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ARS Home » Northeast Area » University Park, Pennsylvania » Pasture Systems & Watershed Management Research » Research » Publications at this Location » Publication #264024
Title: Development of a forecasting tool to guide field management decisions related to fertilizer and manure

Author
item Buda, Anthony
item Kleinman, Peter
item Feyereisen, Gary
item Bryant, Ray
item KNIGHT, PAUL - Pennsylvania State University
item MILLER, DOUGLAS - Pennsylvania State University

Submitted to: Soil and Water Conservation Society Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 2/11/2011
Publication Date: 7/18/2011
Citation: Buda, A.R., Kleinman, P.J., Feyereisen, G.W., Bryant, R.B., Knight, P.G., Miller, D. 2011. Development of a forecasting tool to guide field management decisions related to fertilizer and manure. Soil and Water Conservation Society Proceedings. Paper No. 39428984.

Interpretive Summary: An interpretive summary is not required.

Technical Abstract: Managing the timing of fertilizer and manure application is critical to protecting water quality in the Chesapeake Bay watershed. While modern nutrient management tools are designed to help farmers with their long-term field management planning, they do not support daily decisions on when and where to apply fertilizers and manures. Applying fertilizers and manures at the wrong time (e.g., immediately preceding a rainfall event that produces surface runoff from a field) unnecessarily increases the risk of surface water contamination. We are developing a tool that utilizes daily weather forecast data (rainfall amount, rainfall intensity, soil moisture) to predict the probability of surface runoff over 24, 48 and 120 hour periods and categorize the risk using “stop light” maps (red = high risk, yellow = moderate risk, green = low risk). In support of tool development, a proof-of-concept study is being conducted using independent datasets on daily surface runoff occurrence from two adjacent experimental watersheds in the Ridge and Valley Physiographic region of east-central Pennsylvania. Simple logistic regression models that relate daily runoff occurrence (1 = yes, 0 = no) to weather forecast variables in the test watershed are used to predict the probability of surface runoff occurrence in the validation watershed. The results of these predictions are evaluated in terms of accuracy and forecast skill. This effort will be expanded to include other field management factors and physiographic regions in Pennsylvania to facilitate the construction of a web-based runoff forecasting tool.