This study is an elaboration on the conference article written by the same authors, which presented the results of laboratory tests on the biogas efficiency of the following substrates: maize silage (MS), pig manure (PM), potato waste (PW), and sugar beet pulp (SB). This article presents methane yields from the same substrates, but also on a technical scale. Apart from that, it presents an original methodology of defining the Biochemical Methane Potential Correction Coefficient (BMPCC) based on the calculation of biomass conversion on an industrial scale and on a laboratory scale. The BMPCC was introduced as a tool to enable uncomplicated verification of the operation of a biogas plant to increase its efficiency and prevent undesirable losses. The estimated BMPCC values showed that the volume of methane produced in the laboratory was overestimated in comparison to the amount of methane obtained under technical conditions. There were differences observed for each substrate. They ranged from 4.7% to 17.19% for MS, from 1.14% to 23.58% for PM, from 9.5% to 13.69% for PW, and from 9.06% to 14.31% for SB. The BMPCC enables estimation of biomass under fermentation on an industrial scale, as compared with laboratory conditions.

An important factor along with the availability of food is its quality. It depends, among other things, on the type of plant protection products used and the method of their application. This manuscript presents research on the possibility of using a shielded band sprayer in field onion cultivation. The shielded band spraying technology presented in this article is the subject of a patent application (application number P.428494-The prototype of the machine was produced in Poland in cooperation with the University of Life Sciences in Poznań). The research consisted in comparing the quantity and quality of the obtained crop, based on various methods of reducing the weed population. The research results indicate that the proposed shielded band spraying technology may affect the food quality (the active substance is not sprayed on onion plants) and profitability of farms (less use of plant protection products).

The purpose of the field experiments was to show possible differences in the quality and size of onion yield and the amount of working liquid used in the technology of the precise application of herbicides. The research material was an onion plantation during three growing seasons in 2015/2016, 2016/2017, and 2017/2018. Cultivation conditions were the same for all plots covered by the experiment. Onions were grown in the row-strip method with a spacing of 150 cm. The experimental factor was the method of weed control on the plots: A—without weeding (control); B—manual weeding; C—conventional herbicide application; D—precise herbicide application. Herbicides were used: Agil 100EC, Pendigan 330EC, Roundup 360SL, Galigan 240EC, Goal 480SC, Lontrel 300SL, Emblem 20WP, Fusilade Forte 150EC, Szogun 10EC, and Lentagran 45WP. The total onion yield did not differ statistically within the accepted confidence interval, regardless of the weeding method in the growing seasons studied. The developed technology allows a reduction in herbicide consumption in onion cultivation by 26% compared to conventional technology, which is of great importance in the aspect of introducing chemical substances into the environment and their accumulation in crops.

The experiment was designed in a bifactor model with five maize hybrids (Rasant, Dragon, LG 2252, San and Magister), mowed at two heights, i.e., 10–12 cm (A) and 50 cm (B). The average yield of dry matter of plants cut at a height of 50 cm (B) was 130.5 dt/ha and was significantly lower than...

Modelling and predicting agricultural production processes have high cognitive and practical values. Plant growth, the threat of pathogens and pests, and the structure of agricultural machinery treatments are mostly non-linear, measurable processes that can be described mathematically. In this paper, a multiple regression analysis was carried out in the first step to check the non-linearity of the data and yielded a coefficient of determination of R2 = 0.9741 and the coefficient of determination corrected for degrees of freedom was R2_adj = 0.9739. An artificial neural network model, called CH-NET, is then presented to predict the yield loss of carrot roots by leaving root mass in the field during harvest at the mechanical heading stage. The proposed network model has an architecture consisting of an input layer, three hidden layers with 12 neurons each, and an output layer with one neuron. Twelve input criteria were defined for the analysis and testing of the network, eight of which related to carrot root parameters and four to the heading machine. The training, testing, and validation database of the CH-NET network consisted of the results of field trials and tests of the operation of the patented (P.242097) root heading machine. The proposed CH-NET neural network model achieved global error (GE) values of 0.0931 t·ha⁻¹ for predicting carrot root yield losses for all twelve criteria adopted. However, when the number of criteria is reduced to eight, the error increased to 0.0991 t·ha⁻¹. That is, the prediction was realised with an accuracy of 90.69%. The developed CH-NET model allows the prediction of economic losses associated with root mass left in the field or contamination of the raw material with undercut leaves. The simulations carried out showed that minimum root losses (0.263 t·ha⁻¹) occur at an average root head projection height of 38 mm and a heading height of 20 mm from the ridge surface.

This study aims to determine and evaluate the operating parameters of three modern self-propelled forage harvesters during maize silage harvest. The machines were equipped with operator assistance systems. Field tests were conducted for three self-propelled forage harvesters: Claas Jaguar 870, Claas Jaguar 950, KroneBiG X 650. The tests were conducted in large-scale farms located in Wielkopolskie and Pomorskie voivodeships. Maize was harvested at the beginning of the full-grain maturity stage. A complete time study covering four control shifts in accordance with BN-76/9195-01 was performed to determine operating ratios and indicators. Fuel consumption was determined using the full tank method. The Claas Jaguar 950 forage harvester had the highest effective mass performance: 141.3 Mg·h ^-1 . The same machine also achieved the lowest fuel consumption per tonne of fresh matter (FM) harvested: 0.51 kg·Mg ^-1 . Labour expenditure for the self-propelled forage harvesters tested during the total time of change ranged from 0.38 to 0.62 labour hour per hectare. The tested machines also had very high technical and technological reliability.