Page Banner

United States Department of Agriculture

Agricultural Research Service

Title: Development of Nitrogen-Release Algorithm for Slow Release Fertilizers

Authors
item King, Kevin
item Balogh, J - SPECTRUM RESEARCH INC

Submitted to: Transactions of the ASAE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 31, 2000
Publication Date: N/A

Interpretive Summary: The use of fertilizer is commonplace for anyone who owns or maintains a green space or agricultural plot of land. Fertilizers are generally one of two forms: fast release or slow release types. In order to predict the impact of using fertilizers, a simulation tool or computer model is often used. Those models lack the ability to simulate a slow release fertilizer. The efforts laid forth in this manuscript show the development and validation of a slow release fertilizer method that can be adapted and incorporated into the current suite of water quality models. This method will allow the initial simulation of slow release fertilizers.

Technical Abstract: Current water quality models do not consider the time release rate of sulfur coated ureas (SCUs). However, the use of these slow release formulations is prevalent in urban agricultural management. Using documented slow release fertilizer data, a first order decay equation was fit with reasonable accuracy for both surface (Efficiency R**2 = 0.63) and incorporated (Efficiency R**2 = 0.70) applications. In both cases the deca coefficient was best represented using a 2-parameter model. Temperature and 7-day dissolution amount were determined as best descriptive parameters for the surface model while soil moisture and temperature were used for the incorporated model. Temperature was the more sensitive parameter for the surface applied model while soil moisture was the more sensitive input for the incorporated model. Each model was validated with a limited amount of data. The surface applied model was validated with a prediction efficiency of 0.82 while the subsurface model was validated with a prediction efficiency of 0.63. Even though the models are based on a limited amount of data, incorporation of these results in water quality models will permit the initial simulation of SCUs and allow better simulations of real world situations.

Last Modified: 10/1/2014
Footer Content Back to Top of Page