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Data worth and prediction uncertainty for pesticide transport and fate models in Nebraska and Maryland, United States
Author(s) -
Nolan Bernard T,
Malone Robert W,
Doherty John E,
Barbash Jack E,
Ma Liwang,
Shaner Dale L
Publication year - 2015
Publication title -
pest management science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.296
H-Index - 125
eISSN - 1526-4998
pISSN - 1526-498X
DOI - 10.1002/ps.3875
Subject(s) - environmental science , loam , pesticide , calibration , metolachlor , soil water , uncertainty analysis , watershed , hydrology (agriculture) , soil science , statistics , mathematics , computer science , ecology , engineering , biology , atrazine , geotechnical engineering , machine learning
BACKGROUND Complex environmental models are frequently extrapolated to overcome data limitations in space and time, but quantifying data worth to such models is rarely attempted. The authors determined which field observations most informed the parameters of agricultural system models applied to field sites in Nebraska (NE) and Maryland (MD), and identified parameters and observations that most influenced prediction uncertainty. RESULTS The standard error of regression of the calibrated models was about the same at both NE (0.59) and MD (0.58), and overall reductions in prediction uncertainties of metolachlor and metolachlor ethane sulfonic acid concentrations were 98.0 and 98.6% respectively. Observation data groups reduced the prediction uncertainty by 55–90% at NE and by 28–96% at MD. Soil hydraulic parameters were well informed by the observed data at both sites, but pesticide and macropore properties had comparatively larger contributions after model calibration. CONCLUSIONS Although the observed data were sparse, they substantially reduced prediction uncertainty in unsampled regions of pesticide breakthrough curves. Nitrate evidently functioned as a surrogate for soil hydraulic data in well‐drained loam soils conducive to conservative transport of nitrogen. Pesticide properties and macropore parameters could most benefit from improved characterization further to reduce model misfit and prediction uncertainty. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.