Unraveling uncertainty drivers of the maize yield response to nitrogen: A Bayesian and machine learning approach

Adrian A. Correndo, Nicolas Tremblay, Jeffrey A. Coulter, Dorivar Ruiz-Diaz, David Franzen, Emerson Nafziger, Vara Prasad, Luiz H.Moro Rosso, Kurt Steinke, Juan Du, Carlos D. Messina, Ignacio A. Ciampitti

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3 Scopus citations

Abstract

Development of predictive algorithms accounting for uncertainty in processes underpinning the maize (Zea Mays L.) yield response to nitrogen (N) are needed in order to provide new N fertilization guidelines. The aims of this study were to unravel the relative importance of crop management, soil, and weather factors on both the estimate and the size of uncertainty (as a risk magnitude assessment) of the main components of the maize yield response to N: i) yield without N fertilizer (B0); ii) yield at economic optimum N rate (YEONR); iii) EONR; and iv) the N fertilizer efficiency (NFE) at the EONR. Combining Bayesian statistics to fit the N response curves and a machine learning algorithm (extreme gradient boosting) to assess features importance on the predictability of the process, we analyzed data of 730 response curves from 481 site-years (4297 observations) in maize N rate fertilization studies conducted between 1999 and 2020 in the United States and Canada. The EONR was the most difficult attribute to predict, with an average uncertainty of 50 kg N ha−1, increasing towards low (<100 kg N ha−1) and high (>200 kg N ha−1) EONR expected values. Crop management factors such as previous crop and irrigation contributed substantially (∼50%) to the estimation of B0, but minorly to other components of the maize yield response to N. Weather contributed about two-thirds of explained variance of the estimated values of YEONR, EONR, and NFE. Additionally, weather factors governed the uncertainty (72% to 81%) of all components of the N response process. Soil factors provided a consistent but limited (10% to 23%) contribution to explain both expected N response as well as its associated uncertainties. Efforts to improve N decision support tools should consider the uncertainty of models as a type of risk, potential in-season weather scenarios, and develop probabilistic frameworks for improving this data-driven decision-making process of N fertilization in maize crop.

Original languageEnglish (US)
Article number108668
JournalAgricultural and Forest Meteorology
Volume311
DOIs
StatePublished - Dec 15 2021

Bibliographical note

Publisher Copyright:
© 2021 Elsevier B.V.

Keywords

  • Bayesian models
  • Fertilizer N response
  • Maize
  • Response curves
  • Uncertainty

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