic performance of the articulated vehicle. In the presented study the compensatory model of driver utilizes the lateral acceleration of the tractor, roll angle of trailer sprung mass and the articulation rate as the internal motion feedback variables. The control model of steering of an agricultural set has been implemented in the Matlab/Simulink environment. The model has been constructed with the use of stochastic methods and operational transmittances describing the various components of the system. The model operational transmittances has been estimated using Box-Jenkins and continuous-time process models from input-output data. The model has been tested using experimental data from road investigation of the agricultural set.

A relatively small number of large industrial enterprises and major exporting companies has a dominant position in the market and determines the corresponding demand for transport services. Logistics activity in the country is one of the growth points of the economic system. One of the ways for increasing the efficiency of logistics activities in agriculture and food transport management sectors in Belarus is the creation of logistics clusters. The article proposes a methodological approach to the formation of logistics cluster in the region. The approach is based on three phases of work: identification of a cluster, evaluation logistics rents and logistic potential of the region, where the cluster is located. This allows assessing the expedience of cluster formation and its subsequent development. This process is connected with agriculture and food processing activity.