Neural network model predictions for phosphorus management strategies on tile-drained organic soils

The organic soils of Holland Marsh, Ontario are used for intensive vegetable production, which demands high-phosphorus (P) fertilizer applications. Such high-fertilizer applications on these tile-drained lands lead to eutrophication in surrounding water bodies. This study investigated the application of neural network (NN) models for deriving P management strategies. Seven NN models were assessed using the following two approaches: a time series with 1-year training and 1-year testing of the models and a randomization analysis where a random 80% of data were used for model training and the remainder for model testing. The feed-forward model using the randomization and the long-short-term memory model using time-series outperformed all other models. Two strategies for P management were evaluated: a direct approach that predicts P loads using new fertilizer rates or controlled drainage discharge rates and a particle swarm optimization (PSO) that used percent reduction of actual P loads to predict an optimal water table management strategy. Overall, the direct approach identified a water table level of 30 cm from the soil surface during the spring and 80 cm during the summer period as optimal to reduce P loads. The PSO analysis showed that a reduction of P loads by 20% in the spring and up to 40% in the summer through water table control would not compromise crop production.

Automated summary

This study investigates the application of neural network models for deriving phosphorus (P) management strategies in the organic soils of Holland Marsh. The results show that the feed-forward model using randomization and the long-short-term memory model using time-series perform better than other models. Two P management strategies were evaluated: a direct approach predicting P loads through fertilizer rates or controlled drainage discharge rates, and a particle swarm optimization (PSO) method predicting optimal water table management strategy based on percent reduction of actual P loads. The direct approach suggests that maintaining a water table level of 30 cm in spring and 80 cm in summer can effectively reduce P loads. The PSO analysis indicates that reducing P loads by 20% in spring and up to 40% in summer through water table control will not compromise crop production.