Improving Estimation of Winter Wheat Nitrogen Status Using Random Forest by Integrating Multi-Source Data Across Different Agro-Ecological Zones - Li, Yue; Miao, Yuxin; Zhang, Jing; Cammarano, Davide; Li, Songyang; Liu, Xiaojun; Tian, Yongchao; Zhu, Yan; Cao, Weixing; Cao, Qiang (Frontiers in Plant Science, 2022)
Timely and accurate estimation of plant nitrogen (N) status is crucially important 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 estimation of winter wheat (Triticum aestivum L.) N status across two AZs. Fifteen site-year plot and farmer 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 only using 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 indictors. In higher latitude area, 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 model for N nutrition index estimation. Climatic factors were important for the estimation of aboveground biomass, while management variables were more important to N status estimation in lower latitude area. It is concluded that integrating multi-source data using RFR models can significantly improve the estimation of winter wheat N status indicators across AZs than models using only one vegetation index. More studies are needed to develop unmanned aerial vehicle 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.