Understanding differences between static and dynamic nitrogen fertilizer tools using simulation modeling

German Mandrini, Cameron M. Pittelkow, Sotirios V. Archontoulis, Taro Mieno and Nicolas F. Martin

Agricultural Systems, 2021, vol. 194, issue C

Abstract: Improving nitrogen (N) fertilizer recommendations for maize (Zea mays L.) in the US Midwest has been the focus of much research, yet there is no agreement for which methodology is the best to balance trade-offs between production and environmental outcomes. This study investigated the strengths and limitations of two broad approaches: dynamic and static recommendation tools. Dynamic tools use advanced technology to predict the Economically Optimum N Rate (EONR) using year-specific soil, weather, and crop growth characteristics to detect conditions that need lower or higher N rates. Static tools provide regional N recommendations that are static over time, maximizing long-term profits rather than predicting the best EONR for each field and season.

Keywords: Crop modeling; Machine learning; Environmental indicators; Economic analysis; Maize; Nitrogen fertilizer (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:agisys:v:194:y:2021:i:c:s0308521x21002286

DOI: 10.1016/j.agsy.2021.103275

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