Phosphorus Risk Assessment In Agricultural Fields Of The Mississippi Delta.
Poster Number 1306
Tuesday, November 5, 2013
Tampa Convention Center, East Hall, Third Floor
John J. Ramirez-Avila1, J. Larry Oldham2, Miguel Oliveras-Berrocales3, James L. Martin4 and Sandra L. Ortega-Achury4, (1)Geosystems Research Institute, Mississippi State University, Mississippi State, MS (2)Plant and Soil Sciences, Mississippi State University, Mississippi State, MS (3)Crops and Agroenvironmental Sciences, University of Puerto Rico, Mayaguez, Mayaguez, PR (4)Civil and Environmental Engineering, Mississippi State University, Mississippi State, MS
Nutrient and sediment runoff from agricultural lands is a critical problem associated with the Mississippi River Basin. Suitable practices and better nutrient management plans are needed to avoid nutrient loss, or to control or trap sediment and nutrients before they leave the field to eventually contribute to a low-oxygen environment in streams or subsequently in the Gulf of Mexico. The recently revised NRCS 590 nutrient management standard supports emerging technologies, and emphasizes nutrient risk assessments that minimize agricultural nonpoint source pollution of surface and groundwater. Evaluation and selection of appropriate applicable analytical tools are necessary for designing, sitting and assessing the potential reductions from multiple management practices implemented within the Mississippi Delta region. The performance of the NTT and the ArcAPEX models was evaluated using an 11.3 ha agricultural field located in the Mississippi Delta region by comparing predicted results with monitoring information for monthly and annual runoff depths, sediment and phosphorus loads and crop productivity under reduced tillage practices. The phosphorus risk assessment for the observed and simulated scenarios in the evaluated fields was determined using the current version of the MS P-Index to compare the accuracy of the models in predicting the vulnerability of the field to export phosphorus. The results provide valuable insight to recommend potential NTT model improvements to enhance the accuracy and performance of this tool.