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The aim of this empirical field research was to show potential differences due to the precise application of herbicides in the quality and size of the carrot root yield and the amount of working liquid used compared to those for control samples. Empirical verification of the effectiveness of the developed technology, confirmed by statistical analyses of the obtained results, allows for a comparative assessment of this method with the conventional method of herbicide application used in this study. Due to the methodology used, it can be assumed that for carrots, the years were a random factor, and the experiments carried out were a series. It can be assumed that in the analysis of a series of experiments, for each of the examined features there are no differences between the groups and there is no interaction of the groups with the years. The yields of the groups weeded manually, sprayed in a conventional way, and weeded with precise spraying did not differ in a statistically significant way. In the precise application, a 20–30% lower herbicide consumption was observed, which has an impact on the protection of the environment and improves the economic effect of carrot production.

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 R2adj = 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. [ABSTRACT FROM AUTHOR]

Lakes can be restored by the aeration method with the use of wind driven pulverising aerators. The method allows for moderate oxygenation of hypolimnion waters and it may be part of an integrated surface waters restoration system. The paper attempts to use the author's method of maximum wind speeds to assess the volumetric flow of water through the aerator pulverisation mechanism. The study was conducted in 2018 in windy conditions of Lake Swarzędzkie. The introduction to the paper includes the characteristic of the lake and discusses the construction and operation of the wind driven pulverising aerator. Based on the maximum wind speed model, the theoretical capacity of the machine was calculated, which in the conditions of Lake Swarzędzkie was less than 111,500 m3 per year. Based on maximum wind speeds, the method of assessing the efficiency of the wind driven pulverising aerator is suitable for determining the volumetric flow rate of the pulverisation unit. This can significantly facilitate the planning of water reservoir restoration. [ABSTRACT FROM AUTHOR]

Knowledge of the use-to-failure periods of process equipment, including agricultural vehicles, is essential for the determination of their durability and reliability. Obtaining any empirical data on this issue is difficult and sometimes impossible. Experimental studies are costly and time-consuming. Manufacturers are usually reluctant to share such data, claiming that the information is classified for the sake of their companies. The purpose of this study was to compare empirical data with data generated using adequate statistical tools. The newly generated and very similar in value pseudorandom numbers were obtained by simulations using the Monte Carlo, Latin hypercube sampling and Iman-Conover methods. Reliability function graphs obtained from the generated time-series (use-to-failure periods) with matching Weibull distribution had very similar shape and scale parameters. They were are also comparable to parameters from experimental data extracted from a Polish Zetor agricultural tractor service station. The validation of the applied methods was limited as it was carried out only on the basis of the available data. Analysis of line graphs of cumulative deviations of the values of use-to-failure periods (times-to-fail) generated against empirical times-to-fail indicated that the best method in the studied case was the Monte Carlo method. [ABSTRACT FROM AUTHOR]

Copyright of Ecological Engineering / Inżynieria Ekologiczna is the property of Polish Society of Ecological Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ecological Engineering / Inżynieria Ekologiczna is the property of Polish Society of Ecological Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ecological Engineering / Inżynieria Ekologiczna is the property of Polish Society of Ecological Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ecological Engineering / Inżynieria Ekologiczna is the property of Polish Society of Ecological Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)