Agricultural data source choice as a dominant control on nitrogen surplus uncertainty

Abstract Agricultural nitrogen (N) surplus (the residual of total N inputs after harvest removal, plant-tissue storage, runoff and atmospheric losses) is a leading cause of groundwater and other environmental contamination in intensive food-producing regions, yet N surplus estimates often depend on inconsistent fertilizer, harvest and manure datasets in an N balance. Using California’s Central Valley as a data-rich test case, we propagate expert, survey-based, regulatory and county-level datasets through a fixed crop-specific N surplus framework to generate 24 cases. Holding model structure and land use constant, we quantify the large divergence in estimated N surplus arising from data choice, especially for manure-treated field crops. The uncertainty about N surplus is consequential: it can reorder which crops, regions and pathways receive priority for groundwater protection. The resulting spread among N surplus outcomes is large enough to alter hotspot rankings, mitigation priorities and screening-level estimates of economic burden. Credible nitrogen assessment at farm to global scales therefore requires explicit propagation of agricultural data source uncertainty, which can materially affect resulting N surplus estimates. Absent specific local data, the uncertainty magnitudes reported here may guide N surplus uncertainty assessment in other regional, national and global analyses.