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ARS Home » Midwest Area » Bowling Green, Kentucky » Food Animal Environmental Systems Research » Research » Publications at this Location » Publication #306906

Title: Applicability of models to predict phosphorus losses in drained fields: a review

item RADCLIFFE, D - University Of Georgia
item REID, D - Agriculture And Agri-Food Canada
item BLOMBACK, K - Swedish University Of Agricultural Sciences
item Bolster, Carl
item Collick, Amy
item EASTON, Z - Virginia Tech
item Francesconi, Wendy
item FUKA, D - Virginia Tech
item JOHNSON, H - Swedish University Of Agricultural Sciences
item King, Kevin
item LARSBO, M - Swedish University Of Agricultural Sciences
item YOUSSEF, MOHAMED - North Carolina State University
item MULKEY, A - University Of Maryland
item NELSON, N - Kansas State University
item PERSSON, K - Swedish University Of Agricultural Sciences
item RAMIREZ-AVILA, J - Mississippi State University
item SCHMIEDER, FRANK - Swedish University Of Agricultural Sciences
item Smith, Douglas

Submitted to: Journal of Environmental Quality
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/10/2014
Publication Date: 3/11/2015
Citation: Radcliffe, D.E., Reid, D.K., Blomback, K., Bolster, C.H., Collick, A.S., Easton, Z.M., Francesconi, W., Fuka, D.R., Johnsson, H., King, K., Larsbo, M., Youssef, M.A., Mulkey, A.S., Nelson, N.O., Persson, K., Ramirez-Avila, J.J., Schmieder, F., Smith, D.R. 2015. Applicability of models to predict phosphorus losses in drained fields: a review. Journal of Environmental Quality. 44(2):614-628.

Interpretive Summary: Artificial drainage, either in the form of surface ditches or sub-surface tile drains, is a prerequisite for efficient crop production in many areas of the world. The effect of artificial drainage on field hydrology depends on the drainage system design and management, soil type, and climatic conditions. Drainage systems can transport the majority of phosphorus from agricultural land, in both dissolved and particulate forms. Tile drains have been identified as being significant contributors to P export from agricultural land worldwide. Despite the abundance of data on P losses through artificial drainage systems, there are questions about how well models designed to predict P losses account for the impact of artificial drainage systems. A number of papers have addressed this lack in a general way, but have not attempted a concise listing of the specific shortcomings of individual models. The objective of this research is to review the current models that address or could address P losses in artificially drained fields and give recommendations for model improvements.

Technical Abstract: Most phosphorus (P) modeling studies of water quality have focused on surface runoff loses. However, a growing number of experimental studies have shown that P loses can occur in drainage water from artificially drained fields. In this review paper, we assess the applicability of nine models to predict this type of P losses. A model of P movement in artificially drained systems will likely need to account for the partitioning of water and P that does not infiltrate into macropore flow and runoff. Within the soil profile, sorption and desorption of dissolved P (DP) and filtering of particulate P (PP) will be important. The models reviewed are ADAPT, APEX, DRAINMOD, HSPF, HYDRUS, ICECREAM, P Indexes, PLEASE, and SWAT. Most of these are field-scale models, but HSPF and SWAT are field- to watershed-scale models. Few of the models are designed to address P loss in drainage waters. For example, although the SWAT model has been used extensively for modeling P loss in runoff and includes tile drain flow, P losses are not simulated in tile drain flow. Two European models, ICECREAMDB from Sweden and PLEASE from The Netherlands, are exceptions in that they are designed specifically for P losses in drainage water. Field experiments using a nested, paired research design are needed to improve P models for artificially drained fields. Regardless of the model used, it is imperative that uncertainty in model predictions be assessed.