Although cow manure is a valuable natural fertilizer, it is also a source of extreme greenhouse gas emissions, mainly methane. For this reason, this study aims to determine the impact of investments in a biogas plant on the energy and economic aspects of the operation of a dairy farm. A farm with a breeding size of 600 livestock units (LSU) was adopted for the analysis. In order to reach the paper’s aim, the analysis of two different scenarios of dairy farm functioning (conventional–only milk production, and modern–with biogas plant exploitation) was conducted. The analysis showed that the investment in biogas plant operations at a dairy farm and in using cow manure as one of the main substrates is a more profitable scenario compared to traditional dairy farming. Taking into account the actual Polish subsidies for electricity produced by small biogas plants, the scenario with a functioning biogas plant with a capacity of 500 kW brings €332,000/a more profit compared to the conventional scenario, even when taking into account additional costs, including the purchase of straw to ensure a continuous operation of the installation. Besides, in the traditional scenario, building a biogas plant allows for an almost complete reduction of greenhouse gas emissions during manure storage.

Animal biomass is an important substrate in the anaerobic digestion process. The implementation of a waste technology for energy production, such as the production of biogas from animal waste, has been recognized in many countries as one of the best ways to achieve the Sustainable Energy Development Goals. Without a systematic review of resources and accurate estimation of available sources in terms of the amount of potential electricity, it is impossible to manage biomass rationally. The main aim of the article was to present a new tool for assessing the biomass of animal origin and estimating its potential energy through a computer database, which will be widely available in the end of 2020 to show results from the calculation using the database. This tool is configured to enter the data on the developed and undeveloped biomass resources in production of farm animals in rural areas in Poland. Calculations from the database show the biogas potential of swine and cattle manure and slurry in Poland, which is approximately 5.04 billion m 3 , with a 60% share of methane in biogas. It is the value of approximately 3.03 billion m 3 of methane. It is worth underlining that slurry and manure are not high-energy substrates; therefore, it is necessary to introduce more energetic substrate streams to improve the biogas plant efficiency.

Modelling plays an important role in identifying and solving problems that arise in a number of scientific issues including agriculture. Research in the natural environment is often costly, labour demanding, and, in some cases, impossible to carry out. Hence, there is a need to create and use specific “substitutes” for originals, known in a broad sense as models. Owing to the dynamic development of computer techniques, simulation models, in the form of information technology (IT) systems that support cognitive processes (of various types), are acquiring significant importance. Models primarily serve to provide a better understanding of studied empirical systems, and for efficient design of new systems as well as their rapid (and also inexpensive) improvement. Empirical mathematical models that are based on artificial neural networks and mathematical statistical methods have many similarities. In practice, scientific methodologies all use different terminology, which is mainly due to historical factors. Unfortunately, this distorts an overview of their mutual correlations, and therefore, fundamentally hinders an adequate comparative analysis of the methods. Using neural modelling terminology, statisticians are primarily concerned with the process of generalisation that involves analysing previously acquired noisy empirical data. Indeed, the objects of analyses, whether statistical or neural, are generally the results of experiments that, by their nature, are subject to various types of errors, including measurement errors. In this overview, we identify and highlight areas of correlation and interfacing between several selected neural network models and relevant, commonly used statistical methods that are frequently applied in agriculture. Examples are provided on the assessment of the quality of plant and animal production, pest risks, and the quality of agricultural environments.

The paper covers the problem of determination of defects and contamination in malting barley grains. The analysis of the problem indicated that although several attempts have been made, there are still no effective methods of identification of the quality of barley grains, such as the use of information technology, including intelligent sensors (currently, quality assessment of grain is performed manually). The aim of the study was the construction of a reduced set of the most important graphic descriptors from machine-collected digital images, important in the process of neural evaluation of the quality of BOJOS variety malting barley. Grains were sorted into three size fractions and seed images were collected. As a large number of graphic descriptors implied difficulties in the development and operation of neural classifiers, a PCA (Principal Component Analysis) statistical method of reducing empirical data contained in the analyzed set was applied. The grain quality expressed by an optimal set of transformed descriptors was modelled using artificial neural networks (ANN). The input layer consisted of eight neurons with a linear Postsynaptic Function (PSP) and a linear activation function. The one hidden layer was composed of sigmoid neurons having a linear PSP function and a logistic activation function. One sigmoid neuron was the output of the network. The results obtained show that neural identification of digital images with application of Principal Component Analysis (PCA) combined with neural classification is an effective tool supporting the process of rapid and reliable quality assessment of BOJOS malting barley grains.

The integrated production of bioethanol and biogas makes it possible to optimise the production of carriers from renewable raw materials. The installation analysed in this experimental paper was a hybrid system, in which waste from the production of bioethanol was used in a biogas plant with a capacity of 1 MWe. The main objective of this study was to determine the energy potential of biomass used for the production of bioethanol and biogas. Based on the results obtained, the conversion rate of the biomass—maize, in this case—into bioethanol was determined as the efficiency of the process of bioethanol production. A biomass conversion study was conducted for 12 months, during which both maize grains and stillage were sampled once per quarter (QU-I, QU-II, QU-III, QU-IV; QU—quarter) for testing. Between 342 L (QU-II) and 370 L (QU-I) of ethanol was obtained from the organic matter subjected to alcoholic fermentation. The mass that did not undergo conversion to bioethanol ranged from 269.04 kg to 309.50 kg, which represented 32.07% to 36.95% of the organic matter that was subjected to the process of bioethanol production. On that basis, it was concluded that only two-thirds of the organic matter was converted into bioethanol. The remaining part—post-production waste in the form of stillage—became a valuable raw material for the production of biogas, containing one-third of the biodegradable fraction. Under laboratory conditions, between 30.5 m3 (QU-I) and 35.6 m3 (QU-II) of biogas per 1 Mg of FM (FM—fresh matter) was obtained, while under operating conditions, between 29.2 m3 (QU-I) and 33.2 m3 (QU-II) of biogas was acquired from 1 Mg of FM. The Biochemical Methane Potential Correction Coefficient (BMPCC), which was calculated based on the authors’ formula, ranged from 3.2% to 7.4% in the analysed biogas installation.

Composting is the natural, exothermic process where the huge amount of heat that is created is an issue of organic matter decomposition. However, too high temperature can reduce the microbial activity during the thermophilic composting phase. The aim of this study was to analyze the effect of heat excess removal from composted materials on the process dynamic. The experiment was performed in two parallel bioreactors. One of them was equipped with a heat removal system from the bed of the composted material. Three experiments were carried out with mixtures of different proportions: biological waste, wheat straw, and spent coffee grounds. The content of each option was determined based on a previous study of substrates to maintain the C/N ratio for the right composting process, provide adequate porosity composted material, and enable a proper degree of aeration. The study showed the possibility of receiving part of the heat from the bed of composted material during the thermophilic phase of the process without harm both to the course of composting and the quality of the final product. This shows that at a real scale, it can be possible to recover an important amount of heat from composted materials as a low-temperature heat source.

In the presented study, data regarding the size and structure of cattle herds in voivodeships in Poland in 2019 were analysed and modelled using artificial neural networks (ANNs). The neural modelling approach was employed to identify the relationship between herd structure, biogas production from manure and slurry, and the geographical location of herds by voivodeship. The voivodeships were categorised into four groups based on their location within Poland: central, southern, eastern, and western. In each of the analysed groups, a three-layer MLP (multilayer perceptron) with a single hidden layer was found to be the optimal network structure. A sensitivity analysis of the generated models for herd structure and location within the eastern group of voivodeships revealed significant contributions from dairy cows, heifers (both 6–12 and 12–18 months old), calves, and bulls aged 12–24 months. For the western voivodeships, the analysis indicated that only dairy cows and herd location made significant contributions. The optimal models exhibited similar values of RMS errors for the training, testing, and validation datasets. The model characterising biogas production from manure in southern voivodeships demonstrated the smallest RMS error, while the model for biogas from manure in the eastern region, as well as the model for slurry in central parts of Poland, yielded the highest RMS errors. The generated ANN models exhibited a high level of accuracy, with a fitting quality of approximately 99% for correctly predicting values. Comparable results were obtained for both manure and slurry in terms of biogas production across all location groups.

In the presented study, data on the size and structure of cattle herds in Wielkopolskie, Podlaskie, and Mazowieckie voivodeships in 2019 were analyzed and subjected to modelling with the use of artificial intelligence, namely artificial neural networks (ANNs). The potential amount of biogas (m3) from cattle manure and slurry for the analyzed provinces was as follows: for the Mazowieckie Voivodeship, 800,654,186 m3; for the Podlaskie voivodeship, 662,655,274 m3; and for the Wielkopolskie voivodeship, 657,571,373 m3. Neural modelling was applied to find the relationship between the structure of the herds and the amount of generated slurry and manure (biomethane potential), as well as to indicate the most important animal types participating in biogas production. In each of the analyzed cases, the three-layer MLP perceptron with a single hidden layer proved to be the most optimal network structure. Sensitivity analysis of the generated models concerning herd structure showed a significant contribution of dairy cows to the methanogenic potential for both slurry and manure. The amount of slurry produced in the Mazowieckie and Wielkopolskie voivodeships was influenced in turn by heifers (both 6–12 and 12–18 months old) and bulls 12–24 months old, and in the Podlaskie voivodeship by calves and heifers 6–12 months old. As for manure, in addition to cows, bulls 12–24 months old and heifers 12–18 represented the main factor for Mazowieckie and Wielkopolskie voivodeships, and heifers (both 6–12 and 12–18 months old) for Podlaskie voivodeship.