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Improving estimates of N and P loads in irrigation water from swine manure lagoons
Authors:M.?R.?McLaughlin  author-information"  >  author-information__contact u-icon-before"  >  mailto:mike.mclaughlin@ars.usda.gov"   title="  mike.mclaughlin@ars.usda.gov"   itemprop="  email"   data-track="  click"   data-track-action="  Email author"   data-track-label="  "  >Email author,J.?P.?Brooks,A.?Adeli,J.?N.?Jenkins
Affiliation:1.Genetics and Sustainable Agriculture Research Unit, United States Department of Agriculture,Agricultural Research Service,Mississippi State,USA
Abstract:The implementation of nutrient management plans for confined animal feeding operations requires recording N and P loads from land-applied manure, including nutrients applied in irrigation water from manure treatment lagoons. By regulation, lagoon irrigation water nutrient records in Mississippi must be based on at least one lagoon water nutrient analysis annually. Research in Mississippi has shown that N and P levels in lagoon water, and the N:P ratio, vary significantly through the year. Nutrient estimates based on one annual analysis do not account for this variability and may overestimate or underestimate N and P loads. The present study reports an improved method to more precisely estimate N and P loads in irrigation water from swine manure lagoons. The method is based on predictable annual cycles of N and P levels in lagoon water and employs simple curve-fitting of lagoon-specific formulas derived by analyses of historical data. Similarity of curves from analyses of Mississippi lagoons and other lagoon studies suggests that the method can be applied using the often limited nutrient data for a lagoon to more precisely estimate seasonal shifts of N and P and to improve the precision of estimates for N and P in irrigation water. Although the present study focused on swine manure lagoons in the southern US, recognition that the annual N cycle in lagoon water is temperature driven, suggests that additional research incorporating temperature into future models could extend these models to other types of waste treatment lagoons and climates.
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