The Economics of Agricultural Non-Point Source Pollution Control: Investigating Synergies Between Spatial Targeting and Precision Agriculture

This thesis investigates the cost-effectiveness of agricultural non-point source pollution control policies through a biophysical-economic model for the Eden catchment in North-West England. Firstly, the presented thesis extensively reviews agri-environmental policy in the UK and the economic literature on non-point source pollution control. Moreover, in the context of current agricultural reforms in the UK and recent technological progress in agricultural technology, policy recommendations are drawn from a purpose-built biophysical-economic model covering six key non-point source pollutants (nitrogen and phosphorus to both the river and groundwater, sediment, and carbon emissions). The model is implemented in GAMS and characterised by a novel level of biophysical detail in the literature, including six farm types, six livestock types, 10 hydrological connectivity levels, five soil types, four slope types, 45 years of observed weather data, and 25 crops selected from 24 crop rotations. Policies are assessed over a range of abatement ambitions to facilitate evidence for different policymaker objectives. Overall, incentive-based fertiliser input taxes are found to be the most cost-effective policy mechanism in the Eden catchment. Notably, the presented results confirm previous findings in the literature of inelastic fertiliser demand. Consequently, high levels of taxation are required to achieve non-point source pollution abatement. Further, the novel assessment of Precision Agriculture in the context of a detailed catchment-scale biophysical-economic model highlights the necessary preconditions for precision agriculture to be cost-effectively implemented. Modelling of spatially targeted policies moreover highlights the synergies between spatial targeting and precision agriculture in this respect. Policymakers should ensure sufficient heterogeneity in biophysical variables (soil-types, slope-types, and hydrological connectivity levels) to safeguard successful applications of both spatial targeting and precision agriculture