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

Yue Li; Yuxin Miao; Jing Zhang; Davide Cammarano; Songyang Li; Xiaojun Liu; Yongchao Tian; Yan Zhu; Weixing Cao; Qiang Cao

doi:10.3389/fpls.2022.890892

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

Front Plant Sci. 2022 Jun 10:13:890892. doi: 10.3389/fpls.2022.890892. eCollection 2022.

Authors

Yue Li¹, Yuxin Miao², Jing Zhang³, Davide Cammarano⁴, Songyang Li⁵, Xiaojun Liu¹, Yongchao Tian¹, Yan Zhu¹, Weixing Cao¹, Qiang Cao¹

Affiliations

¹ MARA Key Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, National Engineering and Technology Center for Information Agriculture, MOE Engineering and Research Center for Smart Agriculture, Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing, China.
² Precision Agriculture Center, Department of Soil, Water and Climate, University of Minnesota, St. Paul, MN, United States.
³ Department of Crop and Soil Sciences, University of Georgia, Tifton, GA, United States.
⁴ Department of Agroecology, Aarhus University, Tjele, Denmark.
⁵ Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, China.

Abstract

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.

Keywords: environmental variables; machine learning; management practices; nitrogen nutrition index; precision nitrogen management; variable selection.