Cost-effective mitigation of greenhouse gas emissions from different dairy systems in the Waikato region of New Zealand.

The New Zealand dairy industry produces approximately 17% of this country's total greenhouse gas emissions (GHG-e) and it is also this nation's largest export industry. The industry needs to reduce GHG-e under proposed policy directives and for ongoing market security. Given these pressures, there is the need to identify cost-effective management strategies to reduce on-farm GHG-e. The objective of this study was to investigate how the management of dairy farms in the Waikato region of New Zealand could change to minimise the abatement costs associated with GHG-e mitigation. Three typical farm systems importing low (less than 10%), medium (10-20%), and high (more than 20%) amounts of supplement are modelled using a non-linear optimisation model. A reduction in nitrogen fertiliser application was the production factor that changed the most to achieve the cap in all of the simulated systems, followed by a reduction in stocking rate. With the prices used in this study, decreasing farming intensity by reducing nitrogen fertiliser by 21-42% and stocking rate by 8-10% represented a cost of $68-$119/ha and a production reduction of 54-117 kg MS/ha for the three systems studied. Improving reproductive performance proved to be effective in reducing GHG-e, allowing for fewer replacement cows to be supported. However, it did not have a significant effect on profit when emissions were unconstrained. Nitrification inhibitors and stand-off pads were not identified as useful mitigation options, given their high cost relative to de-intensification.

An optimization model of a New Zealand dairy farm.

Optimization models are a key tool for the analysis of emerging policies, prices, and technologies within grazing systems. A detailed, nonlinear optimization model of a New Zealand dairy farming system is described. This framework is notable for its inclusion of pasture residual mass, pasture utilization, and intake regulation as key management decisions. Validation of the model shows that the detailed representation of key biophysical relationships in the model provides an enhanced capacity to provide reasonable predictions outside of calibrated scenarios. Moreover, the flexibility of management plans in the model enhances its stability when faced with significant perturbations. In contrast, the inherent rigidity present in a less-detailed linear programming model is shown to limit its capacity to provide reasonable predictions away from the calibrated baseline. A sample application also demonstrates how the model can be used to identify pragmatic strategies to reduce greenhouse gas emissions.