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Effect of feed wastage on piggery manure characteristics and methane potential

51 Pages

Author(s): Stephan Tait, Shao Dong Yap, Alan Skerman, Sara Willis

Feed costs represent 65% of total pork production costs in Australia. In addition to the direct financial implications, feed wastage also influences the characteristics of the effluent discharged from intensive piggery sheds. Reliable estimates of effluent characteristics are essential for effectively designing and operating piggery waste management systems to maximise the economic value of the nutrients and energy potential of the effluent, without causing adverse impacts on the environment or community amenity.  Although it is widely recognised that feed wastage is an important issue that can have a major influence on the profitability of pork production, there are currently no simple or practical methods available for directly measuring feed wastage in piggeries.

To assist in developing improved predictive tools to assess and manage feed wastage at piggeries, the objectives of this project were to quantify the effects of different rates of feed wastage on piggery effluent characteristics and potential methane yields, and to validate the method for estimating piggery feed wastage incorporated in the PigBal 4 model.

As anticipated, the analysis results confirmed that increasing levels of feed wastage resulted in incrementally increasing concentrations of Total Solids (TS), Volatile Solids (VS), Chemical Oxygen Demand (COD), most nutrients and Volatile Fatty Acids (VFAs) in the four treatment samples.  However, the measured increases in TS and VS were lower than the values predicted by McGahan et al. (2010) based on previous PigBal modelling. This study indicated that the current version of the PigBal 4 model did not accurately predict the feed wastage levels suggested by AUSPIG modelling, primarily because the standard live weight gain and feed ingestion curves incorporated in PigBal 4 resulted in higher feed consumption estimates in comparison to the AUSPIG predictions.

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