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A total maximum daily load (TMDL) assessment in the Upper North Bosque River (UNBR) has mandated reductions of soluble reactive phosphorus (SRP). The large concentrations of dairies in the UNBR watershed have been identified as a source of the SRP. Agricultural best management practices (BMPs) can be used to reduce in-stream loads of manure nutrients from confined dairy feeding operations (CAFOs). A new BMP utilizes turfgrass sod to export composted dairy manure nutrients out of the impaired watershed in a sustainable manner. Previous plot-scale experiments have showed that 46 to 77% of applied phosphorus (P) and 36 to 47% of applied nitrogen (N) were removed in a single sod harvest. Two, 1.4 ha turfgrass fields were instrumented to measure runoff flow and sediment and nutrient transport. One turfgrass field was topdressed with composted dairy manure and fertilizer N and the other with fertilizer N only. A total of 3.5% of the applied manure P and 3.1% of applied manure N were lost in the surface runoff over a 1.5 year period. The runoff data from the experimental fields were used to calibrate and validate Soil and Water Assessment Tool (SWAT) model simulations of flow, sediment, organic, and mineral nutrients. The Nash-Sutcliffe model fit statistic was greater than 0.6 for flow, sediment, and nutrients during the calibration period and greater than 0.3 during the validation period. Research results indicated that turfgrass sod can be used to export composted dairy manure out of impaired watersheds to improve water and soil quality.

Anaerobic digestion (AD) is an effective way to convert animal manures into profitable byproducts as well as to reduce the pollution of water, air, and soil caused by these wastes. Conventional high-rate anaerobic reactors cannot effectively process high-solids containing animal manures. The two-phase configuration for AD has several advantages over conventional one-phase processes such as increased stability of the process, smaller and cost efficient process configurations, etc. This study investigated the two-phase AD of dairy manure with particular emphasis on the effects of solids retention time (SRT), organic loading rate (OLR) and pH on anaerobic acidification of unscreened dairy manure / the effects of temperature on biogas production and the comparison of one-phase and two-phase system performance of AD. The results revealed that pre-acidification of dairy manure in daily-fed continuously-mixed reactors with no recycle led to substantial volatile fatty acids production. The optimum operational conditions for anaerobic acidification were determined as SRT and OLR of 2 days and 15 g VS/L.day. The pH control at a range of 5.0-5.5 was not found to be necessary for optimum acidification. Molecular analysis indicated that acidogenic bacteria population increased whilst the aerobic bacteria population decreased as time passed in acidogenic phase. The effect of temperature was clearly observed on biogas production efficiency. Two-phase configuration was determined more efficient than one-phase system. The biogas production in two-phase system was calculated to be 41% higher than that of the one-phase for the same OLR of 3.5 g VS/L.day. This translates into significant performance improvement and reduced volume requirement. This finding represents a further step in the achievement of wider use of simple anaerobic reactor configurations in rural areas.

A total maximum daily load (TMDL) assessment in the Upper North Bosque River (UNBR) has mandated reductions of soluble reactive phosphorus (SRP). The large concentrations of dairies in the UNBR watershed have been identified as a source of the SRP. Agricultural best management practices (BMPs) can be used to reduce in-stream loads of manure nutrients from confined dairy feeding operations (CAFOs). A new BMP utilizes turfgrass sod to export composted dairy manure nutrients out of the impaired watershed in a sustainable manner. Previous plot-scale experiments have showed that 46 to 77% of applied phosphorus (P) and 36 to 47% of applied nitrogen (N) were removed in a single sod harvest. Two, 1.4 ha turfgrass fields were instrumented to measure runoff flow and sediment and nutrient transport. One turfgrass field was topdressed with composted dairy manure and fertilizer N and the other with fertilizer N only. A total of 3.5% of the applied manure P and 3.1% of applied manure N were lost in the surface runoff over a 1.5 year period. The runoff data from the experimental fields were used to calibrate and validate Soil and Water Assessment Tool (SWAT) model simulations of flow, sediment, organic, and mineral nutrients. The Nash-Sutcliffe model fit statistic was greater than 0.6 for flow, sediment, and nutrients during the calibration period and greater than 0.3 during the validation period. Research results indicated that turfgrass sod can be used to export composted dairy manure out of impaired watersheds to improve water and soil quality.

Phosphorus (P) pollution has been identified as the most significant agriculture-related threat to water quality impairment in Canada. One approach to reduce P pollution is to identify soils with high P loss potential and develop management strategies to minimize that risk. This thesis contributes towards greater understanding of short- and long- term P dynamics in soils to which different P sources had been applied (Chapters 3 and 4) and improvement in the P measurements for determining long-term P loss potential (Chapter 5). Chapter 3 evaluated immediate and residual effects of swine manure and fertilizer on soil P. Soils were sampled from Brookston clay loam in south-western Ontario, Canada which were treated with liquid (LM), solid (SM), composted (MC) manure and fertilizer, only in the corn phase. Soils were analyzed using a modified Hedley’s fractionation. All P sources influenced soil labile and moderately labile P in the year of application, while MC and SM showed significant residual impacts in the following year. Residual effects of MC and SM are beneficial for crops; however, there may be a P loss potential through leaching and runoff. Chapter 4 considered long-term effects of dairy manure slurry (DMS) and ammonium nitrate (AN) on soil P. Soils were sampled from south coastal region of BC, Canada, which were treated with DMS or AN at 50 or 100 kg NH4-N ha-1, and analyzed using a modified Hedley’s fractionation. DMS significantly increased labile and moderately stable P in surface soil, indicating short- and long-term impacts on P availability and loss potential. Chapter 5 analyzed a new test to predict long-term soil P loss potential. Soils were collected from four agro-ecological areas across Canada, and analyzed using Mehlich-3, Olsen, Resin strips (RMS), FeO-strips, and new procedures: various combinations of NaOH with and without EDTA, with four shaking periods. Statistically significant linear and quadratic relationships between the RMS and NaOH with EDTA-P indicated that the latter provide an efficient basis for predicting long-term soil P loss potential. A highly significant relationship between RMS-P and 0.025M NaOH with EDTA-P indicates this extractant was effective for measuring Total Releasable P. / Agriculture and Agri-Food Canada

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.

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The objectives of this study were to (1) determine an intermittent aeration strategy that could be used to conserve nitrogen (N) via nitrification in dairy manure, (2) determine the effect of recycled flush water on the bio-availability of N during nitrification, and (3) determine effective and economical dosages of chemicals to remove phosphorus (P) from liquid dairy manure. Intermittent aeration strategies, defined in terms of time the aerator is on and off (ON h:OFF h), could be used to conserve N in dairy manure. Testing of four treatments (continuous aeration [100%], 1h:0.33h [75%], 1h:0.67h [60%], and 1h:1h [50%]) showed that only treatments using air provided for 100% and 75% of the time could support nitrification. The 100% and 75% aeration treatments conserved an average of 38% and 25% of influent total ammonia nitrogen (TAN) as nitrite-N+nitrate-N, respectively. Less than 2% of influent TAN was conserved using 60% and 50% treatments. The effect of manure handling technique on N bioavailability and nitrification was tested using flushed and scraped dairy manure. Nitrification was inhibited in scraped manure. Four aluminum- and iron-based salts and five cationic polyacrylamide polymers were evaluated for P removal using jar tests. Ferric chloride (FeCl3·6H2O), aluminum sulfate (Al2[SO4]3·13H2O, alum), and Superfloc 4512 were selected for further study. Polymer addition enhanced floc size and improved P removal. Treatment of manure (0.89% total solids) from Tank 2 at Virginia Tech's dairy using either FeCl3 or alum in combination with polymer resulted in more than 90% P removal. Chemical treatment and transport of P-rich sludge from a 2,270 cubic meter storage tank would result in an estimated 40% cost savings over transport of the entire manure volume offsite for land application elsewhere. The manure treatment strategies tested provide some solutions to dairy farmers regarding adjustment of N:P ratios so that manure can be applied to meet nutrient needs of crops while adhering to regulations set forth by nutrient management plans. / Master of Science

Over the last two decades, livestock operations have become highly concentrated due to growing trends towards larger, more confined facilities and a decrease in cropland on smaller farms. This has led to greater amounts of excess manure nutrients on farms, increasing the potential for nutrient pollution of water bodies from runoff. The purpose of this study was to determine if enhanced biological phosphorus removal (EBPR) is a viable alternative for managing excess manure nutrients on dairy farms. Assessment of EBPR involved the investigation of various aspects of wastewater treatment modeling and design and farm nutrient management. The fermentation potential (volatile fatty acid (VFA) production) of dairy manure was determined through laboratory analysis to be 15.3% of the total COD. Total VFA production was composed of 57, 23, and 20% acetic, propionic, and butyric acids, respectively. The EBPR component of the BioWin wastewater treatment model was evaluated through a sensitivity analysis. The parameters to which effluent phosphate (PO4) concentration was most sensitive were maximum specific growth rate, growth yield, aerobic PO4 uptake rate per unit poly-b-hydroxybutyrate (PHB) utilized, PHB yield from VFA, PO4 release per unit VFA uptake, and fraction of releasable PO4. An EBPR sequencing batch reactor (SBR) was designed for a dairy farm with 700 lactating cows and 325 ha of corn silage. An economic analysis of EBPR for dairy farms employing P-based manure applications was completed. The cost of hauling excess manure to nutrient deficient farms was the most significant expense in comparing costs of manure management with and without EBPR. For a herd of 700 lactating cows, utilizing EBPR was more economical for farms with 270 ha or less cropland, while EBPR did not offer an economic advantage for farms over 270 ha. / Master of Science

In an effort to develop cost-effective technologies for the removal of ammonium nitrogen from dairy waste, a novel biological wastewater treatment process, utilizing anaerobic ammonium oxidation (anammox), referred to as Oxygen-Limited Autotrophic Nitrification and Denitrification (OLAND) was examined. Due to the potential use of OLAND-based systems in dairy manure management, a detailed water quality assessment of a modern dairy farm manure treatment-system was conducted. The Johnson Highland Dairy Farm, Glade Spring, Virginia, was selected for this assessment and a comprehensive analysis of the wastewater characteristics throughout the confined animal feeding operation was completed. The results suggest that ammonia concentrations in the anaerobic storage facility was high enough to justify use of treatment technologies that reduce ammonia loads in stored dairy waste. A lightly loaded Fixed Film Bioreactor (FFBR), in which the OLAND process was desired to occur, was then constructed in the laboratory and monitored over 51 days. Of particular interest was the time taken to achieve stable performance of this OLAND system. Furthermore, a protocol was developed to determine whether OLAND based metabolism was occurring. Ammonium nitrogen removal efficiency in the FFBR throughout the 51-day monitoring period was high, averaging approximately 95 % for the length of the study. From day 32 to 51, simultaneous removal of both ammonium and nitrite with a low level of concomitant nitrate production was observed, a key indicator of possible anammox activity. Stoichiometric ratios calculated for the FFBR compared favorably with those already established for OLAND systems. The developed protocol, incorporating anaerobic and aerobic batch experiments, to verify the occurrence of OLAND based metabolism did not yield expected results and described poorly what was being observed in the FFBR. Volatilization of ammonia during the experimental test was suspected and should be controlled when the protocol is performed in the future. / Master of Science