Potential of Establishing the Universal Critical Nitrogen Dilution Curve for Japonica Rice

Zhaopeng Fu; Rui Zhang; Jiayi Zhang; Ke Zhang; Qiang Cao; Yongchao Tian; Yan Zhu; Weixing Cao; Xiaojun Liu

doi:10.34133/plantphenomics.0036

Potential of Establishing the Universal Critical Nitrogen Dilution Curve for Japonica Rice

Plant Phenomics. 2023:5:0036. doi: 10.34133/plantphenomics.0036. Epub 2023 Mar 27.

Authors

Zhaopeng Fu¹, Rui Zhang¹, Jiayi Zhang¹, Ke Zhang¹, Qiang Cao¹, Yongchao Tian¹, Yan Zhu¹, Weixing Cao¹, Xiaojun Liu¹

Affiliation

¹ Sanya Institute, National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making (Ministry of Agriculture and Rural Affairs), Engineering Research Center of Smart Agriculture (Ministry of Education), Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China.

Abstract

Establishing the universal critical nitrogen (N_C) dilution curve can assist in crop N diagnosis at the regional scale. This study conducted 10-year N fertilizer experiments in Yangtze River Reaches to establish universal N_C dilution curves for Japonica rice based on simple data-mixing (SDM), random forest algorithm (RFA), and Bayesian hierarchical model (BHM), respectively. Results showed that parameters a and b were affected by the genetic and environmental conditions. Based on RFA, highly related factors of a (plant height, specific leaf area at tillering end, and maximum dry matter weight during vegetative growth period) and b (accumulated growing degree days at tillering end, stem-leaf ratio at tillering end, and maximum leaf area index during vegetative growth period) were successfully applied to establish the universal curve. In addition, representative values (most probable number [MPN]) were selected from posterior distributions obtained by the BHM approach to explore universal parameters a and b. The universal curves established by SDM, RFA, and BHM-MPN were verified to have a strong N diagnostic capacity (N nutrition index validation R ² ≥ 0.81). In summary, compared with the SDM approach, RFA and BHM-MPN can greatly simplify the modeling process (e.g., defining N-limiting or non-N-limiting groups) while maintaining a good accuracy, which are more conducive to the application and promotion at the regional scale.