Eberhard Hartung

Reduction of Ammonia and Odor Emissions from a Piggery with Biofilters

Long-term measurements were carried out in parallel on two biofilters under practical conditions to test their reduction potential for ammonia and odor emissions. The average efficiency of the ammonia reduction calculated for the first measuring period (6.5-year-old filterbed material) was about 15% at biofilter 1 and 36% at biofilter 2. The average efficiency of the odor reduction calculated for the first measuring period was about 78% at biofilter 1 and 80% at biofilter 2. The efficiency of ammonia reduction was mainly influenced by the air flow rate. The odor reduction was mainly influenced by the odor concentration before the filterbed. The cleaning efficiency of the biofilters could be improved by increasing the moisture content of the filterbed material from 20% to 40%, or from 20% to 50%.

Reduction potential of microbial, odour and ammonia emissions from a pig facility by biofilters.

The intention of this study was the determination of the potential to reduce specific microbial bioaerosol (cultivable bacteria and fungi, total cell counts of microbes, airborne endotoxins and microbial volatile organic compounds, MVOC), odour and ammonia emissions from a pig facility by biofilters. Five identical biofilter units in half technical scale were filled with different filter materials (Biochips, coconut-peat, wood-bark, pellets + bark and compost) and connected in parallel to a piggery. The results showed obvious differences between the filter materials. Numbers of airborne cultivable bacteria were decreased by ca. 70 to 95% and the total counts of bacterial cells from ca. 25 to (>) 90%. The total amount of fungal cells was reduced by at least 60%, although the percentage of cultivable moulds in the air after passing the filters was sometimes higher than before. Airborne endotoxins and MVOC were effectively reduced by all filter materials to at least 90%. Regarding odour, the average reduction was between 40 and 83%, whereas only one of the filters proved to be capable of slightly reducing the ammonia emissions. No relationships between odour/ammonia and microbial bioaerosols with regard to the reduction efficiency of the different filter materials or the total load of the emitted air could be established. A tendency could be shown, that biofilters best capable to reduce odour emitted slightly more airborne bacteria, both cultivable and total cell counts.

Invited review: Technical solutions for analysis of milk constituents and abnormal milk

Information about constituents of milk and visual alterations can be used for management support in improving mastitis detection, monitoring fertility and reproduction, and adapting individual diets. Numerous sensors that gather this information are either currently available or in development. Nevertheless, there is still a need to adapt these sensors to special requirements of on-farm utilization such as robustness, calibration and maintenance, costs, operating cycle duration, and high sensitivity and specificity. This paper provides an overview of available sensors, ongoing research, and areas of application for analysis of milk constituents. Currently, the recognition of abnormal milk and the control of udder health is achieved mainly by recording electrical conductivity and changes in milk color. Further indicators of inflammation were recently investigated either to satisfy the high specificity necessary for automatic separation of milk or to create reliable alarm lists. Likewise, milk composition, especially fat:protein ratio, milk urea nitrogen content, and concentration of ketone bodies, provides suitable information about energy and protein supply, roughage fraction in the diet, and metabolic imbalances in dairy cows. In this regard, future prospects are to use frequent on-farm measurements of milk constituents for short-term automatic nutritional management. Finally, measuring progesterone concentration in milk helps farmers detect ovulation, pregnancy, and infertility. Monitoring systems for on-farm or on-line analysis of milk composition are mostly based on infrared spectroscopy, optical methods, biosensors, or sensor arrays. Their calibration and maintenance requirements have to be checked thoroughly before they can be regularly implemented on dairy farms.

Use of near infrared spectroscopy in monitoring of volatile fatty acids in anaerobic digestion.

Recently biogas production from agricultural sources has rapidly developed. Therefore the demands on biogas plants to optimise the efficiency of the anaerobic digestion (AD) process have grown immensely. At present there is no online-supervision tool available to monitor the AD process, but costly and time-consuming chemical analyses are necessary. The possibility to use near-infrared spectroscopy (NIRS) in order to track relevant process parameters like total volatile fatty acids (VFA), acetic acid and propionic acid was investigated in the present research project. A NIR-sensor was integrated into a full-scale 1 MW biogas plant and NIR-spectra of the fermenter contents were recorded semi-continuously for 500 days. Weekly samples were taken and analysed for the above mentioned parameters. Calibration models were calculated, capable of following these parameters: VFA (r(2)=0.94), acetic acid (r(2)=0.69), propionic acid (r(2)=0.89).

Use of near infrared spectroscopy in online-monitoring of feeding substrate quality in anaerobic digestion

In order to keep the anaerobic process stably and uniformly producing biogas it needs to be supplied with either an even amount of substrate of stable quality or varying amounts according to variations in quality. Feeding amounts are usually adjusted manually as a reaction to changing rates of biogas production. Continuous information about the actual substrate quality is not available and feedstuff analyses are costly. Aim of this study was to assess the feasibility of near infrared spectroscopic (NIRS) online monitoring of substrate quality in order to find ways towards more exact control of biogas plant feeding. A NIRS sensor system was designed, constructed and calibrated for continuous monitoring of (RMSECV in brackets) dry matter (DM) (0.75%fresh matter (FM)), volatile solids (0.74%FM), crude fat (0.09%FM), crude protein (0.22%FM), crude fiber (1.50%DM) and nitrogen-free extracts (0.93%FM) of maize silage.

Accuracy of in-line milk composition analysis with diffuse reflectance near-infrared spectroscopy

Knowledge of daily milk composition changes can assist in monitoring dairy cow health and can help to detect nutritional imbalances. An analytical tool offering the possibility of analyzing milk during the daily milking routine would provide such information. Near-infrared (NIR) spectroscopy can analyze multiple constituents in a given substrate at the same time. In this study, a special NIR in-line milk-analyzing device was designed, and its ability to predict the contents of fat, protein, lactose, and urea and the somatic cell count in milk during the milking process was evaluated. The NIR spectra were acquired with a diode array spectrometer in diffuse reflection in the wavelength range 851 to 1649 nm. The spectra originated from a total of 785 partial milkings out of 84 composite milkings. Corresponding subsamples of the composite milkings were used for reference analysis (n=785). Excellent validation results were obtained with regard to the coefficients of determination (R(2)=0.99, 0.98, and 0.92), and standard errors of prediction (0.09, 0.05, and 0.06) for fat (%), protein (%), and lactose (%), respectively. Satisfying results were achieved for urea content (mg/L) and logarithmically transformed SCC in milk, with R(2) of 0.82 and 0.85 and standard errors of prediction of 19.3 and 0.18, respectively. The accuracy of predicting protein, lactose, and urea contents was in accordance with international recommendations for reproducibility specified for in-line analytical devices.

Rumination activity of dairy cows in the 24 hours before and after calving

Monitoring rumination behavior serves multiple purposes in feeding and herd management of dairy cows. The process of calving is a major event for cows, and a detailed understanding of alterations in behavioral patterns of animals in the time around calving is important in calving detection. The objective of this study was to describe the short-term changes in rumination patterns in dairy cows immediately before and after parturition. In total, 17 cows were fitted with rumination sensors that were able to monitor rumination time, number of rumination boli, and number of rumination jaw movements. Rumination time was decreased in the last 4h antepartum and in the first 8h postpartum. Cows stopped ruminating 123 ± 58 min (mean ± standard deviation) before calving and resumed ruminating 355 ± 194 min after calving. The number of rumination jaw movements and boli per day were decreased in the 24-h period postpartum. Rumination rate, the number of rumination jaw movements per rumination minute, and the number of boli per rumination minute changed little around calving. The calving event primarily influenced the duration and frequency of various rumination characteristics but not rumination intensity. Among detected characteristics, rumination time showed the greatest potential for monitoring of calving events.

REDUCTION OF GAS, ODOR AND DUST EMISSIONS FROM SWINE OPERATIONS WITH BIOFILTERS

The intention of this investigation was to optimize biofilters for the use in livestock facilities, considering both economical (costs) and ecological factors (odor and gas reduction). Two different phases were conducted. In phase A five different biofilter materials (biochips, coconutpeat, bark-wood, pellets+bark and compost) were tested with the aim to select the best material regarding the odor reduction and over all efficiency. In conclusion of phase A biochips, a new filter material, was selected due to it’s high average odor reduction (81 %) in combination with a very low flow resistance. A significant reduction of coarse particles was found for all filter materials. The main factors influencing the filtration characteristic are water vapor condensation, water content of the filter material and hygroscopical properties of the particles. In phase B biochips and coconut fiber peat were tested under different operating conditions (0.5 an 1m bulk layer; down and up flow air supply). The odor emission was in average reduced between 61 and 75 %. The main factor could be found the high of odor concentration in the waste air as well as the homogenous moisture distribution of filter materials. Biofilters were not suitable for a high ammonia reduction (9 up to 26 %). All tested filter materials showed none or only a low reduction potential for CO2 and CH4. Fine structured filter materials can increase the N2O emission, probably due to anaerobic processes. All in all the investigation showed that the efficiency of biofilters can be optimized by using a new filter material and a appropriate operating mode.

Evaluation of Control Strategies for Fogging Systems in Pig Facilities

During hot periods, heat loss from non-sweating animals can be improved by fogging water, in order to cool the ambient air and enhance sensible heat dissipation. A dynamic simulation model was used to test control settings for fogging (turning on the system at 1.5°C and 3°C above setpoint temperature, fogging system duty cycle 30 s and continuous) and ventilation (control range 3°C and 6°C, maximum available ventilation rate 32 and 43 air volume changes per hour). The control settings were evaluated by the number of simulated hours in specific temperature-humidity categories, by maximum temperature-humidity index (THI), as well as by the simulated energy use and water consumption of the control strategies. The validation during four growing-finishing periods resulted in an agreement within the accuracy of the temperature and humidity measurements: the difference between simulated and measured values averaged 0.2°C and 3% RH and 0.6°C and 3% RH with and without evaporative cooling, respectively. The efficacy of the fogging system, as measured by reductions in THI, predicted that alert situations were reduced from 13% to 1%, and all dangerous or emergency situations were eliminated. A year-round simulation resulted in water consumption of 2.5 L pig-1 day-1 if fogging was used on maximum settings and was lowered by about 25% for an optimized ventilation setting, namely a reduced maximum ventilation rate combined with shifting the temperature at which fogging was turned on. The results demonstrate that water and energy consumption can be reduced clearly without considerable negative effects on THI and indoor air climate.

Robustness of near-infrared calibration models for the prediction of milk constituents during the milking process.

The robustness of in-line raw milk analysis with near-infrared spectroscopy (NIRS) was tested with respect to the prediction of the raw milk contents fat, protein and lactose. Near-infrared (NIR) spectra of raw milk (n = 3119) were acquired on three different farms during the milking process of 354 milkings over a period of six months. Calibration models were calculated for: a random data set of each farm (fully random internal calibration); first two thirds of the visits per farm (internal calibration); whole datasets of two of the three farms (external calibration), and combinations of external and internal datasets. Validation was done either on the remaining data set per farm (internal validation) or on data of the remaining farms (external validation). Excellent calibration results were obtained when fully randomised internal calibration sets were used for milk analysis. In this case, RPD values of around ten, five and three for the prediction of fat, protein and lactose content, respectively, were achieved. Farm internal calibrations achieved much poorer prediction results especially for the prediction of protein and lactose with RPD values of around two and one respectively. The prediction accuracy improved when validation was done on spectra of an external farm, mainly due to the higher sample variation in external calibration sets in terms of feeding diets and individual cow effects. The results showed that further improvements were achieved when additional farm information was added to the calibration set. One of the main requirements towards a robust calibration model is the ability to predict milk constituents in unknown future milk samples. The robustness and quality of prediction increases with increasing variation of, e.g., feeding and cow individual milk composition in the calibration model.