Using artificial neural networks to improve phosphorus indices

The phosphorus index (PI) was developed as a field-scale assessment tool used to identify critical source areas of phosphorus (P) loss, thus most US states have adopted the PI as their strategy for targeted management and conservation practices for effective mitigation of P loss front agricultural landscapes to surface waters. Recent studies have focused on evaluating and updating PI weighting factors (WFs) to ensure agreement between PI values and measured losses of P. Given that the WF of each site characteristic is usually determined individually without considering possible interactions, the goal of this study was to demonstrate how artificial neural networks (ANNs) that consider real-world interdependence can be used to determine WFs. Our specific objectives were to evaluate ANN performance for predicting soluble P (SP) concentrations in tile effluent using site characteristics as predictor variables, and to evaluate whether ANN-generated WFs can be used to improve PI performance. Garson's algorithm was used to determine the relative importance of each site characteristic to SP loss. Data from a monitored in-field laboratory were used to evaluate the ability of a PI with no WFs (PINO), a PI with WFs as proposed in the original Lemunyon and Gilbert PI (PILG), and a PI with ANN-generated WFs (MANN), to estimate SP loss potential in tile discharge. Simulation results showed that the ANN model provided reliable estimates of SP in tile effluent (R-2 = 0.99; RMSE = 0.0024).The relative importance analysis underscored the value of routine soil P testing for agronomic sufficiency for environmental stewardship, and highlighted the necessity of prioritizing both contemporary and legacy P sources during P loss risk assessments. Unlike the other PIs, PIANN was able to provide reasonable estimates of SP loss potential as illustrated with significant exponential relationships (R-2 = 0.60; p < 0.001) between PIANN values and measured mean annual SP concentrations in tile effluent. These findings demonstrate that ANNs can be used to develop PIs with a strong correlation to measured SP.