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.

A model of macadamia with application to pruning in orchards

A self-organising model of macadamia, expressed using L-Systems, was used to explore aspects of canopy management. A small set of parameters control the basic architecture of the model, with a high degree of self-organisation occurring to determine the fate and growth of buds. Light was sensed at the leaf level and used to represent vigour and accumulated basipetally. Buds also sensed light so as to provide demand in the subsequent redistribution of the vigour. Empirical relationships were derived from a set of 24 completely digitised trees after conversion to multiscale tree graphs (MTG) and analysis with the OpenAlea software library. The ability to write MTG files was embedded within the model so that various tree statistics could be exported for each run of the model. To explore the parameter space a series of runs was completed using a high-throughput computing platform. When combined with MTG generation and analysis with OpenAlea it provided a convenient way in which thousands of simulations could be explored. We allowed the model trees to develop using self-organisation and simulated cultural practices such as hedging, topping, removal of the leader and limb removal within a small representation of an orchard. The model provides insight into the impact of these practices on potential for growth and the light distribution within the canopy and to the orchard floor by coupling the model with a path-tracing program to simulate the light environment. The lessons learnt from this will be applied to other evergreen, tropical fruit and nut trees.