Robert E. Graves

Ammonia and Greenhouse Gas Flux from Manure in Freestall Barn with Dairy Cows on Precision Fed Rations

Two lactating cow trials were conducted to evaluate the impact of diets differing in silage source (alfalfa/maize vs. grass/maize or maize/hay) and maize grain particle size (fine vs. coarse) on ammonia (NH3), carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) emissions from a freestall barn floor with two groups of 60 cows each. In addition, the effects of environment (temperature, humidity) and management (manure depth, time since manure removal) factors were evaluated. Manure (feces and urine), spilled feed, bedding, and spilled drinking water were allowed to collect on the solid concrete, sloped barn alleys and were scraped twice a day. Gas fluxes from the freestall floor were measured at 64 locations over a 12 h period using a fast-response, non-steady-state flux chamber and an infrared photoacoustic gas analyzer during 18 trial days over a 9-month period. Fluxes of ammonia from manure on the barn floor were within average reported values for these 165 g kg-1 crude protein diets. All gas fluxes were similar (p = 0.253 to 0.977) regardless of silage source and maize grain particle size. The highest average ammonia emissions coincided with higher environmental temperature at 30 NH3 g AU-1 d-1, while the highest average greenhouse gas emissions from the manure on the floor were 10 g AU-1 d-1 for CH4 and 580 g AU-1 d-1 for CO2. Time in hours since scraping the floor had little impact on the production of ammonia, but greenhouse gas loss from the barn floor was reduced after scraping. Air and manure temperatures were positively correlated with emissions of NH3, CO2, and CH4 (p = <0.0001). NH3 (g AU-1 d-1) = (R2 = 0.38) for indoor air temperatures (Tair, °C) over the range from -5°C to 32°C (AU = 500 kg animal unit). Ammonia emissions were positively correlated with CO2 and CH4 gas emissions, suggesting that NH3 release from the manure was controlled to some extent by microbial activity and similar environmental factors. Nitrous oxide emissions remained <0.1 g AU-1 d-1 and were relatively constant for all diets and trials.

CIP Cleaning of a Pipeline Milking System Using Electrolyzed Oxidizing Water

Electrolyzed oxidizing (EO) water is a technology that electrolyzes a weak sodium chloridesolution into sodium and chlorine, resulting in two solutions alkaline and acid. The goal of thisresearch is to determine if EO water is an acceptable cleaning agent for pipeline milking systems.After constructing 1.5 inch-diameter pipeline milking system, the system was soiled using raw milkthat had been spiked with common raw milk microorganisms. After soiling, the system was rinsedwith warm water and then evaluated in several locations for initial counts. To evaluate the level ofsoiling, the surfaces were evaluated using an ATP bioluminescence method. The milk contactsurfaces were also swabbed for microbial analysis. The pipeline system was then washed with analkaline EO water treatment followed by an acidic EO water treatment. After treatment, theeffectiveness of the treatment was evaluated by ATP bioluminescence and microbiological analysis.First, a 10 min wash with 60C alkaline water followed by a 10 min wash with 60C acid watersuccessfully removed all detectable bacteria and ATP from the non-porous milk contact surfaces.Shorter treatment times (5 and 7.5 min) with EO water were also tested, along with a controltreatment using conventional dairy cleaning chemicals. Using ANOVA, there were no significantdifferences between the EO water treatments and the conventional treatment, however the 5-min EOwater treatment was significantly less effective than the 10-min treatment.

CLEANING MILKING SYSTEMS USING ELECTROLYZED OXIDIZING WATER

Electrolyzed oxidizing (EO) water is a novel cleaning and disinfecting agent, produced by separating a weak sodium chloride solution into alkaline and acidic components. A pilot-scale pipeline milking system was soiled using raw milk inoculated with common microorganisms. The milking system was then washed with alkaline EO water followed by acidic EO water. After cleaning, the effectiveness of the EO water treatment was evaluated by ATP bioluminescence and microbiological analysis. A 10 min wash with 60°C alkaline EO water followed by a 10 min wash with 60°C acid EO water successfully removed all detectable bacteria and ATP from the non-porous milk contact surfaces. Shorter treatment times (5 and 7.5 min) with EO water were also evaluated, along with a control treatment using conventional dairy cleaning chemicals. There were no significant differences between the 10 min and 7.5 min EO water treatments and the conventional treatment. In a longer-term soiling-washing simulation, only the 7.5 min EO water treatment was evaluated after ten soiling and cleaning cycles, and it was

OPTIMIZATION OF WINDROW FOOD WASTE COMPOSTING TO INACTIVATE PATHOGENIC MICROORGANISMS

Composting is a popular means of treating organic wastes, and properly controlled composting can destroy the pathogenic microorganisms present in wastes for a human- and environment-friendly end product. Therefore, optimization of windrow composting of food waste, manure, and bulking agents was evaluated for maximum pathogen inactivation (Salmonella and E. coli O157:H7). Seasonal effects on reductions of Salmonella and E. coli O157:H7 according to the compost temperatures were studied (90 to 150 days). Fecal coliforms and fecal streptococcus were also monitored during composting. The most probable number (MPN) method was used for enumerating both indicator and pathogenic microorganisms. The results of this study indicated that seasonal differences caused significant effects on the peak temperatures and the duration of high thermophilic temperatures (>55°C) of windrows. Winter conditions resulted in inconsistent inactivation of pathogenic microorganisms including regrowth to high values during several time intervals. The reduction levels of Salmonella spp. and E. coli O157:H7 ranged from initial ranges of 377-483 MPN/g to final ranges of 6-150 MPN/g in winter, and to <0.3 MPN/g in summer. It was also observed that summer composting resulted in a better correlation (r2 > 0.90) between the number of fecal coliforms and the pathogenic microorganisms (Salmonella spp. and E. coli O157:H7). Fecal streptococcus was only slightly reduced in the majority of the trials from 377-483 MPN/g to 221-514 MPN/g in winter and from 365-460 MPN/g to a range of 11-265 MPN/g in summer. Extreme Vertices Mixture Design (EVMD) analysis suggested an optimum mixture as: 43.3% food waste, 28.3% manure, and 28.3% bulking agents. The performance of the optimum mixture has been validated, achieving a high level of inactivation of pathogens similar to that with previous trials, with good correlations of fecal coliforms to the pathogens of interest, but with a high resistance of fecal streptococcus to inactivation. It was concluded that the EVMD design was successful in optimizing mixture components for windrow composting in order to achieve maximum pathogen reduction.

Feedstock optimization of in-vessel food waste composting systems for inactivation of pathogenic microorganisms.

An optimum composting recipe was investigated to reduce pathogenic microorganisms in a forced-aerated in-vessel system (55 liters). The feedstocks used for in-vessel composting were food waste, cow manure, and bulking materials (wood shavings and mulch hay). A statistical extreme vertices mixture design method was used to design the composting experiments and analyze the collected data. Each mixture (nine total) was replicated randomly three times. Temperature was monitored as an indicator of the efficiency of the composting experiments. The maximum temperature values of the mixtures were used as a response for both extreme vertices mixture design and statistical analyses. Chemical changes (moisture content, carbon/nitrogen ratio, volatile solids, and pH) and reductions of indicator (fecal coliforms and fecal streptococci) and pathogenic microorganisms (Salmonella and Escherichia coli O157:H7) were measured by the most-probable-number method before and after a 12-day composting period. Maximum temperatures for the tested compost mixtures were in the range of 37.0 to 54.7 degrees C. Extreme vertices mixture design analysis of the surface plot suggested an optimum mixture containing 50% food waste, 40% manure, and 10% bulking agents. This optimum mixture achieved maximum temperatures of 54.7 to 56.6 degrees C for about 3.3 days. The total reduction of Salmonella and E. coli O157:H7 were 92.3%, whereas fecal coliforms and fecal streptococci reductions were lower (59.3 and 27.1%, respectively). Future study is neededto evaluate the extreme vertices mixture design method for optimization of large-scale composting.

Amendments for mitigation of dairy manure ammonia and greenhouse gas emissions: Preliminary screening

Amendments can be practical and cost-effective for reducing ammonia [NH3] and greenhouse gas [GHG] emissions from dairy manure. In this study, the effect of 22 amendments on NH3 and GHG carbon dioxide [CO2], methane [CH4] and nitrous oxide [N2O] emissions from dairy manure were simultaneous investigated at room temperature (20oC). Dairy manure slurry (2 kg; 1:1.7 urine:feces; 12% total solids) was treated with various amendments, representing different classes of product, following the suppliers’ recommended rates. In this screening of products, one sample of each amendment was evaluated along with untreated manure slurry with repeated measurements over 24 h. Gas emissions were measured after short (3 d) and medium (30 d) storage duration using a photoacoustic multi-gas analyzer. Six amendment products that acted as microbial digest, oxidizing agent, masking agent or adsorbent significantly reduced NH3 by >10% (P = 0.04 to <0.001) after both 3 and 30 d. Microbial digest/enzymes with nitrogen substrate appeared effective in reducing CH4 fluxes for both storage times. Most of the masking agents and disinfectants significantly increased CH4 in both storage periods (P = 0.04 to <0.001). For both CH4 and CO2 fluxes, aging the manure slurry for 30 d significantly reduced gas production by 11 to 100% (P <0.001). While some products reduced emissions at one or both storage times, results showed that the ability of amendments to mitigate emissions from dairy manure is finite and re-application may be required even for a static amount of manure. Simultaneous measurement of gases identified glycerol as a successful NH3 reduction agent while increasing CH4 in contrast to a digestive-microbial product that significantly reduced CH4 while enhancing NH3 release.

MODELING OF COMPOST TEMPERATURE AND INACTIVATION OF SALMONELLA AND E. COLI O157:H7 DURING WINDROW FOOD WASTE COMPOSTING

A simulation model was developed to predict temperature and inactivation of E. coli O157:H7 and Salmonella during windrow composting. In particular, the model included an energy balance to estimate the change in temperature based on heat generated by biological decomposition and heat losses by convection, conduction, evaporation, and radiation. The model was validated with the measured data for the effects of seasonal variation on compost temperature and pathogen reduction. Sensitivity analysis was performed on the model to evaluate the variations in both seasons (winter and summer) and moisture contents (40% to 80%). The model showed the highest variation between experimental and predicted data only in winter composts. The results suggested that moisture content of 40% to 60% was appropriate for summer and 40% to <60% for winter composting. Higher moisture levels did not demonstrate pathogen inactivation during winter conditions, whereas it took a month to eliminate the pathogens in summer according to the model predictions. Overall, the model was promising for evaluation of the composting process for different conditions. Further research is needed to improve the model predictions using measured process parameters under different environmental conditions.

Optimization of Windrow Food Waste Composting to Inactivate Pathogenic Microorganisms

Composting is a popular means of treating organic wastes. Properly controlled composting can destroy the pathogenic microorganisms present in wastes for environmentally friendly end product. Optimization of windrow composting of food waste, manure, and bulking agents was evaluated for maximum pathogen inactivation (Salmonella and E. coli O157:H7). Seasonal effects on reductions of Salmonella and E. coli O157:H7 related to the compost temperatures were studied (90-150 days). Fecal coliforms and fecal streptococcus were also monitored during composting. The results of this study indicated that seasonal differences caused significant effects on the peak temperatures and the duration of high thermophilic temperatures ( =55oC) of windrows. Winter conditions resulted in inconsistent inactivation of pathogenic microorganisms including regrowth to high values during several time intervals. The reduction levels of Salmonella spp. and E. coli O157:H7 ranged from initial ranges of 377-483 MPN/g to final ranges of 6-150 MPN/g in winter, and to = 0.3 MPN/g in summer. It was also observed that summer composting resulted in a better correlation (r2>0.90) between number of fecal coliforms and the pathogenic microorganisms (Salmonella spp and E. coli O157:H7). Fecal streptococcus was slightly reduced in most trials from 377-483 to 221-514 MPN/g in winter and from 365-460 MPN/g to a range of 11-265 MPN/g in summer. Extreme Vertices Mixture Design (EVMD) analysis suggested an optimum mixture as: 43.3% food waste, 28.3% manure, and 28.3% bulking agents. The performance of the optimum mixture has been validated, achieving a high level of inactivation of pathogens similar to previous trials, good correlations of fecal coliforms to the pathogens of interest, and high resistance of fecal streptococcus. Therefore, it was concluded that the EVMD design was successful in optimizing windrow composting for maximum pathogen reduction.

Amendments for mitigation of odor emissions from dairy manure: Preliminary screening

Manure amendments have shown variable effectiveness in reducing odor. Twenty-two amendments were evaluated for dairy manure odor stored at 20oC for 3 d and 30 d. Amendments represented different classes of product including microbial, oxidizing agent, disinfectant, masking agent, and adsorbent. Each amendment was added to 2 kg dairy manure (1:1.7 urine:feces, 12% total solids) following recommended rates. In this preliminary screening, one sample (n=1) of each amendment was evaluated along with untreated manure (Control). Odor emissions from each amendment and Control was estimated twice by five qualified odor assessors (n=10) after each storage duration following an international standard method for Triangular Forced-Choice Olfactometry. Odor quality was quantified using a hedonic tone scale, a Labeled Magnitude Scale and ASTM methods for suprathreshold odor intensity and an odor character wheel for description. Odor emissions were significantly reduced at 30 d versus 3 d incubation (P<0.0001) with no amendment effective for both incubation times. Likewise, for all amendments tested, aging the manure slurry for 30 d reduced malodor and odor intensity by 10 to 105% (P<0.0001). A microbial digest/enzyme product (proprietary), disinfectant (hydrogen peroxide) and masking agent (Hyssopus officinalis essential oil) provided significant short-term control of odor (P <0.0001). However, after 30 d, only a proprietary microbial aerobic/facultative product and a proprietary mix of chemicals, both with weekly re-application, retained efficacy. Hedonic tone indicated an improvement to “slightly to moderately unpleasant” smell versus untreated manure for all amendments except clinoptilolite zeolite. Hedonic tone improvement was correlated with reduced manure odor intensity.

Prediction of Hedonic Tone Using an Electronic Nose and Artificial Neural Networks

An electronic nose was used in conjunction with a human panel and artificial neural networks to predict human assessments on a hedonic (pleasantness) scale of various odors, including fragrances, spent and phase I mushroom substrate odors. After calculating the average rating from the human panel for each odor sample, the hedonic rating was matched with the 32 electronic nose sensor readings from the corresponding sample. The data were then used to develop and train artificial neural networks. Several neural networks were developed and tested by comparing predicted hedonic tone from the neural networks with the average hedonic tone assessed by the human panel. The general regression neural network using 11 e-nose sensors as input gave the best test set results, yielding an r2 value of 0.92, and a root mean error of 0.06 hedonic tone units. The genetic strategy neural network gave the best validation results, with an r2 value of 0.67 and a root mean error of 1.11 pleasantness units. These results provide evidence that an electronic nose could potentially be used to assess the quality of odors in a similar manner to a human panel.