The Canadian livestock industry generates 150 million tonnes of manure annually and the majority of this manure is land applied. This practice allows the manure nutrients to be recycled to the soil crop system while improving soil fertility. However, land application of manure has the potential to negatively impact soil, water, and air quality if not managed properly. Microbial processes transform the manure nutrients into forms that are susceptible to leaching or volatilization. Balancing the nutrient loss dynamics from fertilized soil is very difficult because the nutrient transformations are affected by the soil environment such as air and water content, pH, and labile carbon content. All of these soil environmental factors can be influenced by manure application practices such as application rate, timing, and manure placement. Knowledge of how these management practices affect the soil environment can help producers make management decisions that reduce the likelihood of soil, water, and air contamination from manure application.<p> Very few data exist on how manure application practices affect odour emissions after spreading. Therefore, the efficiency of subsurface application in reducing odours from manure spreading for both solid and liquid manure was assessed. Flux chambers and dynamic dilution olfactometry were used to measure odour emissions from five livestock manure species applied at three application rates using surface and subsurface application methods. The results indicated that odour concentrations from injected plots were up to 66% (37% on average) lower than concentrations from broadcast applications. Injection seemed to have a larger impact on reducing odours from solid manure than liquid manure, mainly due to efficient manure coverage from solid manure injection. Odours measured immediately after solid manure applications were also 37% lower than from liquid manure applications. In general, odours from both manure types increased with higher application rates, but there was little difference in the odours among low, mid, and high application rates. The specific odour rate (odour emission rate per kg N applied) decreased with application rate due to the reduced surface area available for volatilization of compounds with higher application rates. Based on these results, injection of manure is an effective way to reduce the odour emissions immediately after spreading, particularly for solid manure. However, other factors associated with manure injection, such as the increased power requirement and soil disturbance must be considered when evaluating the overall impact of manure injection versus surface application.<p> The odour data collected in this study described how management practices affected odours immediately after spreading. Knowledge of how these practices affect the emission rate trend over time is required to apply dispersion models to optimize the minimum separation distances for manure spreading activities. The model parameters for an existing volatilization model were determined from field and literature data and the resulting model allowed the effects of application mode (surface vs. subsurface) and manure type (liquid vs. solid) on odour emissions for 48 hours after application to be simulated. The effects of injection depth and a coverage factor on emissions were also simulated. The modeled peak fluxes from liquid manure applications were higher than those for solid manure applications, but the extended duration of odour emissions from solid manure resulted in higher cumulative losses from solid manure applications. While the application rate had no effect on the initial odour flux, higher application rates resulted in higher peak fluxes, higher overall emissions, and longer odour durations for both manure types and application methods. Modest injection depths were shown to reduce odours from both liquid and solid manure applications compared to surface spreading. The percent reductions in cumulative odours due to injection were estimated assuming typical coverage factors. The general predictions of the model developed in this study agree reasonably well with odour emission rate trends reported in literature. Future work should focus on better estimation of the model parameters and the variation of effective diffusivity with time and soil conditions.<p> Greenhouse gas (GHG) emissions from agricultural activities such as land application of livestock manure cannot be ignored when assessing overall emissions from anthropogenic sources. Like odour emissions, the magnitude of the GHG emissions will be influenced by management practices such as manure placement during land application. The GHG fluxes resulting from the surface and subsurface application of liquid and solid manure were also compared within 24 hours of application using a static chamber and gas chromatography. The results showed that carbon dioxide equivalent (CO2-e) fluxes were approximately three times higher from the injected plots than the surface plots for both solid and liquid manure. The elevated CO2-e fluxes were mainly due to a pronounced increase in N2O fluxes which was likely caused by increased denitrification rates. The CO2-e fluxes from the liquid manure applications were also approximately three times higher than the CO2-e fluxes from the solid manure applications, probably due to higher levels of ammonium available for nitrification and subsequent denitrification. The CH4 fluxes were generally low and the treatments had no effect. The measured specific fluxes (total flux per kg N applied) remained relatively constant with application rate, indicating that, in this study, GHG emissions from manure applications were approximately proportional to the amount of land applied manure.<p> While the data from this study showed that manure type and placement influenced short-term nitrous oxide (N2O) emissions, manure management practices (particularly slurry injection or solid manure incorporation) have the potential to influence long-term emissions by changing the magnitude and pattern of the nitrogen cycle in the soil-plant system. Management practices also impact the magnitude of other nitrogen losses (ammonia volatilization, nitrate leaching) which affect indirect N2O emissions. A model that simulates the environmental conditions and nutrient transformations after manure application may allow a more reliable prediction of the effect of management practices on total GHG emissions. Numerous process-based models have been used to estimate N2O emissions as influenced by agricultural practices in Canada. However, these models do not account for enhanced denitrification that potentially exists after slurry injection or manure incorporation, resulting in an underestimation of N2O emissions. A simple mass balance of nitrogen after application to land showed that enhanced denitrification can increase total N2O-N emissions by a factor of 5. By accounting for the increased microbial activity, slower oxygen diffusion and higher water filled pore space that exists after manure injection, models may better estimate N2O emissions from manure application practices.

Efficient disposal methods are needed to manage manure produced by industrial animal production. Saprophagous fly larvae could potentially convert manure into fertilizer and produce protein; however, the process is not well studied. Musca domestica, Hydrotaea aenescens, and Coproica hirtula were investigated to determine the most suitable species and conditions that facilitate efficient poultry manure conversion. The objectives were to (1) develop laboratory protocols and timelines for fly production; (2) identify environmental conditions that affect conversion; and (3) determine the ideal manure moisture content, depth, and fly egg-to-manure ratio for manure conversion and protein production. Mass-production was possible for every species and timelines were established for all species except C. hirtula. The most promising species for use in a conversion system was M. domestica and the presence of C. hirtula facilitated complete conversion. When using these species simultaneously the ideal initial conditions were: 77.5% moisture, 2.9cm deep and 0.82g eggs/kg manure. / OMAFRA/ University of Guelph partnership

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

A project was developed to determine the feasibility of using liquid hog manure as a nutrient source (Nitrogen) for the potato crop. A survey of liquid hog manure storage facilities on Prince Edward Island (P.E.I.) provided a sampling technique and the range of nutrients found on hog farms. It was concluded from the data that there were large differences between farms and on farm manure sampling was required to determine accurate nutrient applications. An infrastructure was designed, constructed and tested for storage, handling and the application of liquid hog manure at the Harrington Research Farm, Crops and Livestock Research Centre, Charlottetown, P.E.I. Data from the first of two three year experiments determined that the placement of liquid hog manure under the sown potato row and beside the row (0.23 m) provided yields better than manure placed between the sown rows. These yields were not different for the extra Nitrogen fertilizer treatment. The second field experiment examined the placement of liquid hog manure by various injector designs between the rows after the potato crop was planted. Potato tuber yield data over the three years indicated no differences among injector design nor between the injector treatments and the treatment which received the extra Nitrogen fertilizer. Over both experiments there was a decline in the severity of Rhizoctonia ( Rhizoctonia solani) in one year for plots receiving manure compared to those which received only inorganic fertilizer. There were no differences in the incidence or severity of scab (Streptomyces scabies) over the study. In general liquid manure can be used as nutrient source for the potato crop on P.E.I.

Stallgödsel består av spillning, gödselvatten, urin och strömaterial. Miljö- och hälsoskadliga ämnen kan också förekomma i form av antibiotikarester eller patogener. Det är därför viktigt att gödselhanteringen sköts på korrekt sätt för att undvika att de skadliga ämnena vållar miljö- och hälsoproblem. Naturliga ämnen som kväve och fosfor förekommer också i stallgödsel bidrar till miljöeffekter som övergödning eller försurning vid felaktig gödselhantering. Miljöeffekterna drabbar i sin tur ekosystem och de konsekvenser som uppstår är bland andra fiskdöd och giftalgblomningar i vattendrag. Examensarbetet kommer att belysa de ekologiska och sociala fördelarna som Kolmården skulle kunna erhålla om de byter gödselhanteringssystem från gödselspridning till rötning. Dessutom kommer Kolmårdens nuvarande stallgödselhantering att beskrivas, aspekter som kan påverka biogaspotentialen att presenteras och den teoretiska mängden biogas som kan utvinnas ur stallgödseln att beräknas. Det första som genomfördes i examensarbetet var att dela in Kolmårdens djur i olika djurgrupper för att kunna utföra beräkningar på biogaspotentialen. Totalt tre djurgrupper figurerar i examensarbetet och de är: fågel, icke-idisslare och idisslare samt allätare och rovdjur När djuruppdelningen var slutförd påbörjades litteraturstudien för att få fram aktuellt material att använda i studien och förbereda de intervjufrågor som sedan användes i samband med ett studiebesök på Kolmården. Stallgödseln som uppkommer i djurparken mockas från djurens hägn och stall varje dag för att sedan lagras på mindre gödselplattor. Dessa töms sedan tre gånger i veckan av entreprenörer som fraktar stallgödseln till en central gödselplatta innan den sprids på arrenderad mark eller överförs till en lantbruksentreprenör. Stallgödseln på Kolmården har teoretisk sett en potential att kunna täcka nära hälften av djurparkens årliga energibehov. Aspekter som påverkar biogasutbytet är föda, strömaterial, näringsinnehåll och antibiotika. / Manure consist of feces, manure water, urine and stray material. Environmental and health harmful substances can also occur in manure. That is why it is important that the manure management is handled in a correct way to avoid the harmful substances that are likely to cause environmental and health problems. Natural substances are also a part of the manure and those are nitrogen and phosphorus and they could cause environmental effects as eutrophication and acidification with insufficient manure management. The environmental effects could affect ecosystems and the consequences could appear as that fishes are dying and poisonous algal blooms in watercourses. The bachelor thesis will illustrate the ecological and social benefits that Kolmården could achieve if they are replacing the current manure management which is manure spreading with anaerobic digestion. Besides that the current manure management will be described in more depth, aspects that could affect the biogas potential will be presented and the theoretical amount of biogas of the manure will be calculated. The first step that was completed in the thesis were to divide Kolmården’s animals into different animal groups that later could be used in the calculations of the biogas potential. Totally there are three animal groups in this thesis and they are: fowl, non-ruminant and omnivore and beast of prey. When dividing the animals was completed the search for literature began with the purpose to get up-to-date material that could be used in the thesis and to prepare the interview questions for the study visit at Kolmården. The amount of manure that emerge in the zoo is mucked from the animal’s pens and stables every day to be stored in small manure storage places. Those are emptied three times a week and freighted to a central storage place before the manure is spread on tenancy land or transferred to a farmer. The theoretical biogas potential from the manure at Kolmården can cover almost half of the energy need in a year. Aspects as food, straw material, nutrition content and antibiotics can affect the biogas yield.

No description available.

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.

The biogeochemical processes responsible for production and emission of ammonia from stored liquid dairy manure are governed by environmental factors (e.g. manure temperature, moisture) and manure characteristics (e.g. total ammoniacal nitrogen concentration, pH). These environmental factors and manure characteristics vary spatially as a result of spatially heterogeneous physical, chemical, and biological properties of manure. Existing process-based models used for estimating ammonia emission consider stored manure as a homogeneous system and do not consider these spatial variations leading to inaccurate estimations. In this study, a one-dimensional compartmental biogeochemical model was developed to (i) estimate spatial variation of temperature and substrate concentration (ii) estimate spatial variations and rates of biogeochemical processes, and (iii) estimate production and emission of ammonia from stored scraped liquid dairy manure. A one-dimension compartmentalized modeling approach was used whereby manure storage is partitioned into several sections in vertical domain assuming that the conditions are spatially uniform within the horizontal domain. Spatial variation of temperature and substrate concentration were estimated using established principles of heat and mass transfer. Pertinent biogeochemical processes were assigned to each compartment to estimate the production and emission of ammonia. Model performance was conducted using experimental data obtained from National Air Emissions Monitoring Study conducted by the United States Environmental Protection Agency. A sensitivity analysis was performed and air temperature, manure pH, wind speed, and manure total ammoniacal nitrogen concentration were identified as the most sensitive model inputs. The model was used to estimate ammonia emission from a liquid dairy manure storage of a dairy farm located in Rockingham and Franklin counties in Virginia. Ammonia emission was estimated under different management and weather scenarios: two different manure storage periods from November to April and May to October using historical weather data of the two counties. Results suggest greater ammonia emissions and manure nitrogen loss for the manure storage period in warm season from May to October compared to the storage period in cold season from November to April. / Ph. D. / Dairy manure is a byproduct of dairy farming that can be used as a fertilizer to provide essential plant nutrients such as nitrogen, phosphorus, and potassium. However, manure can only be applied to crop lands in a certain time of the year during growing seasons. Further, discharge of dairy manure into natural environment is prevented by the environmental regulations. Therefore, manure storage structures are used to store liquid dairy manure until time permits for land application or use for other purposes. During the storage, liquid dairy manure goes through biological, chemical, and physical processes and release manure gases that are linked to deteriorate human and animal health and contribute to environmental pollution. Ammonia is one of the manure gases released to atmosphere from stored liquid dairy manure. Furthermore, release of ammonia from stored manure reduce nitrogen content and reduce fertilizer value of stored manure. Implementing control measures to mitigate ammonia emission is necessary to prevent ammonia emission and reduce nitrogen loss from stored manure. Deciding and applying of appropriate control measures require knowledge of the rate at which ammonia emission occurs and when ammonia emission occurs. Use of process-based models is one of the less expensive and reliable method for estimating ammonia emission from stored liquid dairy manure. Process-based model is a mathematical model that simulates processes related to ammonia production and emission from stored manure. Even though, there are several process-based models available for estimating ammonia emission from stored liquid dairy manure, these models do not fully represent the actual processes and conditions relevant to production and emission of ammonia. For instance, spatial variation of temperature and total ammoniacal nitrogen concentration within stored manure is not considered in existing process-based models. Therefore, in this study a new compartmental process-based model was developed for estimating these spatial variations and production and emission of ammonia from stored liquid dairy manure. The model uses weather data and manure management information as inputs for estimating ammonia emission and nitrogen loss. The performance evaluation of the compartmental process-based model revealed that air temperature, manure pH, wind speed, manure total ammoniacal nitrogen concentration are important model inputs for estimating ammonia emission from stored liquid dairy manure. The model was used to estimate ammonia emission from a dairy farm located in Rockingham and Franklin counties in Virginia. Results suggest greater ammonia emissions and manure nitrogen loss for the manure storage period in warm season from May to October compared to the storage period in cold season from November to April.

A pilot scale nitrogen (N) removal system was constructed and operated for approximately 365 days and was designed to remove inorganic total ammonia nitrogen (TAN) from solids-separated dairy manure. An anaerobic fermenter, upstream of the N removal reactor, produced volatile fatty acids (VFAs), to be used as an electron donor to fuel denitrification, and TAN at a COD:N ratio of 18:1. However, sufficient amounts of non-VFA COD was produced by the fermenter to fuel the denitrification reaction at an average NO3- removal rate of 5.3 ± 2 mg/L NO₃--N. Total ammonia N was removed from the fermenter effluent in an N removal reactor where a series of aerobic and anoxic zones facilitated aerobic TAN oxidation and anoxic NO₃- and NO₂- reduction. The minimum dissolved oxygen (DO) concentration allowing for complete TAN removal was found to be 0.8 mg/L. However, TAN removal rates were less than predicted using default nitrifying kinetic parameters in BioWin®, a biological modeling simulator, which indicated the presence of a nitrification inhibitor in fermented dairy manure. Furthermore, an N balance during the aerobic zone indicated that simultaneous nitrification-denitrification (SND) was occurring in the aerobic zone of the N removal reactor and was most apparent at DO concentrations below 1.3 mg/L. A series of nitrite generation rate (NGR) experiments confirmed the presence of an inhibitor in fermented dairy manure. A model sensitivity analysis determined that the most sensitive ammonia oxidizing bacteria (AOB) kinetic parameters were the maximum specific growth rate, , and the substrate half saturation coefficient, . Nitrifying inhibition terms of competitive, non-competitive, mixed competitive, and un-competitive were applied to the growth rate equation in BioWin® but an accurate representation of the observed TAN removal rates in the pilot scale system could not be found by adjusting the kinetic parameters alone. Reducing the default BioWin® hydrolysis rate by approximately 50% produced a more accurate calibration for all inhibition terms tested indicating that the hydrolyization of organic N in dairy manure is less than typical municipal waste water. / Master of Science

Land application and storage of horse manure and municipal sludge can increase nutrient and bacteria concentrations in runoff. Biochar increases soil nutrient retention when used as a soil amendment. The objectives of this study were to determine if biochar, when mixed with horse manure or sludge, affects runoff concentrations of total Kjehldahl nitrogen (TKN), ammonia-nitrogen (NH3-N), nitrate (NO3-N), total phosphorus (TP), dissolved phosphorus (DP), total suspended solids (TSS), chemical oxygen demand (COD), and fecal coliforms (FC). Horse manure and sludge were applied to 2.4 x 6.1 m fescue plots (six each), with three plots of each material amended with 5-8% biochar w/w. Simulated rainfall (101.6 mm/h) was applied to the 12 treatment plots and three control plots. The first 0.5 h of runoff was collected and analyzed for the above-listed parameters. The data were analyzed using an ANCOVA, with SCS runoff curve number (CN) used as the covariate. In general, CN was directly correlated to runoff concentrations of parameters. Plots with low CN values displayed no treatment differences for any measured parameter. Biochar reduced runoff concentrations of TKN and NH3-N for municipal sludge treatments, and TKN, NH3-N, TP, TSS, and FC for horse manure treatments.