An optimized strategy of nitrogen-split application based on the leaf positional differences in chlorophyll meter readings

Modern rice production faces the dual challenges of increasing grain yields while reducing inputs of chemical fertilizer.However,the disequilibrium between the nitrogen(N)supplement from the soil and the demand for N of plants is a serious obstacle to achieving these goals.Plant-based diagnosis can help farmers make better choices regarding the timing and amount of topdressing N fertilizer.Our objective was to evaluate a non-destructive assessment of rice N demands based on the relative SPAD value(RSPAD)due to leaf positional differences.In this study,two field experiments were conducted,including a field experiment of different N rates(Exp.I)and an experiment to evaluate the new strategy of nitrogen-split application based on RSPAD(Exp.II).The results showed that higher N inputs significantly increased grain yield in modern high yielding super rice,but at the expense of lower nitrogen use efficiency(NUE).The N nutrition index(NNI)can adequately differentiate situations of excessive,optimal,and insufficient N nutrition in rice,and the optimal N rate for modern high yielding rice is higher than conventional cultivars.The RSPAD is calculated as the SPAD value of the top fully expanded leaf vs.the value of the third leaf,which takes into account the non-uniform N distribution within a canopy.The RSPAD can be used as an indicator for higher yield and NUE,and guide better management of N fertilizer application.Furthermore,we developed a new strategy of nitrogen-split application based on RSPAD,in which the N rate was reduced by 18.7%,yield was increased by 1.7%,and the agronomic N use efficiency was increased by 27.8%,when compared with standard farmers'practices.This strategy of N fertilization shows great potential for ensuring high yielding and improving NUE at lower N inputs.

Image Processing Techniques in Shockwave Detection and Modeling

Shockwave detection is critical in analyzing shockwave structure and location. High speed video imaging systems are commonly used to obtain image frames during shockwave control experiments. Image edge detection algorithms become natural choices in detecting shockwaves. In this paper, a computer software system designed for shockwave detection is introduced. Different image edge detection algorithms, including Roberts, Prewitt, Sobel, Canny, and Laplacian of Gaussian, are implemented and can be chosen by the users to easily and accurately detect the shockwaves. Experimental results show that the system meets the design requirements and can accurately detect shockwave for further analysis and applications.