When reducing ammonia emissions from cowsheds, it is recommended to reduce the ventilation intensity, air temperature in the barn, manure moisture by using bedding and manure-contaminated surfaces, and to prevent urine from accumulating in the airways. Using the mass flow method in the wind tunnel, after research on seven types of cattle manure with different moisture contents, it was found that ammonia evaporates up to 3.9 times more intensively from liquid manure than from solid manure. There is a strong correlation between ammonia and water evaporation from manure. Ammonia emission from liquid manure decrease by 2.0–2.3 times, emissions from solid manure decrease by 1.9–2.1 times. Different cowsheds have different opportunities to reduce air pollution and conditions for manure to dry and crusts to form on the surface. The best results will be achieved by applying complex measures to reduce air pollution.

Increasing control of localized air pollution caused by ammonia is identified, including limiting the maximum emissions from agriculture. In EU countries, the agricultural sector is the source of above 94% of the total anthropogenic emissions of ammonia, of which manure removal systems account for 56%. In view of the reason for the agricultural waste management by formation and propagation of ammonia gas—the bacterial and enzymatic degradation of organic components in excrement—it is important to evaluate the effect of biotreatment of 100% natural composition (contain Azospirillum sp. (N) (number of bacterial colonies −1 × 109 cm⁻³), Frateuria aurentia (K) (number of bacterial colonies −1 × 109 cm⁻³), Bacillus megaterium (P) (bacterial colony count −1 × 109 cm⁻³), seaweed extract (10% by volume), phytohormones, auxins, cytokinin, gibberellins, amino acids, and vitamins) on the emission of ammonia from organic waste. Experimental research was carried out to determine significant differences of dynamics in agrochemical composition of manure, NH₃ gas emissions, depending on biotreatment, manure storage duration, and ventilation intensity of the barn. Gas emission was obtained via laser gas analyzer using a spectroscopic method in a specially reconstructed wind tunnel chamber. About 32% manure biotreatment effect on reduction of ammonia emissions was established. The maximum effect of the biodegradable compound on gaseous propagation was assessed after 28–35 days of manure storage and proved all biotreatment effect time of 49–56 days. By the saving nitrogen loses priority, manure biotreatment could reduce nitrogen losses from manure and inorganic N fertilizers by approximately 5%, also could reduce approximately 5911.1 thousand tones nitrogen fertilizer in the world and reduce approximately 5.5 Eur ha⁻¹. “The biotreatment impact assessment confirmed that proper application of biotreatment can reduce ammonia emissions from manure and environmental pollution from agriculture”.

Anaerobic digestion of poultry manure is a potentially-sustainable means of stabilizing this waste while generating biogas. However, technical, and environmental protection challenges remain, including high concentrations of ammonia, low C/N ratios, limited digestibility of bedding, and questions about transformation of nutrients during digestion. This study evaluated the effect of primary biological treatment of poultry manure on the biogas production process and reduction of ammonia emissions. Biogas yield from organic matter content in the aerobic pretreatment groups was 13.96% higher than that of the control group. Biogas production analysis showed that aerobic pretreatment of poultry manure has a positive effect on biogas composition; methane concentration increases by 6.94–7.97% after pretreatment. In comparison with the control group, NH₃ emissions after aerobic pretreatment decreased from 3.37% (aerobic pretreatment without biological additives) to 33.89% (aerobic pretreatment with biological additives), depending on treatment method.

One of the problematic sectors according to GHG (greenhouse gas) and ammonia (NH₃) emission quantities is agriculture. Without endangering food production (and intensifying), GHG emissions come from all sources in animal husbandry. The aim of this study was to comprehensively reduce GHG emissions by applying a holistic process management model to one of the most popular cowsheds in Lithuania (260-seat boxing cowshed, cows are milked on site, computerized management of technological processes, productivity of 8600 kg of milk, barn system, and liquid manure). Considering the cow keeping technology applied on the farm, the equipment used, and the feed production and ration system, a model for the management of technological parameters of production processes was prepared for the farm. This model balanced trade-offs among animal welfare, cow productivity, production costs, and GHG and NH₃ emissions. The aim of the research was the adaptation of the integrated model to fully control, manage, and optimize milk production processes through bio- and engineering innovations to implement climate-friendly feed production and feeding and feed rationing systems, to improve animal housing and working conditions, and to reduce GHG and NH₃ emissions without increasing production costs. The environmental impact assessment was performed with SimaPro 9.1 process modeling software. Data from milk production, biomass cultivation, and feed preparation, transportation, and equipment were used from the Ecoinvent v3 database. Based on the LML-I calculation methodology, the effect of processes was determined. To quantify the potential emissions in the dairy farm, the emission factors were estimated using a life cycle assessment method per functional unit—1000 kg—of standardized milk. Grass silage, maize silage, and feed concentrate were found to account for the largest share of gas emissions—26.09% (107.39 kg CO₂ eq. FU⁻¹), 22.70% (93.44 kg CO₂ eq. FU⁻¹), and 21.85% (89.92 kg CO₂ eq. FU⁻¹) of the total CO₂ emissions during the process, respectively. Considering the critical points of the classic SC scenario, the cultivation technology was adjusted, where 50% of N fertilizers were replaced by bioproducts (biological preparations). Both scenarios—classic SC (control variant) and Bio SC (variants using bioproducts)—were evaluated for comparison. The use of biopreparations in the categories reduced the environmental impact from 0.1% to 45.7% in dairy production technology grass silage, barley grain, hay production, and corn silage stocks. The carbon footprint of the sustainable bio-based milk production (0.393 kg CO₂ eq. kg⁻¹ FPCM (fat- and protein-adjusted milk)) was lower by 4.6% compared to the average Lithuanian classic dairy farm (0.412 kg CO₂ eq. kg⁻¹ FPCM). Based on this methodology, it is possible to assess many dairy farms and address critical points in an integrated way, which can help to improve the quality of dairy production and the environment.

The research is aimed at creating a methodology for increasing the positioning accuracy of an industrial robot and minimizing the vibration of the robot gripper by applying machine learning based on the developed mathematical model for estimating the positioning error. Two components of positioning accuracy are considered: geometric and kinematic errors and elastic static deformations. The dynamic error in the partial system of motion of the robot manipulator links is analyzed. The equation of partial motions is obtained from Lagrange’s differential equation of motion of the II kind. The system of differential equations for the positioning error was solved analytically by Euler’s method. An example of modeling the position and orientation error of the gripper due to temperature deformations of the third link for the manipulator scheme is given. An example of the modeling of static deformations and errors of the manipulator with elastic pliability of the robot links is given. An example of dynamic error modeling in a partial system of motion of the robot links is given. The proposed method of modeling robot gripper positioning errors makes it possible to increase the positioning accuracy of the industrial robot and minimize the vibration of the gripper. Having a mathematical model of positioning errors, it is possible to compensate for the positioning error by changing the speed of movement of the gripper reference point before determining the direct kinematic task.