The objectives of this study were to determine: 1) ammonia emission rates from stored scraped and flushed manure from dairy cows fed either normal or low N diet; and 2) seasonal effects on ammonia emission rates from stored scraped and flushed dairy manure. Four pilot-scale tanks were used for manure storage with different treatments - scraped manure for normal diet (NS), flushed manure for normal diet (NF), scraped manure for low N diet (LS), and flushed manure for low N diet (LF). The first part of the study lasted for 1 month and four treatments were all investigated; the second part of the study lasted for 12 months and two tanks with treatments NS and NF were investigated. Dynamic flux chambers and a photoacoustic gas analyzer were used to measure ammonia emission rates. There was no significant change of the N content of manure as the dietary N content is reduced (from 17.8% to 15.9% crude protein). However, ammonia emission rates from manure storage tanks were reduced by 33% (from 27.4 ± 38.1 to 18.4 ± 21.9 mg m⁻²h⁻¹; P<0.0001 based on paired t-test). Flushing manure reduced emission rates by 72% compared to scraping manure (from 35.6 ± 39.6 to 10.1 ± 8.2 mg m⁻²h⁻¹; P<0.0001 based on paired t-test). Ammonia emission rates for NS, NF, LS and LF were 43.9 ± 48.0, 10.9 ± 8.7, 27.4 ± 27.3, and 9.3 ± 7.8 mg m-2 h-1, respectively. The chamber headspace temperature for NS, NF, LS and LF were 26.0 ± 6.9, 25.8 ± 6.8, 26.6 ± 6.5, and 27.2 ± 6.7 °C, respectively. The manure pH for NS, NF, LS, and LF were 6.3 ± 0.1, 6.4 ± 0.3, 6.4 ± 0.1, and 6.1 ± 0.1, respectively. Both dietary N reduction and manure flushing are recommended to reduce ammonia emission rates from dairy manure storage tanks. Ammonia emission rates were higher in summer and fall, due to higher air temperature and higher manure pH. The pH of scraped manure was 7.2 ± 0.6, 6.7 ± 0.2, 6.5 ± 0.3 and 7.0 ± 0.3 for fall, winter, spring and summer, respectively. The pH of flushed manure was 6.8 ± 0.4, 6.7 ± 0.4, 6.4 ± 0.3 and 6.8 ± 0.4 for fall, winter, spring and summer, respectively. Ammonia emission rates from scraped manure for fall, winter, spring, and summer were 7.4 ± 8.6, -0.5 ± 1.2, 1.1 ± 1.9, and 5.8 ± 2.7 mg m⁻²h⁻¹, respectively. Ammonia emission rates from flushed manure for fall, winter, spring, and summer were 3.9 ± 4.2, -0.5 ± 0.9, 0.8 ± 1.4, and 4.4 ± 1.2 mg m⁻²h⁻¹, respectively. Seasonal changes of air temperature and manure pH were key factors affecting ammonia emissions from manure storage in this study. Seasonal climate conditions including precipitations (rainstorms and snows) and icing can cause reduction of ammonia emissions from manure storage in open air. More attention should be paid to reduce ammonia emissions in warmer seasons, e.g., by covering the storage facilities. / Master of Science

Broilers are raised indoors on high density farms with bedding/litter to trap their manure. Ammonia gas, which is produced as the manure decomposes, has adverse effects on human health, bird welfare and the environment. Using litter amendments can reduce the amount and, consequently, the effects of ammonia emitted from broiler houses. The objective of this study was to determine the effectiveness of a biodegradable litter amendment (BLA) in reducing ammonia emitted from a broiler house. A pilot scale test was set up with six adjacent, individually ventilated rooms and a stocking density of 0.07 m² per bird. The birds were fed with a standard commercial, corn and soybean meal based diet and water was provided ad libitum. The first flock was grown on 10 cm of fresh, kiln-dried pine shavings, while subsequent flocks were grown on top-dressed reused litter. The two treatments (control (CTL) and BLA) were randomly assigned to the six rooms after flock 1, to give three replicates per treatment. The exhaust air from the rooms was sampled for ammonia concentration for two days each week starting at four days of age to determine the amount of ammonia emitted. Over three subsequent flocks, the total mass of ammonia emitted from rooms treated with BLA was 31% to 47% lower than the control. Ammonia emitted per bird grown on treated litter and per kg of harvested bird weight was 32% to 44% lower, and the exhaust fans ran 7% to 22% less than CTL over the same period. For both BLA and CTL, the amount of ammonia emitted generally increased with bird age and litter reuse. The study showed that BLA effectively reduced ammonia emitted from a broiler house and that there are potential energy savings from using the amendment. However, ammonia emitted from the BLA rooms during the final flock was 57% higher than CTL, which was attributed to insufficient water (less than 18% moisture by weight) to support the reaction between BLA and ammonia. / Ph. D.

The aim of the paper: to investigate the effect of microclimatic factors on the process of ammonia emission from manure, and evaluate possibilities to reduce ammonia emission from cowsheds by controlling these factors. The completed analysis of microclimatic factors in various naturally ventilated cowsheds revealed patterns of variation in ammonia concentration depending on air temperature in the barn. While analysing the process of ammonia evaporation from the manure, the effect of interacting environmental factors on the intensity of evaporation was evaluated. The effect of temperature, ventilation intensity and drying of manure surface on the intensity of ammonia evaporation process from manure was determined and proved. Theoretical and experimental presumptions were made for the investigation of the effect of the crust formation at the manure surface on ammonia diffusion process. Based on the obtained results, ammonia emission from naturally ventilated cowsheds with various engineering solutions can be predicted, and equipment reducing the ammonia emission from them can be installed. / Darbo tikslas ˗ Ištirti mikroklimato veiksnių įtaką amoniako emisijos procesui iš mėšlo ir įvertinti galimybes juos valdant sumažinti amoniako emisiją iš karvidžių. Ištyrus mikroklimato veiksnius įvairiose natūraliai vėdinamose karvidėse, nustatyti amoniako koncentracijos kaitos dėsningumai priklausomai nuo oro temperatūros tvarte. Analizuojant amoniako garavimo iš mėšlo procesą, įvertinta tarpusavyje sąveikaujančių aplinkos veiksnių komplekso įtaka garavimo intensyvumui. Nustatyta ir įrodyta temperatūros, vėdinimo intensyvumo ir mėšlo paviršiaus džiūvimo įtaka amoniako garavimo iš mėšlo proceso intensyvumui. Sukurtos teorinės ir eksperimentinės prielaidos tirti mėšlo paviršiuje besiformuojančios plutos įtaką amoniako difuzijos procesui. Pagal gautus rezultatus galima prognozuoti amoniako emisiją iš natūraliai vėdinamų karvidžių su įvairiais inžineriniais sprendimais ir diegti priemones mažinančias amoniako emisiją iš jų.

Storing manure on dairy farms is essential for maximizing its fertilizer value, reducing management costs, and minimizing potential environmental pollution challenges. However, ammonia loss through volatilization during storage remains a challenge. Quantifying these losses is necessary to inform decision-making processes to improve manure management, and design ammonia mitigation strategies. In 2003, the National Research Council recommended using process-based models to estimate emissions of pollutants, such as ammonia, from animal feeding operations. While much progress has been made to meet this call, still, their accuracy is limited because of the inadequate values of manure properties such as heat and mass transfer coefficients. Additionally, the process-based models lack realistic estimations for manure temperatures; they use ambient air temperature surrogates which was found to underestimate the atmospheric emissions during storage. This study uses machine learning algorithms' unique abilities to address some of the challenges of process-based modeling. Firstly, ammonia concentrations, manure temperature, and local meteorological factors were measured from three dairy farms with different manure management practices and storage types. This data was used to estimate the influence of manure characteristics and meteorological factors on the trend of ammonia emissions. Secondly, the data was subjected to four data-driven machine learning algorithms and a physics-informed neural network (PINN) to predict manure temperature. Finally, a deep-learning approach that combines process-based modeling and recurrent neural networks (LSTM) was introduced to estimate ammonia loss from dairy manure during storage. This method involves inverse problem-solving to estimate the heat and mass transfer coefficients for ammonia transport and emission from stored manure using the hyperparameters optimization tool, Optuna. Results show that ammonia flux patterns mirrored manure temperature closely compared to ambient air temperature, with wind speed and crust thickness significantly influencing ammonia emissions. The data-driven machine learning models used to estimate the ammonia emissions had a high predictive ability; however, their generalization accuracy was poor. However, the PINN model had superior generalization accuracy with R2 during the testing phase exceeded 0.70, in contrast to -0.03 and 0.66 for finite-elements heat transfer and data-driven neural network, respectively. In addition, optimizing the process-based model parameters has significantly improved performance. Finally, Physics-informed LSTM has the potential to replace conventional process-based models due to its computational efficiency and does not require extensive data collection. The outcomes of this study contribute to precision agriculture, specifically designing suitable on-farm strategies to minimize nutrient loss and greenhouse gas emissions during the manure storage periods. / Doctor of Philosophy / Dairy farming is critical for meeting the global demand for animal protein products; however, it generates a lot of manure that must be appropriately managed. Manure can only be applied to crop or pasture lands during growing seasons. Typically, manure is stored on farms until time permits for land application. During storage, microbial processes occur in the manure, releasing gases such as ammonia. Ammonia emitted contributes to the degradation of ambient air quality, human and animal health problems, biodiversity loss, and soil health deterioration. Furthermore, releasing ammonia from stored manure reduces the nitrogen fertilizer value of stored manure. Implementing control measures to mitigate ammonia emission is necessary to reduce nitrogen loss from stored manure. Deciding and applying appropriate control measures require knowledge of the rate of ammonia emission and when it occurs. Process-based models are a less expensive and more reliable method for estimating ammonia emissions from stored liquid dairy manure. Process-based model is a mathematical model that simulates processes related to ammonia production and emission from stored manure. However, process-based models have limitations because they require estimates of manure properties, which vary depending on the manure management. Additionally, these models use air temperature instead of manure temperature, underestimating the ammonia lost during storage. Therefore, this study used machine learning algorithms to develop more accurate models for predicting manure temperature and estimating ammonia emissions. First, we collected manure temperature, ammonia emissions, and weather data from three dairy farms with different manure management practices and storage structures. We used it to estimate the factors that affect ammonia emissions. The data was then used to develop four machine-learning models and one integrated machine-learning-based to assess their ability to predict manure temperature. Finally, a different machine learning approach that combines process-based modeling and neural networks was used to directly estimate ammonia loss from dairy manure during storage. The results show that manure temperature is closely related to the amount of ammonia lost, and factors like wind speed and crust thickness also influence the amount of ammonia lost. Machine learning algorithms offer a more accurate way to predict manure temperature than traditional methods. Finally, combining machine learning and process-based modeling improved the ammonia emission estimates. This study contributes to precision agriculture by designing suitable on-farm strategies to minimize nutrient loss during manure storage periods. It provides valuable information for dairy farmers and policymakers on managing manure storage more effectively and sustainably.

This is a study on the conditions of collaborative learning in the context of UK ammonia emissions. By conducting an extensive review of over 40 scientific articles, this study identifies and synthesizes a list of nine conditions deemed necessary for successful collaborative learning processes and explores their extent and overall influence between three stakeholders involved in UK ammonia emissions. Hybrid focus group/key informant interviews provided the data for this exploration. This study suggests that the extent of these 9 conditions are present enough between the three stakeholders to initiate a collaborative learning process. By conducting further studies with a wider field of stakeholders, a collaborative learning process could identify possible ways to mitigate UK ammonia emissions.

This thesis links papers reporting field measurements, modelling studies and reviews of greenhouse gas (GHG) emissions and their abatement from agriculture, in particular from livestock production. The aims of the work were to: quantify GHG emissions from litter-based farmyard manures; evaluate means by which GHG emissions from agricultural production may be abated; assess synergies and conflicts between the abatement of other N pollutants on emissions of nitrous oxide (N2O); analyse two records of soil temperature from 1976-2010 from Wolverhampton (UK) and Vienna (Austria). Agricultural emissions of GHGs are not readily abated by ‘end of pipe’ technologies. Large decreases in agricultural GHG emissions may require changes in the production and consumption of food that could have unwelcome impacts on both consumers and producers. However, identifying and prioritizing both modes and locations of production, together with utilizing inputs, such as N fertilizer and livestock feeds, more efficiently can reduce GHG emissions while maintaining outputs. For example, GHG emissions from livestock production may be lessened by increasing the longevity of dairy cows, thereby decreasing the proportion of unproductive replacement animals in the dairy herd. Sourcing a larger proportion of calves from the dairy herd would decrease emissions of GHGs from beef production. The distance between the region of food production to that of consumption has relatively little impact on total GHG emissions per tonne of food product. Due to greater productivity or lesser energy inputs, importing some foods produced in other parts of the world may decrease GHG emissions per tonne compared with UK production, despite the additional emissions arising from long-distance transport. Manure application techniques to abate ammonia (NH3) emissions do not axiomatically increase emissions of N2O and may decrease them. Soil temperature measurements from 1976 to 2010 were consistent with the warming trends reported over the last 40 years.

No description available.