Nitrogen fixation characteristics of root nodules in different peanut varieties and their relationship with yield.

Root nodules in peanut (Arachis hypogaea L.) could directly utilize nitrogen (N) in the atmosphere as N source, which plays an important role in the N supply in peanut. However, little is known about the mechanism of efficient N fixation by root nodule. In this study, 15N tracer technology was used to investigate the characteristics of N fixation by root nodule and its relationship with peanut yields of 19 varieties in a pot culture experiment. Results showed that there were significant differences in nodule number, fresh quality, internal material, N fixation amount and other related indices among different varieties. The range of number and fresh mass of root nodule was 170.59-696.15 per pot and 0.83-3.74 g per pot, with coefficients of variation (CV) of 36.1% and 41.1%, respectively. The range of leghaemoglobin content and nitrogenase activity was 15.51-23.23 mg per pot and 2.75-20.46 µmol C2H4·h-1 per pot, with CV of 13.1% and 57.2%, respectively. The CV of nitrogenase activity was significantly higher than that of leghaemoglobin content, indicating that nitrogenase activity was not only affected by leghaemoglobin content but also other factors. The range of N fixation by root nodule and total N accumulation was 0.71-1.82 and 2.16-3.72 g per pot, with CV of 21.6% and 12.9%, respectively. The CV of the former was significantly higher than that of the latter, indicating that other N sources could automatically compensate the deficit when N fixation of root nodule was insufficient. Nitrogen fixation by root nodule was one of the main N sources for peanut, while the average N supply percentage was more than 40% of the total N, with a maximum of 50%. Cultivating the variety with high N supply capacity is an effective way for N-saving cultivation in the peanut production. Except for nodule number, there were significantly positive correlations between the other indices and peanut yield, indicating that the physiological indicators of N fixation were closely related to N fixation capacity by root nodule and the final yield. Therefore, enhancing these characters would help achieve high yield of peanut and simultaneously reduce fertilizer application.

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A total of twenty genotypes of peanut (Arachis hypogaea) were used to investigate the differences in nitrogen (N) utilization characteristics in a pot experiment with 15N isotope tracing analysis. Results showed the main N sources for peanut in high fertility soils following as soil N source > N fixed by root nodule source > fertilizer N source. The N uptake and accumulation in peanut from total N and the three N supplied sources (fertilizer N, soil N and N fixation) varied among the different genotypes. N fixation source had the largest genetic variation among the twenty genotypes while genetic variation for fertilizer N source and soil N source were lower and similar. Significant differences showed among the twenty genotypes in pod producing efficiency of N and N use efficiency, and the highest values were respectively 3.6 and 2.1 times of the lowest values. There were also significant differences among the twenty genotypes in the harvest indexes of total N, fertilizer N source, soil N source and N fixation source, and the largest variation showed in the harvest index of N fixation source. The pod yields significantly or extremely significantly corrected with N accumulation amounts from different N sources, N harvest index, pod producing efficiency of N, and N use efficiency. According to N uptake and accumulation and pod yield, four major types of peanut were classified, namely high total N accumulation high yield type, high fertilizer N source high yield type, high soil N source high yield type, and high N fixation source high yield type. Four of the twenty genotypes had all characteristics of the four major types.

[The PLS analysis with short wave near-infrared of whole wheat for protein].

Short wave near-infrared spectrum of whole wheat was obtained by diffusion reflection. PLS method was used to analyze protein content of whole wheat. Different wavelength ranges were chosen for regression and information abstraction. The 3D curves were shown for different factors, prediction residual sum of squares (PRESS), and RMSECV. The best wavelength ranges and factors were determined. Analysis results for protein content of whole wheat were shown in three wavelength regions, and the predicted results were compared and discussed. The method of selecting advantageous wavelength ranges is feasible to obtain high prediction precision.

[Influence of smooth, 1st derivative and baseline correction on the near-infrared spectrum analysis with PLS].

This paper studied the influence of using pre-procession such as smooth, 1st derivative and baseline correction on the analysis of near-infrared spectrum. Comparing the analysis results by the pre-procession methods, and using PLS arithmetic, the best pre-procession was determined. In smooth pre-procession method, the best smooth points were proposed for regression using PLS. The analysis result is satisfactory.

[Research on model and wavelength selection of near infrared spectral information].

Based on stepwise linear regression, and according to the theory of near infrared absorbption, spectrum (1000-2500 nm) obtained by detector was divided into three ranges, which were I (1000-1400 nm) and II (1400-1860 nm) and III (1860-2500 nm). In each range the regression wavelengths of different wavelength gaps were picked up stepwise. Regression coefficients and parameters were calculated by Matlab5.3 Program. Regression models were built up in different ranges with different wavelength gaps. Best models could be determined. Prediction results of protein content of ground wheat were displayed in scatter plots. Different results were discussed and compared, which has referencemeaning for application.

[The effect of adjusting the ANN's parameters on the identifying of biology spectrum].

A method to identify the visible spectrum of micro areas on the biological surface with artificial neural net (BP-ANN) was introduced in this paper. The visible spectra (from 500 nm to 730 nm) of the micro areas with some rotten or scars on the surface of the apples were measured with fiber sensor spectrometer. A kind of ANN with a single hidden layer was created to identify the characters on the surface automatically. The effects of different ranges of output, different training functions, different number of single hidden layers, and different noise levels on the ANN were also studied.

[CCD spectrum processing of automobile lamp testing with wavelet analysis].

According to the wavelet multi-resolution analysis, the optical spectrum signal received by CCD was processed and analyzed by using the wavelet transformation technique. The noise in the optical spectrum signal can be removed by using the wavelet multi-resolution analysis technique. The curve of spectral signal can be smoothed. The ratio of signal to noise was enhanced. The spectrum of Asource by CCD was smoothed with wavelet multi-resolution analysis. The processing results with different wavelets in different orders were discussed. An effective data processing method was used for spectrum analysis. The difficulty in the analysis and processing of real-time signal can be solved, which is significant in the color testing field.

[Study on the method of skin's spectral analysis and diagnosis].

A kind of systematic structure to test skin color with optic-fiber probe is introduced. The system can be used to measure skin's reflecting spectrum by using a grating instrument system and a linear CCD sensor. According to the color theory and skin's reflecting spectrum, the formulae for calculating skin's color tri-stimulus value and chromatic error and the identified area of diseased skin's color are given. By measuring skin's surface color and reflecting spectral curve, a way to distinguish its feature, automatically and objectively, is offered. Some skin disease auto-diagnosis can be realized, comparing the reflecting spectrum and chromatic aberration of malignant skin with those of healthy skin. Good results were achieved by using this system to analyze and diagnose skin disease.