Agricultural nutrient pollution is an ongoing challenge affecting global water quality. When not properly managed, farming operations can contribute to nutrient pollution, leading to harmful algal blooms and dangerous water quality according to the EPA. Addressing this issue calls for large-scale implementation of cost-effective agricultural pollution control strategies, such as reducing fertilizer use or creating buffer zones around water bodies. Developing these strategies requires cost data for various pollution control measures, but the availability of this data is limited due to a lack of sufficient research studies in many parts of the world.
WRI’s recent working paper, Achieving Abundance:Understanding the Cost of a Sustainable Water Future, estimated the cost of agricultural pollution control at the country scale by using available cost data. However, the approach adopted in this study doesn’t account for the impacts of important regional factors such as geographic characteristics on pollution control cost. First, a global average cost value was calculated from available data. Country-level costs were estimated from this value by applying the purchasing power of different currencies. Purchasing power parity is an economic indicator that compares the cost of same amount of goods in different currencies.
A Global Model for Agricultural Pollution Control Cost
WRI is fundraising to apply a methodology that builds on the approach adopted in Achieving Abundance by:
Improving the quantity and quality of data considered for cost estimation. WRI has reviewed the literature on the cost of pollution control, evaluating the research based on their coverage of solutions for multiple land types. At this stage, the Chesapeake Assessment Scenario Tool (CAST) has been identified as a highly comprehensive data source, providing cost data for about 250 nutrient pollution control methods for three pollutants — nitrogen, phosphorus and suspended solids — covering seven states in the United States. While CAST has information on a wide variety of solutions, WRI is researching the limitations of applying data from one geography around the globe. Some limitations already identified include the exclusion of solutions for agricultural practices nonexistent around the Chesapeake Bay region, such as best practices specific to rice and coffee production. There are financial limitations to some methods, particularly for regions experiencing poverty.
Improving the geospatial component by:
Modelling the pollution control method applicable to each land use type. The research will model the application of pollution control methods based on global land use maps. Each pollution control method from the CAST dataset has been assigned applicable land use types. For example, fertilizer application-related pollution control methods are only suitable on cropland. This research will calculate the extent to which pollution control methods can be applied based on the current land use.
Assigning the degree of mechanization required for each pollution control method. Pollution control methods which require a high degree of agricultural mechanization (like techniques involving mechanized manure application to soil) may not be suitable for low-income countries. Therefore, such methods have not been considered for low-income countries.
Developing a prioritization process that provides the order in which control methods can be applied on given land type until desired nutrient removal is achieved. Prioritization is required because more than one pollution control method can be suitable for a given land type. WRI is working to optimize practices to achieve nutrient reduction targets. Assuming that the most cost-effective pollution control method would be applied first, the methods have been prioritized from highest to lowest order of cost-effectiveness ($/lb. nutrient removed).
Calculating country-level cost requires desired nutrient removal (“nutrient reduction”) for each country along with the steps mentioned above. WRI has partnered with Utrecht University to develop an updated dataset on nutrient reduction goals for achieving good water quality globally. Adapting an existing model to generate a better dataset with finer spatial resolution on nutrient loads can be used to calculate the index of coastal eutrophication potential (ICEP). The final step in the methodology is applying the purchasing power parity conversion, which adds another geospatial component to cost. It is important to note that due to lack of sufficient data, this methodology doesn’t account for the impacts of other parameters like soil characteristics or climate on removal efficiencies and the resulting pollution control cost.
Applications of the Model
A global model developed using the methodology described here, along with nutrient removal data, will aid public and private sector decision-makers in estimating the financial resources required for pollution control. Potential applications of this methodology include:
Target-setting and budgets. Providing a better understanding of the current state of nutrient overloading and the costs required to eliminate the overload can help companies and governments develop water quality targets complete with budget estimates.
Cost scenarios. This methodology can be used to estimate and compare pollution control costs for different land types. For example, this methodology could be used to estimate the cost of applying pollution prevention measures on cropland and compare it with the cost of pollution prevention measures on waterfront land.
Our next step is to combine the Achieving Abundance methodology with nutrient removal data, and then generate a global model for agricultural pollution control cost using appropriate global land cover datasets. This model will be a useful resource for financial analysis of nutrient pollution control strategies for companies and governments. Subscribe to WRI Water’s Newsletter for updates.