[Monitoring leaf nitrogen concentration and nitrogen accumulation of double cropping rice based on crop growth monitoring and diagnosis apparatus]. / 基于作物生长监测诊断仪的双季稻叶片氮含量和氮积累量监测.

To verify the accuracy and adaptability of crop growth monitoring and diagnosis apparatus (CGMD) in monitoring nitrogen nutrition index of double cropping rice, we established a monitoring model of leaf nitrogen concentration (LNC) and leaf nitrogen accumulation (LNA) for double cropping rice based on CGMD. Eight early and late rice cultivars were selected and four nitrogen application rates were set up. The differential vegetation index (DVI), normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) were collected using CGMD. Meanwhile, ASD FH2 high spectrometer was used to collect canopy spectral reflectance and calculated DVI, NDVI, and RVI. To verify the accuracy of CGMD, we compared the canopy vegetation indices change characteristics collected by CGMD and ASD FH2. The CGMD-based monitoring models of LNC and LNA were established, which was tested with independent field data. The results showed that LNC, LNA, DVI, NDVI and RVI of early and late rice increased with increasing nitrogen application rate, and increased first and then decreased with the advance of growth progress. The determination coefficient (R2) of fitting for DVI, NDVI and RVI from CGMD and ASD FH2 were 0.9350, 0.9436 and 0.9433, respectively. This result indicated that the measurement accuracy of CGMD was high, and that the CGMD could be used to replace ASD FH2 to measure canopy vegetation indices of early and late rice. Compared with the three canopy vegetation indices based on CGMD, the correlation between NDVICGMD and LNC and that between RVICGMD and LNA was the highest. The exponential model based on NDVICGMD could be used to accurate estimate LNC with the R2 in the range of 0.8581-0.9318, and the root mean square error (RMSE), relation root mean square error (RRMSE) and correlation coefficient (r) of model validation in the range of 0.1%-0.2%, 4.0%-8.5%, and 0.9041-0.9854, respectively. The power function model based on RVICGMD could be used to estimate LNA with the R2 in the range of 0.8684-0.9577, and the RMSE, RRMSE and r of model validation in the range of 0.37-0.89 g·m-2, 6.7%-20.4% and 0.9191-0.9851, respectively. Compared with the chemical testing method, using the CGMD could conveniently and accurately measure LNC and LNA of early and late rice, which had a potential to be widely applied for high yield and high efficiency cultivation and precise management of nitrogen fertilizer in double cropping rice production.

[Model construction and application for nitrogen nutrition monitoring and diagnosis in double-cropping rice of Jiangxi Province, China]. / æ±Ÿè¥¿åŒå­£ç¨»æ°®ç´ ç›‘æµ‹è¯Šæ–­æ¨¡åž‹çš„å»ºç«‹ä¸Žåº”ç”¨.

The spectrometer-based nitrogen (N) nutrition monitoring and diagnosis models for double-cropping rice in Jiangxi is important for recommending precise N topdressing rate, achieving high yield, improving grain quality and increasing economic efficiency. Field experiments were conducted in Jiangxi in 2016 and 2017, involving different early rice and late rice cultivars and N application rates. Plant N accumulation (PNA) and canopy spectral vegetation indices (VIs) were measured at tillering and jointing stages with two spectrometers, i.e., GreenSeeker (an active multispectral sensor containing 780 and 660 nm wavelengths) and crop growth monitoring and diagnosis apparatus (CGMD, a passive multispectral sensor containing 810 and 720 nm wavelengths). The VI-based models of PNA were established from a experimental dataset and then validated using an independent dataset. The N topdressing rates for tillering and jointing stages were calculated using the newly developed N spectral diagnosis model and higher yield cultivation experience of double-cropping rice. The results showed that the VIs from two spectrometers were strongly positively correlated with PNA at both growth stages, with the model performance for tillering or jointing stages was better than that for the early growth stages. The exponential equation of normalized difference vegetation index (NDVI(780,660)) from GreenSeeker could be used to estimate PNA with a determination coefficient (R2) in the range of 0.92-0.94, the root mean square error (RMSE), relative root mean square error (RRMSE) and correlation coefficient (r) of model validation in the range of 3.09-5.96 kg·hm-2, 5.8%-18.5% and 0.92-0.98, respectively. The linear equation of difference vegetation index (DVI(810,720)) from CGMD could be used to estimate PNA with a R2 in the range of 0.90-0.93, the RMSE, RRMSE and r of model validation in the range of 3.71-6.33 kg·hm-2, 11.7%-14.3% and 0.93-0.96, respectively. The recommended N topdressing rate with CGMD was higher than that with GreenSeeker. Compared with conventional farmer's plan, the precision N application plan reduced N fertilizer application rate by 5.5 kg·hm-2, while N agronomic efficiency and net income was improved by 0.8% and 128 yuan·hm-2, respectively. Application of the spectral monitoring and diagnosis method to guiding fertilization could reduce cost and increase grain yield and net income, and thus had great potential for guiding double-cropping rice production.

[Estimation of sugar to nitrogen ratio in wheat leaves with near infrared spectrometry].

The soluble sugar to nitrogen ratio reflects the coordination degree of carbon (C) and nitrogen (N) metabolism. Precise and real-time monitoring of soluble sugar to nitrogen ratio is of significant importance for nitrogen diagnosis and management regulation in wheat production. In this study, time-course near infrared spectroscopy and soluble sugar to nitrogen ratio of fresh and dry leaves were obtained under different field experiments with varied years and cultivar and N rates. The methods of partial least squares (PLS), back-propagation neural network (BPNN) and wavelet neural network (WNN) were used to develop the calibration models with the preprocessed spectra, respectively, and the dataset selected randomly was used to evaluate the constructed models. The results showed that the performance of the models for fresh-leaves was not satisfied, but good for dry-leaves with the root mean square errors of prediction (RMSEP) by PLS, BPNN and WNN models based on 1655-2378 nm less than 0.3% and with the coefficients of determination (R2) over than 0.9, respectively. In comparison, the model based on WNN was the best one. All these indicated that near infrared spectrometry could be applied to estimating the soluble sugar to nitrogen ratio in plant. The results provided the theoretical basis and technological approach for diagnosing crop C/N.

[Quantitative relationships between satellite channels-based spectral parameters and wheat canopy leaf nitrogen status].

Using space-borne remote sensing information to monitor the crop canopy nitrogen status and crop productivity in a large-scale is of great significance and application prospect i1 modern agriculture. With the hyper-spectral reflectance data from the wheat canopy under different nitrogen fertilization levels, this paper constructed the spectral indices (including the single wavelength, ratio spectral index, and normalized difference spectral index) simulated by satellite channels, and established the nitrogen estimation equations by quantifying the relationships between the simulated channels spectral indices and the leaf nitrogen index. The results indicated that the spectral indices based on NDVI (MSS7, MSS5), NDVI (RBV3, RBV2), TM4, CH2, MODIS1, and MODIS2 could be reliably used for estimating the leaf nitrogen content (LNC), with R2 over 0.60, and the spectral indices based on NDVI (PB4, PB2), NDVI (CH2, CHl1), NDVI (MSS7, MSS5), RVI (MSS7, MSS5), MODIS1, and MODIS2 could be accurately used for predicting the leaf nitrogen accumulation (LNA), with R2 greater than 0.86. Comparatively, NDVI (MSS7, MSS5) and NDVI (PB4, PB2) could be the more suitable spectral indices for predicting the wheat canopy LNC and LNA, respectively.

[Estimation of winter wheat leaf nitrogen accumulation based on coupling ground- and space-remotely sensed information].

By coupling the SPOT-5 multi-spectral RS images, ground-spectrum, and field measured data of different winter wheat ecological zones, a pure pixel spectrum extraction method was developed based on spectral response function and pixel unmixed, and the quantitative relationships between leaf nitrogen accumulation (LNA) and simulated, measured, and pure pixel spectra were analyzed. The estimation accuracy for LNA was in the sequence of simulated pixel spectra > pure pixel spectra > measured pixel spectra. However, the LNA monitoring model based on simulated pixel spectra couldn't be extrapolated directly to spatial level. The results of model verification also indicated that the monitoring model based on pure pixel spectra performed well in two different wheat ecological zones. Therefore, the pure pixel spectrum extraction method could be applied to other varied and remotely sensed data with different spatial and spectral resolutions by making use of the merits of ground- and space- remote sensing simultaneously, which provided a technological basis for estimating winter wheat nitrogen status in regional scale.

[Accumulative nitrogen deficit models of wheat aboveground part based on critical nitrogen concentration].

Based on three-year field experiments, three models of critical nitrogen concentration dilution curve, nitrogen nutrition index, and accumulative nitrogen deficit were constructed for the aboveground dry matter in medium protein wheat variety Yangmai 16 and low protein wheat variety Ningmai 13, respectively. The critical nitrogen concentration dilution curve model had specific biological meaning, i. e., there existed a negative power function correlation between shoot maximum dry matter (DM) and critical nitrogen concentration (Ncnc) (Yangmai 16: Ncnc = 4.65DM(-0.4); Ningmai 13: Ncnc = 4.33DM(-0.45)), the nitrogen nutrition index model could be used for accurate diagnosis of wheat plant nitrogen status, and the accumulative nitrogen deficit model could be used for quantitative regulation of nitrogen fertilizer management. The tests of the derived equations with independent experiment data (2007-2008) showed higher accuracy and reliable prediction, suggesting that the present models could be used for the diagnosis and regulation of wheat nitrogen nutrition, providing a key technical approach to precise fertilization management in wheat production.

[Effects of Xue-Bi-Jing combined with forsythia suspension on the liver gene expression levels of rats with sepsis model].

OBJECTIVE: To study the effects of "XUE BI JING plus LIANQIAO" injection on gene expression levels of rats with sepsis model. METHODS: One hundred and twenty rats were randomly divided into sham operation group, sepsis model group, Te-neng group and "XUE BI JING plus forsythia suspension" group. The sepsis model of rats was prepared by "CLP" method. Tai neng group was treated by peritoneal injection Imipenem/ Cilastatin (0.18 g/kg); "XUE BI JING plus LIANQIAO" group was treated by peritoneal injection Imipenem/ Cilastatin (0.18 g/kg) plus "xue-bi-jing" (10 ml/kg) and "liang ge san" (18 g/200 g) by intragastric administration 2 times a day; the sham operation group and model group were treated by peritoneal injection of normal saline (10 ml/kg). The survival rates at 48h and 72h were observed for all groups. The gene expression levels of livers in all groups were detected by BiostarR-40s chip. The NCBI database was used to inquest Gene function and class. RESULTS: The survival rates at 48h and 72h in "XUE BI JING+ forsythia suspension" group were 83.3% and 76.7% which were significantly higher than those (30.0% and 16.7%) in sepsis model group and those (60.0% and 33.3%) in Te-neng group (P < 0.01). Model group/control group have 305 differential expression genes with 159 up-regulation genes and 146 down-regulation genes. Tai-neng group/model group have 386 differential expression genes with 206 up-regulation genes and 180 down-regulation genes. "XUE BI JING plus forsythia suspension" group/model group have 342 differential expression genes with 102 up-regulation genes and 240 downregulation genes. The genes with up-regulation in model group/ control group and with down-regulation in"XUE BI JING plus forsythia suspension" group/model group were 24. The genes with down-regulation in the model group/ sham operation group and with up-regulation in "XUE BI JING plus forsythia suspension"group/model group were 16. CONCLUSION: "XUE BI JING plus forsythia suspension" can reduce the mortality of rats with sepsis, which could be due to the expression of relative regulation genes.

[Defining of wheat growth management zones based on remote sensing and geostatistics].

Taking the winter wheat planting areas in Rugao City and Haian County of Jiangsu Province as test objects, the clustering defining of wheat growth management zones was made, based on the spatial variability analysis and principal component extraction of the normalized difference vegetation index (NDVI) data calculated from the HJ-1A/B CCD images (30 m resolution) at different growth stages of winter wheat, and of the soil nutrient indices (total nitrogen, organic matter, available phosphorus, and available potassium). The results showed that the integration of the NDVI at heading stage with above-mentioned soil nutrient indices produced the best results of wheat growth management zone defining, with the variation coefficients of NDVI and soil nutrient indices in each defined zone ranged in 4.5% -6.1% and 3.3% -87.9%, respectively. However, the variation coefficients were much larger when the wheat growth management zones were defined individually by NDVI or by soil nutrient indices, suggesting that the newly developed defining method could reduce the variability within the defined management zones and improve the crop management precision, and thereby, contribute to the winter wheat growth management and process simulation at regional scale.

[Estimation of optimum normalized difference spectral index for nitrogen accumulation in wheat leaf based on reduced precise sampling method].

Four independent field experiments with 6 wheat varieties and 5 nitrogen application levels were conducted, and time-course measurements were taken on the canopy hyperspectral reflectance and leaf N accumulation per unit soil area (LNA, g N x m(-2)). By adopting reduced precise sampling method, all possible normalized difference spectral indices [NDSI(i,j)] within the spectral range of 350-2500 nm were constructed, and the relationships of LNA to the NDSI(i,j) were quantified, aimed to explore the new sensitive spectral bands and key index from precise analysis of ground-based hyperspectral information, and to develop prediction models for wheat LNA. The results showed that the sensitive spectral bands for LNA were located in visible light and near infrared regions, especially at 860 nm and 720 nm for wheat LNA. The monitoring model based on the NDSI(860,720) was formulated as LNA = 26.34 x [NDSI(860,720)](1.887), with R2 = 0.900 and SE = 1.327. The fitness test of the derived equations with independent datasets showed that for wheat LNA, the model gave the estimation accuracy of 0.823 and the RMSE of 0.991 g N x m(-2), indicating a good fitness between the measured and estimated LNA. The present normalized hyperspectral parameter of NDSI(860,720) and its derived regression model could be reliably used for the estimation of winter wheat LNA.

[A near-infrared spectral index for estimating soil organic matter content].

Taking the air-dried samples of five soil types from middle and eastern China as test materials, the correlations of their organic matter content with the spectral reflectance of near-infrared (1000-2500 nm), and with the ratio index (RI), difference index (DI), and normalized difference index (ND) of the first derivative values of the reflectance between two bands were studied. Based on this, the key spectral indices and the quantitative models for estimating soil organic matter (SOM) content were developed. After corrected with Multiplicative Scatter Correction (MSC) and Savitzky-Golay (SG) smoothing methods, the spectral reflectance of near-infrared had an obviously high correlation with SOM, compared with the original spectral reflectance, while the corrected spectral indices of the first derivative values of the reflectance between two bands took the intermediate position. The correlation of the spectral indices with SOM was in the order of was DI > RI > ND, regardless the composition of the original spectral reflectance or the first derivative spectra. The DI of the reflectance of near-infrared between 1883 and 2065 nm corrected with MSC and SG smoothing methods [DI(CR1883, CR2065)] had the best linear correlations with SOM. The test of the monitoring model based on DI(CR1883, CR2065) with the independent datasets of SOM showed that the R2 and RMSE validation values were 0.837 and 4.06, respectively. Comparing with the results from the Partial Least Square (PLS) method, the monitoring model based on DI (CR1883, CR2065) was somewhat inferior. However, the DI(CR1883, CR2065) only needed two reflectance bands, and the monitoring model was simpler, being able to provide more available information for developing portable instruments, and a good spectral index for estimating SOM content.

[Quantitative relationships between hyper-spectral vegetation indices and leaf area index of rice].

Based on field experiments with different rice varieties under different nitrogen application levels, the quantitative relationships of rice leaf area index (LAI) with canopy hyper-spectral parameters at different growth stages were analyzed. Rice LAI had good relationships with several hyper-spectral vegetation indices, the correlation coefficient being the highest with DI (difference index), followed by with RI (ratio index), and NI (normalized index), based on the spectral reflectance or the first derivative spectra. The two best spectral indices for estimating LAI were the difference index DI (854, 760) (based on two spectral bands of 850 nm and 760 nm) and the difference index DI (D676, D778) (based on two first derivative bands of 676 nm and 778 nm). In general, the hyper-spectral vegetation indices based on spectral reflectance performed better than the spectral indices based on the first derivative spectra. The tests with independent dataset suggested that the rice LAI monitoring models with difference index DI (854,760) as the variable could give an accurate LAI estimation, being available for estimation of rice LAI.

[Exploring novel hyperspectral band and key index for leaf nitrogen accumulation in wheat].

The objectives of the present study were to explore new sensitive spectral bands and ratio spectral indices based on precise analysis of ground-based hyperspectral information, and then develop regression model for estimating leaf N accumulation per unit soil area (LNA) in winter wheat (Triticum aestivum L.). Three field experiments were conducted with different N rates and cultivar types in three consecutive growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance and LNA tinder the various treatments. By adopting the method of reduced precise sampling, the detailed ratio spectral indices (RSI) within the range of 350-2 500 nm were constructed, and the quantitative relationships between LNA (gN m(-2)) and RSI (i, j) were analyzed. It was found that several key spectral bands and spectral indices were suitable for estimating LNA in wheat, and the spectral parameter RSI (990, 720) was the most reliable indicator for LNA in wheat. The regression model based on the best RSI was formulated as y = 5.095x - 6.040, with R2 of 0.814. From testing of the derived equations with independent experiment data, the model on RSI (990, 720) had R2 of 0.847 and RRMSE of 24.7%. Thus, it is concluded that the present hyperspectral parameter of RSI (990, 720) and derived regression model can be reliably used for estimating LNA in winter wheat. These results provide the feasible key bands and technical basis for developing the portable instrument of monitoring wheat nitrogen status and for extracting useful spectral information from remote sensing images.

[Monitoring of wheat leaf pigment concentration with hyper-spectral remote sensing].

In a two-year field experiment with wheat cultivars under different application rates of fertilizer N, the wheat leaf pigment concentrations were monitored with hyper-spectral remote sensing, and quantitative monitoring models were established. The results showed that the pigment concentrations in wheat leaves increased with increasing N application rate, and differed significantly among test cultivars. With the growth of wheat, the relative concentration of chlorophyll a + b varied more obviously than those of chlorophyll b and carotenoid (Car), and the sensitive bands of the pigments occurred mostly within visible light range, especially in red-edge district. The analyses on the relationships between eight existing vegetation indices and leaf pigment concentrations indicated that the concentrations of chlorophyll a, chlorophyll b, and chlorophyll a + b were highly correlated with red edge position, and the relationships to REP(LE) were better than to REP(IG), giving the determination coefficient R2 as 0.835, 0.841 and 0.840, and standard error SE as 0.264, 0.095 and 0.353, respectively. However, the R2 values between Car and different spectral indices decreased significantly, and the differences among the spectrum indices were very small. The tests of the monitoring models with independent datasets indicated that REP(LE) and REP(IG) were the best to predict leaf pigment concentrations. The R2 of chlorophyll a, chlorophyll a + b, and Car for REP(LE) were 0.805, 0.744 and 0.588, with the RE being 9.0%, 9.7% and 14.6%, respectively, and the R2 and RE of chlorophyll b for REP(IG) were 0.632 and 18.2%, respectively. It was suggested that the red-edge parameters of hyper-spectral reflectance had stable relationships with the pigment concentrations in wheat leaves, and especially, REP(LE) could be used to reliably estimate the concentrations of leaf chlorophyll a and chlorophyll a + b.

[Quantitative relationships between leaf total nitrogen concentration and canopy reflectance spectra of rice].

By the method of statistics, this paper approached the quantitative relationships between leaf total nitrogen concentration (LNC) and canopy reflectance spectra of rice, based on the data from 5-year field experiments involving different varieties and nitrogen fertilization rates. The results showed that the LNC had higher correlations with the key spectral parameters of two bands than of single band. The relative, differential, and normalized difference vegetation indices (RVI, DVI, NDVI) of the bands in near infrared (760-1,220 nm) and visible light 510 nm, 560 nm, 680 nm and 710 nm all showed significantly positive correlations to LNC, and NDVI showed the best. All the parameters having significant correlations with LNC were selected to compare the R2 and SE in the regression equations with LNC, which confirmed that the NDVI of R1220 and R710 was the best parameter for predicting the LNC. The quantitative equation LNC = 3.2708 x NDVI (1220, 710) + 0.8654 was tested by the data from other three field experiments with different rice cultivars, water conditions and nitrogen fertilization rates, and the estimated R2, slope, and RMSE were ranged in 0.674-0.862, 0.908-1.010 and 11.315%-19.491%, respectively, indicating a good fit between the predicted and observed values of LNCs, which suggested that this model was feasible for predicting the LNC of rice under different cultivation conditions.

[Estimation of dry matter accumulation in above-ground part of cotton by means of canopy reflectance spectra].

Through analyzing the relationships of the dry matter accumulation in above-ground part of cotton with the canopy reflectance of single waveband and all two-band combinations in ratio vegetation index (RVI, R(lamda1)/R(lamda2)), normalized difference vegetation index (NDVI, (R(lamda1)-R(lamda2))/(R(lamda1) + R(lamda2 and differential vegetation index (DVI, R(lamda1)-R(lamda2)), the characteristic spectral wavebands for indicating the dry matter accumulation in above-ground part of cotton were determined, and the corresponding prediction model was established. The results showed that the vegetation indices comprised of visible light (560 and 710 nm) and near infrared light (810, 870, 950, 1100 and 1220 nm) were highly related to the dry matter accumulation in the above-ground part of cotton, and the RVI (1100, 560) was the best spectral index for the estimation. The corresponding prediction model established by stepwise regression method was Y (g x m(-2)) = 66.274 x RVI (1100, 560)-148.84. It could be feasible to estimate the dry matter accumulation in above-ground part of cotton with remote sensing.

[A dynamic knowledge model for designing suitable scheme of rapeseed sowing and transplanting].

Based on the sum-up and abstraction of the relationships of rapeseed growth characters with ecological environment, cultivar type, production condition and yield target, a dynamic knowledge model was developed by using knowledge engineering and system modeling method, which could be used for designing a suitable sowing and transplanting scheme of different rapeseed varieties under different spatial and temporal environments. Case studies on the knowledge model with the data sets of three different sites, nine different variety types, and two different sowing styles indicated a good performance of the model system in decision-making, explanation, and wide applicability.

[Estimation of nitrogen concentration in cotton leaf based on canopy reflectance spectra].

Through analyzing the relationships of nitrogen concentration in cotton leaf under different nitrogen supply levels with canopy multi-spectral reflectance and its derived ratio vegetation index (RVI, rholambda1/rholambda2), normalized difference vegetation index (NDVI, (rho(lambda1) - rho(lambda2))/(rho(lambda1)) + rho(lambda2)) and differential vegetation index (DVI, rho(lambda1) - rho(lambda2)), the sensitive wave bands and prediction functions of cotton leaf nitrogen concentration were worked out. The vegetation index composed of visible region (610, 660, 680 and 710 nm) and near infrared region (760, 810, 870, 950, 1 100 and 1 220 nm) had a higher correlation with the nitrogen concentration in cotton leaf, and the RVI composed of 950 nm and 710 nm could best predict the leaf nitrogen concentration. The validation with independent field experimental data indicated that RVI (950 nm and 710 nm) -based model was suitable for estimation of leaf nitrogen concentration of different cotton cultivars at their different growth stages.