How much will precision nitrogen management pay off? An evaluation based on simulating thousands of corn fields over the US Corn-Belt

Zhenong Jin, Sotirios V. Archontoulis, David B. Lobell

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


Variable rate technology nitrogen (VRT-N) application, a precision management approach to synchronize N input with crop demand, has received increasing attention in recent years. Although existing field trials have suggested the agronomic and environmental feasibility of VRT-N adoption, the potential benefits at large scale remain a puzzle to farmers and policy-makers, partly because no tools are available for aggregated assessments. In this study, we develop a scalable framework to support large-scale precision N management by fully leveraging recent advances in crop modeling, remote sensing, and publicly available weather and soil datasets. Using this new framework, we investigated the agronomic and environmental benefits of VRT-N application for corn compared to Uniform-N application across 9 states and ca. 2600 fields in the US Corn-Belt. Our results showed that, given an Extension-reported service fee of 6.2 USD$/ha for applying N at variable rate, the multi-year averaged mean agronomic margin of adopting VRT-N were only 6.7 $/ha more than Uniform-N. Accounting for the environmental cost of N leaching and N2O emission increased the mean benefit of VRT-N to 26.4 $/ha. Agronomic benefits in general increased with the yield heterogeneity in field, and the mean value was driven by a small fraction of very heterogenous fields. Additional benefits from managing the temporal variability were 29.5 $/ha and 51.6 $/ha on average from an agronomic and environmental perspective, respectively. Environmental benefits are considerable but are unlikely to drive farmer adoption in the absence of new incentives for precision technologies. Although various uncertainties remain to be reduced, the current framework opens opportunity to ingest future improvements in models and geospatial datasets and could serve as a foundation for other precision management practices such as seeding rate and irrigation.

Original languageEnglish (US)
Pages (from-to)12-22
Number of pages11
JournalField Crops Research
StatePublished - Jul 1 2019


  • Corn
  • N management
  • Precision agriculture
  • SCYM
  • VRT


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