Tian Yongchao

Monitoring leaf nitrogen accumulation in wheat with hyper-spectral remote sensing

Abstract Crop nitrogen status is a key indicator for evaluating crop growth, increasing yield and improving grain quality. Non-destructive and rapid assessment of leaf nitrogen is required for improving nitrogen management in wheat production. This study aims at identification of the quantitative relationship between leaf nitrogen accumulation and canopy reflectance spectra in winter wheat (Triticum aestivum L.), and to derive regression equations to monitor N nutrition status in wheat. 3 field experiments were conducted with different N application rates and wheat cultivars across 3 growing seasons, and time-course measurements were taken on canopy spectral reflectance, leaf N content and leaf dry weights under various treatments. In these studies, leaf nitrogen accumulation in wheat increased with increasing nitrogen rates. Canopy reflectance changed with increasing leaf nitrogen accumulation. Sensitivity bands mainly occurred in near infrared and visible light, and strong correlation existed between red light and leaf nitrogen accumulation. The relationships of 8 vegetation indicators and leaf nitrogen accumulation were analyzed using statistical models. Hyper-spectral variables were significantly correlated with leaf nitrogen accumulation, and the relationships between the leaf nitrogen accumulation and SDr/SDb, FD742 and AVHRR-GVI were all highly significant with determination of coefficients (R2) of 0.9163, 0.9097 and 0.9142, respectively, and standard errors (SE) of 1.165, 1.079 and 1.077, respectively. Tests with another independent dataset showed that FD742 and REPIG could be well used to predict leaf nitrogen accumulation in wheat with R2 of 0.8449 and 0.8394, and root mean square error (RMSE) of 0.984 and 1.014, respectively. This suggests that FD742 and REPIG can be used to estimate leaf nitrogen accumulation, of which FD742 performed better in modeling and testing.

Development of a growth model-based decision support system for crop management

A growth model-based decision support system for crop management (GMDSSCM) was developed, which integrates process-based models of four different crops—wheat, rice, rape and cotton—and realized decision support function, thus facilitating the simulation and application of the crop models for different purposes. The individual models include six sub models for simulating phase development, organ formation, biomass production, yield and quality formation, soil-crop water relations and nutrient (N, P, K) balance. The implemented system can be used for evaluating individual and comprehensive management strategies based on the results of crop growth simulation under various environments and different genotypes. A stand-alone edition (GMDSSCMA) was established on VC++ and VB platforms by adopting the characteristics of object-oriented and component-based software and with the effective integration and coupling of the growth prediction and decision-making functions. A web-based system (GMDSSCMW) was then further developed on the.net platform using C# language. These GMDSSCM systems have realized dynamic prediction of crop growth and decision making on cultural management, and thus should be helpful for the construction and application of informational and digital farming system.