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.
|Original language||English (US)|
|Journal||Frontiers in Plant Science|
|State||Published - Jun 10 2022|
Bibliographical noteFunding Information:
We would like to thank Fanglin Xiang and Xinge Li from Nanjing Agricultural University and Lan Zhou from China Agricultural University for their fieldwork and contributions to data collection. We would also like to thank Dr. Syed Tahir Ata-UI-Karim from the University of Tokyo and Jufang Wang from the College of Foreign Studies in Nanjing Agricultural University for their contributions to manuscript revisions and English corrections.
This research was funded by the Jiangsu Province Key Technologies Research and Development Program (BE2021308), the National Natural Science Foundation of China (31601222), the Norwegian Ministry of Foreign Affairs (SINOGRAIN II, CHN-17/0019), the Academic Program Development of Jiangsu Higher Education Institutions (PAPD), and the 111 Project (B16026).
Copyright © 2022 Li, Miao, Zhang, Cammarano, Li, Liu, Tian, Zhu, Cao and Cao.
- environmental variables
- machine learning
- management practices
- nitrogen nutrition index
- precision nitrogen management
- variable selection