Methane Emission Characteristics of Naturally Ventilated Cattle Buildings

The mandate to limit global temperature rise calls for a reliable quantification of gaseous pollutant emissions as a basis for effective mitigation. Methane emissions from ruminant fermentation are of particular relevance in the context of greenhouse gas mitigation. The emission dynamics are so far insufficiently understood. We analyzed hourly methane emission data collected during contrasting seasons from two naturally ventilated dairy cattle buildings with concrete floor and performed a second order polynomial regression. We found a parabolic temperature dependence of the methane emissions irrespective of the measurement site and setup. The position of the parabola vertex varied when considering different hours of the day. The circadian rhythm of methane emissions was represented by the pattern of the fitted values of the constant term of the polynomial and could be well explained by feeding management and air flow conditions. We found barn specific emission minima at ambient temperatures around 10 °C to 15 °C. As this identified temperature optimum coincides with the welfare temperature of dairy cows, we concluded that temperature regulation of dairy cow buildings with concrete floor should be considered and further investigated as an emission mitigation measure. Our results further indicated that empirical modeling of methane emissions from the considered type of buildings with a second order polynomial for the independent variable air temperature can increase the accuracy of predicted long-term emission values for regions with pronounced seasonal temperature fluctuation

Opening Size Effects on Airflow Pattern and Airflow Rate of a Naturally Ventilated Dairy Building-A CFD Study

Airflow inside naturally ventilated dairy (NVD) buildings is highly variable and difficult to understand due to the lack of precious measuring techniques with the existing methods. Computational fluid dynamics (CFD) was applied to investigate the effect of different seasonal opening combinations of an NVD building on airflow patterns and airflow rate inside the NVD building as an alternative to full scale and scale model experiments. ANSYS 2019R2 was used for creating model geometry, meshing, and simulation. Eight ventilation opening combinations and 10 different reference air velocities were used for the series of simulation. The data measured in a large boundary layer wind tunnel using a 1:100 scale model of the NVD building was used for CFD model validation. The results show that CFD using standardk-epsilon turbulence model was capable of simulating airflow in and outside of the NVD building. Airflow patterns were different for different opening scenarios at the same external wind speed, which may affect cow comfort and gaseous emissions. Guiding inlet air by controlling openings may ensure animal comfort and minimize emissions. Non-isothermal and transient simulations of NVD buildings should be carried out for better understanding of airflow patterns

Opening Size E ects on Airflow Pattern and Airflow Rate of a Naturally Ventilated Dairy Building : A CFD Study

Airflow inside naturally ventilated dairy (NVD) buildings is highly variable and difficult to understand due to the lack of precious measuring techniques with the existing methods. Computational fluid dynamics (CFD) was applied to investigate the effect of different seasonal opening combinations of an NVD building on airflow patterns and airflow rate inside the NVD building as an alternative to full scale and scale model experiments. ANSYS 2019R2 was used for creating model geometry, meshing, and simulation. Eight ventilation opening combinations and 10 different reference air velocities were used for the series of simulation. The data measured in a large boundary layer wind tunnel using a 1:100 scale model of the NVD building was used for CFD model validation. The results show that CFD using standard k-ε turbulence model was capable of simulating airflow in and outside of the NVD building. Airflow patterns were different for different opening scenarios at the same external wind speed, which may affect cow comfort and gaseous emissions. Guiding inlet air by controlling openings may ensure animal comfort and minimize emissions. Non-isothermal and transient simulations of NVD buildings should be carried out for better understanding of airflow patterns

Non-linear temperature dependency of ammonia and methane emissions from a naturally ventilated dairy barn

Ammonia (NH3) and methane (CH4) emissions from naturally ventilated dairy barns affect the environment and the wellbeing of humans and animals. Our study improves the understanding of the dependency of emission rates on climatic conditions with a particular focus on temperature. Previous investigations of the relation between gas emission and temperature mainly rely on linear regression or correlation analysis. We take up a preceding study presenting a multilinear regressionmodel based onNH3 and CH4 concentration and temperaturemeasurements between 2010 and 2012 in a dairy barn for 360 cows inNorthern Germany.We study scatter plots and non-linear regressionmodels for a subset of these data and show that the linear approximation comes to its limits when large temperature ranges are considered. The functional dependency of the emission rates on temperature differs among the gases. For NH3, the exponential dependency assumed in previous studies was proven. For methane, a parabolic relation was found. The emissions show large daily and annual variations and environmental impact factors like wind and humidity superimpose the temperature dependency but the functional shape in general persists. Complementary to the former insight that high temperature increases emissions, we found that in the case of CH4, also temperatures below 10 C lead to an increase in emissions from ruminal fermentation which is likely to be due to a change in animal activity. The improved prediction of emissions by the novel non-linear model may support more accurate economic and ecological assessments of smart barn concepts

Factors That Influence Nitrous Oxide Emissions from Agricultural Soils as Well as Their Representation in Simulation Models: A Review

Nitrous oxide (N2O) is a long-lived greenhouse gas that contributes to global warming. Emissions of N2O mainly stem from agricultural soils. This review highlights the principal factors from peer-reviewed literature affecting N2O emissions from agricultural soils, by grouping the factors into three categories: environmental, management and measurement. Within these categories, each impact factor is explained in detail and its influence on N2O emissions from the soil is summarized. It is also shown how each impact factor influences other impact factors. Process-based simulation models used for estimating N2O emissions are reviewed regarding their ability to consider the impact factors in simulating N2O. The model strengths and weaknesses in simulating N2O emissions from managed soils are summarized. Finally, three selected process-based simulation models (Daily Century (DAYCENT), DeNitrification-DeComposition (DNDC), and Soil and Water Assessment Tool (SWAT)) are discussed that are widely used to simulate N2O emissions from cropping systems. Their ability to simulate N2O emissions is evaluated by describing the model components that are relevant to N2O processes and their representation in the model

Influence of barn climate, body postures and milk yield on the respiration rate of dairy cows

The main objective of this study was to identify the influences of different climatic conditions and cow-related factors on the respiration rate (RR) of lactating dairy cows. Measurements were performed on 84 lactating Holstein Friesian dairy cows (first to eighth lactation) in Brandenburg, Germany. The RR was measured hourly or twice a day with up to three randomly chosen measurement days per week between 0700 h and 1500 h (GMT + 0100 h) by counting right thoracoabdominal movements of the cows. Simultaneously with RR measurements, cow body postures (standing vs. lying) were documented. Cows’ milk yield and days in milk were recorded daily. The ambient temperature and relative humidity of the barn were recorded every 5 min to calculate the current temperature-humidity index (THI). The data were analyzed for interactions between THI and cow-related factors (body postures and daily milk yield) on RR using a repeated measurement linear mixed model. There was a significant effect of the interaction between current THI category and body postures on RR. The RRs of cows in lying posture in the THI < 68, 68 ≤ THI < 72 and 72 ≤ THI < 80 categories (37, 46 and 53 breaths per minute (bpm), respectively) were greater than those of standing cows in the same THI categories (30, 38 and 45 bpm, respectively). For each additional kilogram of milk produced daily, an increase of 0.23±0.19 bpm in RR was observed. Including cow-related factors may help to prevent uncertainties of RR in heat stress predictions. In practical application, these factors should be included when predicting RR to evaluate heat stress on dairy farm

Ammonia emission prediction for dairy cattle housing from reaction kinetic modeling to the barn scale

One way to estimate ammonia emission rates from naturally ventilated housing systems is to scale-up mechanistic modeling results. However, obtaining the relevant data to set initial and boundary conditions adequately is usually very challenging and for a whole barn barely possible. This study has investigated the potential of coupling different mechanistic modeling approaches towards an overarching barn scale ammonia emission model, which might permit ammonia emission projections for naturally ventilated housing systems with minimal measurement efforts. To this end, we combined an ammonia volatilization model for shallow urine or slurry puddles with a dynamic mechanistic model of digestion and excretion of nitrogen, an empirical model to estimate urination volumes, semi-empirical models for pH and temperature dynamics of the puddles and a mechanistic air flow model. The ammonia volatilization model was integrated with a time step of one second over a period of twenty-four hours, while the relevant boundary conditions were updated on an hourly base (determined by the other mentioned submodels). Projections and uncertainties of the approach were investigated for a farm case with about ten months of on-farm measurements in a naturally ventilated dairy cattle building with scraped solid floor in Northern Germany. The results showed that the nested model was in general capable to reproduce the long-term emission trend and variability, while the short-term variability was damped compared with the emission measurements. A sensitivity study indicated that particularly a refinement of the submodules for urine puddle alkalizing, urination volume and urea concentration distributions as well as for local near-surface wind speeds have a great potential to further improve the overall model accuracy. The cleaning efficiency of the scraper has turned out to be a crucial and sensitive parameter in the modeling, which so far has been described insufficiently by measurements or modeling approaches

Influence of Preprocessing Methods of Automated Milking Systems Data on Prediction of Mastitis with Machine Learning Models

Missing data and class imbalance hinder the accurate prediction of rare events such as dairy mastitis. Resampling and imputation are employed to handle these problems. These methods are often used arbitrarily, despite their profound impact on prediction due to changes caused to the data structure. We hypothesize that their use affects the performance of ML models fitted to automated milking systems (AMSs) data for mastitis prediction. We compare three imputations—simple imputer (SI), multiple imputer (MICE) and linear interpolation (LI)—and three resampling techniques: Synthetic Minority Oversampling Technique (SMOTE), Support Vector Machine SMOTE (SVMSMOTE) and SMOTE with Edited Nearest Neighbors (SMOTEEN). The classifiers were logistic regression (LR), multilayer perceptron (MLP), decision tree (DT) and random forest (RF). We evaluated them with various metrics and compared models with the kappa score. A complete case analysis fitted the RF (0.78) better than other models, for which SI performed best. The DT, RF, and MLP performed better with SVMSMOTE. The RF, DT and MLP had the overall best performance, contributed by imputation or resampling (SMOTE and SVMSMOTE). We recommend carefully selecting resampling and imputation techniques and comparing them with complete cases before deciding on the preprocessing approach used to test AMS data with ML models

Direct Measurements of the Volume Flow Rate and Emissions in a Large Naturally Ventilated Building

The direct measurement of emissions from naturally ventilated dairy barns is challenging due to their large openings and the turbulent and unsteady airflow at the inlets and outlets. The aim of this study was to quantify the impacts of the number and positions of sensors on the estimation of volume flow rate and emissions. High resolution measurements of a naturally ventilated scaled building model in an atmospheric boundary layer wind tunnel were done. Tracer gas was released inside the model and measured at the outlet area, using a fast flame ionization detector (FFID). Additionally, the normal velocity on the area was measured using laser Doppler anemometry (LDA). In total, for a matrix of 65 × 4 sensor positions, the mean normal velocities and the mean concentrations were measured and used to calculate the volume flow rate and the emissions. This dataset was used as a reference to assess the accuracy while systematically reducing the number of sensors and varying the positions of them. The results showed systematic errors in the emission estimation up to +97%, when measurements of concentration and velocity were done at one constant height. This error could be lowered under 5%, when the concentrations were measured as a vertical composite sample