Validation of PigBal model predictions for pig manure production

PigBal is a mass balance model that uses pig diet, digestibility and production data to predict the manure solids and nutrients produced by pig herds. It has been widely used for designing piggery effluent treatment systems and sustainable reuse areas at Australian piggeries. More recently, PigBal has also been used to estimate piggery volatile solids production for assessing greenhouse gas emissions for statutory reporting purposes by government, and for evaluating the energy potential from anaerobic digestion of pig effluent. This paper has compared PigBal predictions of manure total, volatile, and fixed solids, and nitrogen (N), phosphorus (P) and potassium (K), with manure production data generated in a replicated trial, which involved collecting manure from pigs housed in metabolic pens. Predictions of total, volatile, and fixed solids and K in the excreted manure were relatively good (combined diet R2 ≥ 0.79, modelling efficiency (EF) ≥ 0.70) whereas predictions of N and P, were generally less accurate (combined diet R2 0.56 and 0.66, EF 0.19 and –0.22, respectively). PigBal generally under-predicted lower N values while over-predicting higher values, and generally over-predicted manure P production for all diets. The most likely causes for this less accurate performance were ammonium-N volatilisation losses between manure excretion and sample analysis, and the inability of PigBal to account for higher rates of P uptake by pigs fed diets containing phytase. The outcomes of this research suggest that there is a need for further investigation and model development to enhance PigBal’s capabilities for more accurately assessing nutrient loads. However, PigBal’s satisfactory performance in predicting solids excretion demonstrates that it is suitable for assessing the methane component of greenhouse gas emission and the energy potential from anaerobic digestion of volatile solids in piggery effluent. The apparent overestimation of N and P excretion may result in conservative nutrient application rates to land and the over-prediction of the nitrous oxide component of greenhouse gas emissions.

Nitrogen use efficiency in summer sorghum grown on clay soils

Nitrogen fertilizer inputs dominate the fertilizer budget of grain sorghum growers in northern Australia, so optimizing use efficiency and minimizing losses are a primary agronomic objective. We report results from three experiments in southern Queensland sown on contrasting soil types and with contrasting rotation histories in the 2012-2013 summer season. Experiments were designed to quantify the response of grain sorghum to rates of N fertilizer applied as urea. Labelled 15N fertilizer was applied in microplots to determine the fate of applied N, while nitrous oxide (N2O) emissions were continuously monitored at Kingaroy (grass or legume ley histories) and Kingsthorpe (continuous grain cropping). Nitrous oxide is a useful indicator of gaseous N losses. Crops at all sites responded strongly to fertilizer N applications, with yields of unfertilized treatments ranging from 17% to 52% of N-unlimited potential. Maximum yields ranged from 4500 (Kupunn) to 5450 (Kingaroy) and 8010 (Kingsthorpe) kg/ha. Agronomic efficiency (kg additional grain produced/kg fertilizer N applied) at the optimum N rate on the Vertosol sites was 23 (80 N, Kupunn) to 25 (160N, Kingsthorpe), but 40-42 on the Ferrosols at Kingaroy (70-100N). Cumulative N2O emissions ranged from 0.44% (Kingaroy legume) to 0.93% (Kingsthorpe) and 1.15% (Kingaroy grass) of the optimum fertilizer N rate at each site, with greatest emissions from the Vertosol at Kingsthorpe. The similarity in N2O emissions factors between Kingaroy and Kingsthorpe contrasted markedly with the recovery of applied fertilizer N in plant and soil. Apparent losses of fertilizer N ranged from 0-5% (Ferrosols at Kingaroy) to 40-48% (Vertosols at Kupunn and Kingsthorpe). The greater losses on the Vertosols were attributed to denitrification losses and illustrate the greater risks of N losses in these soils in wet seasonal conditions.

Validation of PigBal model predictions for pig manure production

PigBal is a mass balance model that uses pig diet, digestibility and production data to predict the manure solids and nutrients produced by pig herds. It has been widely used for designing piggery effluent treatment systems and sustainable reuse areas at Australian piggeries. More recently, PigBal has also been used to estimate piggery volatile solids production for assessing greenhouse gas emissions for statutory reporting purposes by government, and for evaluating the energy potential from anaerobic digestion of pig effluent. This paper has compared PigBal predictions of manure total, volatile, and fixed solids, and nitrogen (N), phosphorus (P) and potassium (K), with manure production data generated in a replicated trial, which involved collecting manure from pigs housed in metabolic pens. Predictions of total, volatile, and fixed solids and K in the excreted manure were relatively good (combined diet R2 ≥ 0.79, modelling efficiency (EF) ≥ 0.70) whereas predictions of N and P, were generally less accurate (combined diet R2 0.56 and 0.66, EF 0.19 and -0.22, respectively). PigBal generally under-predicted lower N values while over-predicting higher values, and generally over-predicted manure P production for all diets. The most likely causes for this less accurate performance were ammonium-N volatilisation losses between manure excretion and sample analysis, and the inability of PigBal to account for higher rates of P uptake by pigs fed diets containing phytase. The outcomes of this research suggest that there is a need for further investigation and model development to enhance PigBal's capabilities for more accurately assessing nutrient loads. However, PigBal's satisfactory performance in predicting solids excretion demonstrates that it is suitable for assessing the methane component of greenhouse gas emission and the energy potential from anaerobic digestion of volatile solids in piggery effluent. The apparent overestimation of N and P excretion may result in conservative nutrient application rates to land and the over-prediction of the nitrous oxide component of greenhouse gas emissions. © CSIRO 2016.

Odour Emissions from Tunnel Ventilated Poultry Housing

In Australia, factors such as local planning processes, urban encroachment into rural areas and intensification of the poultry industry have increased the potential for odour and dust nuisance. At present, accurate estimates of odour emissions from mechanically ventilated poultry housing systems do not exist for Australian conditions. This has made the poultry industry vulnerable to unsubstantiated criticism. Recently, the Australian poultry industry have made a significant investment in research to obtain accurate estimates of odour, dust and volatile chemical emission rates from typical poultry housing systems. This paper describes the measurement of odour emissions from tunnel ventilated poultry housing systems in different climatic zones in Queensland and Victoria, Australia (humid sub-tropical and Mediterranean respectively) during two seasons (summer and winter). Samples were collected at defined intervals over typical batch production cycles to define the odour emission profiles. These samples were analysed using dynamic olfactometry according to the Australian Standard 4323.3 to derive the odour concentration values. Ventilation rates were measured concurrently, allowing the calculation of odour emission rates. Odour concentration and emission rates were assessed in terms of ventilation rate, ambient and shed air temperature and relative humidity and litter moisture status. Odour emission rates varied with bird age. Seasonal differences in odour emission rate were also observed.

Characterisation of humidity dependence of a metal oxide semiconductor (MOS) sensor array using Partial Least Squares (PLS)

Metal oxide semiconductor (MOS) sensors are a class of chemical sensor that have potential for being a practical core sensor module for an electronic nose system in various environmental monitoring applications. However, the responses of these sensors may be affected by changes in humidity and this must be taken into consideration when developing calibration models. This paper characterises the humidity dependence of a sensor array which consists of 12 MOS sensors. The results were used to develop calibration models using partial least squares. Effects of humidity on the response of the sensor array and predictive ability of partial least squares are discussed. It is shown that partial least squares can provide proper calibration models to compensate for effects caused by changes in humidity.

Characterisation of humidity dependence of a metal oxide semiconductor sensor array using partial least squares

Metal oxide semiconductor (MOS) sensors are a class of chemical sensors that have potential for being a practical core sensor module for an electronic nose system in various environmental monitoring applications. However, the responses of these sensors may be affected by changes in humidity and this must be taken into consideration when developing calibration models. This paper characterises the humidity dependence of a sensor array which consists of 12 MOS sensors. The results were used to develop calibration models using partial least squares (PLS). Effects of humidity on the response of the sensor array and predictive ability of partial least squares are discussed. It is shown that partial least squares can provide proper calibration models to compensate for effects caused by changes in humidity. Special Issue: Selected Paper from the 12th International Symposium on Olfaction and Electronic Noses - ISOEN 2007, International Symposium on Olfaction and Electronic Noses.

Process studies of odour emissions from effluent ponds using machine-based odour measurement

Replicable experimental studies using a novel experimental facility and a machine-based odour quantification technique were conducted to demonstrate the relationship between odour emission rates and pond loading rates. The odour quantification technique consisted of an electronic nose, AromaScan A32S, and an artificial neural network. Odour concentrations determined by olfactometry were used along with the AromaScan responses to train the artificial neural network. The trained network was able to predict the odour emission rates for the test data with a correlation coefficient of 0.98. Time averaged odour emission rates predicted by the machine-based odour quantification technique, were strongly correlated with volatile solids loading rate, demonstrating the increased magnitude of emissions from a heavily loaded effluent pond. However, it was not possible to obtain the same relationship between volatile solids loading rates and odour emission rates from the individual data. It is concluded that taking a limited number of odour samples over a short period is unlikely to provide a representative rate of odour emissions from an effluent pond. A continuous odour monitoring instrument will be required for that more demanding task.

Odour emissions from anaerobic piggery ponds. 2: Improving estimates of emission rate through recognition of spatial variability

Odour emission rates were measured for seven different anaerobic ponds treating piggery wastes at six to nine discrete locations across the surface of each pond on each sampling occasion over a thirteen month period. Significant variability in emission rates were observed for each pond. Measurement of a number of water quality variables in pond liquor samples collected at the same time and from the same locations as the odour samples indicated that the composition of the pond liquor was also variable. The results indicated that spatial variability was a real phenomenon and could have a significant impact on odour assessment practices. Considerably more odour samples would be required to characterise pond emissions than currently recommended by most practitioners, or regulatory agencies.

Odour emissions from anaerobic piggery ponds. 1. Results of a three season, 14-month survey

Odour emission rates were measured for seven different anaerobic ponds treating piggery wastes at six to nine discrete locations across the surface of each pond on each sampling occasion over a 14-month period. Emission rate values varied between ponds, between seasons for the same pond and even for the same pond on different days of a sampling week. Average seasonal emission rates ranged from 7.9 to 46.5 OU/m2 s, while average emission rates ranged from 16.0 to 29.0 OU/m2 s. Factors potentially responsible for the variability in emission rates were investigated, including air and pond liquor temperatures, time of day of sample collection, season and the impact of a prolonged drought.

Non-specific conducting polymer-based array capable of monitoring odour emissions from a biofiltration system in a piggery building

A commercial non-specific gas sensor array system was evaluated in terms of its capability to monitor the odour abatement performance of a biofiltration system developed for treating emissions from a commercial piggery building. The biofiltration system was a modular system comprising an inlet ducting system, humidifier and closed-bed biofilter. It also included a gravimetric moisture monitoring and water application system for precise control of moisture content of an organic woodchip medium. Principal component analysis (PCA) of the sensor array measurements indicated that the biofilter outlet air was significantly different to both inlet air of the system and post-humidifier air. Data pre-processing techniques including normalising and outlier handling were applied to improve the odour discrimination performance of the non-specific gas sensor array. To develop an odour quantification model using the sensor array responses of the non-specific sensor array, PCA regression, artificial neural network (ANN) and partial least squares (PLS) modelling techniques were applied. The correlation coefficient (r(2)) values of the PCA, ANN, and PLS models were 0.44, 0.62 and 0.79, respectively.

Odour emissions from tunnel-ventilated broiler sheds: case study of nine Queensland farms.

Odour emission rates were measured from nine tunnel-ventilated broiler farms in south-eastern Queensland, Australia. At one farm, odour emission rates were measured over two sequential batches approximately weekly, while at the remaining farms, odour emission rates were measured just before the first pickup (around Day 35 of the batch) when bird liveweight was greatest and peak odour emission rates were expected. Odour samples were analysed using dynamic olfactometry (to AS/NZS 4323.3:2001), and an artificial olfaction system was used to continuously monitor odour emission rates at one farm. Odour emission rates ranged from 330 to 2960 ou/s per 1000 birds and from 0.19 to 2.12 ou/s.kg, with a significant amount of variability observed throughout the batch and throughout each sampling day. While the wide range in odour emission rates was primarily due to changes in bird liveweight and ventilation requirements, other factors were also involved. The artificial olfaction system proved useful for quantifying the range and variability of odour emission rates, especially when olfactometry analysis was impractical.

Free range chicken farms - odour emissions and nutrient management

Quantification of air emissions, in particular, from free range farms for comparison with conventional farming may demonstrate that free range farming has lower emissions. This finding may support conventional farms that are under pressure due to air quality impacts to more readily convert to free range. Industry will benefit by maintaining/increasing production and the community will benefit from fewer impacts.

Quantifying greenhouse gas emissions from Australian piggeries

Current research proposal will conduct a review of measurement techniques and recommendation for a suite of techniques to be used in method and measurement protocol development.

Feeding to increase productivity and reduce greenhouse gas emissions

Feeding to increase productivity and reduce greenhouse gas emissions.

Kangaroo bacteria to reduce methane emissions and increase productivity

Using kangaroo bacteria to reduce emissions of methane and increase productivity.