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Improving Estimation of Winter Wheat Nitrogen Status Using Random Forest by Integrating Multi-Source Data Across Different Agro-Ecological Zones

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  • Yue Li, Nanjing Agricultural University
  • ,
  • Yuxin Miao, University of Minnesota
  • ,
  • Jing Zhang, University of Georgia Tifton Campus
  • ,
  • Davide Cammarano
  • Songyang Li, Hong Kong Polytechnic University
  • ,
  • Xiaojun Liu, Nanjing Agricultural University
  • ,
  • Yongchao Tian, Nanjing Agricultural University
  • ,
  • Yan Zhu, Nanjing Agricultural University
  • ,
  • Weixing Cao, Nanjing Agricultural University
  • ,
  • Qiang Cao, Nanjing Agricultural University

Timely and accurate estimation of plant nitrogen (N) status is crucial to the successful implementation of precision N management. It has been a great challenge to non-destructively estimate plant N status across different agro-ecological zones (AZs). The objective of this study was to use random forest regression (RFR) models together with multi-source data to improve the estimation of winter wheat (Triticum aestivum L.) N status across two AZs. Fifteen site-year plot and farmers' field experiments involving different N rates and 19 cultivars were conducted in two AZs from 2015 to 2020. The results indicated that RFR models integrating climatic and management factors with vegetation index (R2 = 0.72–0.86) outperformed the models by only using the vegetation index (R2 = 0.36–0.68) and performed well across AZs. The Pearson correlation coefficient-based variables selection strategy worked well to select 6–7 key variables for developing RFR models that could achieve similar performance as models using full variables. The contributions of climatic and management factors to N status estimation varied with AZs and N status indicators. In higher-latitude areas, climatic factors were more important to N status estimation, especially water-related factors. The addition of climatic factors significantly improved the performance of the RFR models for N nutrition index estimation. Climatic factors were important for the estimation of the aboveground biomass, while management variables were more important to N status estimation in lower-latitude areas. It is concluded that integrating multi-source data using RFR models can significantly improve the estimation of winter wheat N status indicators across AZs compared to models only using one vegetation index. However, more studies are needed to develop unmanned aerial vehicles and satellite remote sensing-based machine learning models incorporating multi-source data for more efficient monitoring of crop N status under more diverse soil, climatic, and management conditions across large regions.

OriginalsprogEngelsk
Artikelnummer890892
TidsskriftFrontiers in Plant Science
Vol/bind13
Antal sider16
ISSN1664-462X
DOI
StatusUdgivet - jun. 2022

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