Psychrobacter halodurans sp. nov. and Psychrobacter coccoides sp. nov., two new slightly halophilic bacteria isolated from marine sediment.

In this study, two bacterial strains designated F2608T and F1192T, isolated from marine sediment sampled in Weihai, PR China, were characterized using a polyphasic approach. Strains were aerobic, Gram-stain-negative and motile. According to the results of phylogenetic analyses based on their 16S rRNA genes, these two strains should be classified under the genus Psychrobacter and they both show <98.5% sequence similarity to their closest relative, Psychrobacter celer JCM 12601T. Moreover, strain F2608T showed 97.5% sequence similarity to strain F1192T. Strain F2608T grew at 4-37 °C (optimum, 30-33 °C) and at pH 6.0-9.0 (optimum, pH 6.5-7.0) in the presence of 0-12% (w/v) NaCl (optimum, 4.0-5.0%). Strain F1192T grew at 4-37 °C (optimum, 30 °C) and at pH 5.5-9.0 (optimum, pH 7.0-7.5) in the presence of 0.5-12% (w/v) NaCl (optimum, 3.0-4.0%). The genomic DNA G+C contents of strain F2608T and strain F1192T were 47.4 and 44.9 %, respectively. Genomic characteristics including average nucleotide identity and digital DNA-DNA hybridization values clearly separated strain F2608T from strain F1192T. The sole isoprenoid quinone in these two strains was ubiquinone 8 and the major cellular fatty acids (>10.0%) were C18:1 ω9c and C17:1 ω8c. The major polar lipids of these two strains were phosphatidylglycerol, phosphatidylethanolamine and diphosphatidylglycerol. Based on the results of polyphasic analysis, the two strains represent two novel species of the genus Psychrobacter, for which the names Psychrobacter halodurans sp. nov. and Psychrobacter coccoides sp. nov. are proposed. The type strains are F2608T (=MCCC 1K05774T=KCTC 82766T) and F1192T (=MCCC 1K05775T=KCTC 82765T), respectively.

Winter Wheat Nitrogen Estimation Based on Ground-Level and UAV-Mounted Sensors.

A better understanding of wheat nitrogen status is important for improving N fertilizer management in precision farming. In this study, four different sensors were evaluated for their ability to estimate winter wheat nitrogen. A Gaussian process regression (GPR) method with the sequential backward feature removal (SBBR) routine was used to identify the best combinations of vegetation indices (VIs) sensitive to wheat N indicators for different sensors. Wheat leaf N concentration (LNC), plant N concentration (PNC), and the nutrition index (NNI) were estimated by the VIs through parametric regression (PR), multivariable linear regression (MLR), and Gaussian process regression (GPR). The study results reveal that the optical fluorescence sensor provides more accurate estimates of winter wheat N status at a low-canopy coverage condition. The Dualex Nitrogen Balance Index (NBI) is the best leaf-level indicator for wheat LNC, PNC and NNI at the early wheat growth stage. At the early growth stage, Multiplex indices are the best canopy-level indicators for LNC, PNC, and NNI. At the late growth stage, ASD VIs provide accurate estimates for wheat N indicators. This study also reveals that the GPR with SBBR analysis method provides more accurate estimates of winter wheat LNC, PNC, and NNI, with the best VI combinations for these sensors across the different winter wheat growth stages, compared with the MLR and PR methods.

Identifying barriers to sustainable apple production: A stakeholder perspective.

Apple is one of the most important cash crops in China. However, negative economic, environmental and social impacts are associated with its production. This study aims to apply a holistic systems perspective to understand existing problems associated with apple production in China and use this information to improve its sustainability. A structured survey was administered to farmers (n = 245) in Shandong and Shanxi provinces, combined with semi-structured interviews with apple supply chain stakeholders (n = 25). Themes, dimensions and relationships were identified based on an inductive thematic analysis of interview data, and then triangulated against the survey data. Interpretive Structural Modelling and Cross-Impact Matrix Multiplication Applied to Classification methods were applied to investigate interrelationships and effects of the elicited elements within the system. The results indicated that various environmental, economic and social problems are associated with apple production in China, including environmental and health risks associated with synthetic input applications, yield instability, deterioration of apple quality, farmers' uncertainty about accessing routes to market, and the ageing farming workforce. The interaction of socio-economic and supply chain issues has contributed to the system "lock-in" to unsustainable practices within the apple production system. Existing agricultural policies were ineffective as they did not include policy leverage to mitigate the multiple factors driving lock-in to unsustainable practices within the system. The research has provided evidence to enable policymakers to develop effective and targeted strategies to facilitate sustainable production within the apple production system. In particular, the future policy mix should consider the entirety of the food system including perspectives and requirements of different stakeholders. The three-stage approach applied has demonstrated its feasibility of investigating sustainability issues facing a particular industry within a specific cultural and policy context.

Establishing NDRE dynamic models of winter wheat under multi-nitrogen rates based on a field spectral sensor.

Field spectral sensors provide real-time, reliable, quantitative monitoring of crop growth. Fitting the continuous growth in the entire growing period from the measurements of limited frequency is helpful to the comparative analysis of interannual growth and fertilizer management in the field. To exploit this capacity, our work presents a model that uses the normalized difference red edge (NDRE) index derived from the field spectral sensor for real-time monitoring of the canopy growth of winter wheat in the whole growing period. We developed this model from experiments in three counties in Hebei province, China, where we obtained the near-infrared and red edge reflectance, grain yield, and canopy parameters for eight growth stages and for various nitrogen (N) rates. Given the correlation between effective accumulated temperature and crop growth, we used the growing degree-days as an adjustment parameter to develop models for dynamic monitoring of the NDRE of the winter wheat canopy during the entire growing period. The results show that high determination coefficients (R2=0.89 to 0.96) are obtained from all models based on relative NDRE and effective accumulative temperature (independent of N fertilization rates). The model based on the rational function is the best of all models tested, with the accuracy for normal and high N fertilization rates being slightly greater than that for low N fertilization rates. Therefore, a relative-NDRE model with the accumulative growing degree-days since sowing could allow monitoring canopy NDRE of winter wheat at any time, which could be helpful for overcoming the shortage of incomparable growth derived from the differences of sensing date, sowing date, and fertilizer, etc.

Inversion reflectance by apple tree canopy ground and unmanned aerial vehicle integrated remote sensing data.

To obtain accurate spatially continuous reflectance from Unmanned Aerial Vehicle (UAV) remote sensing, UAV data needs to be integrated with the data on the ground. Here, we tested accuracy of two methods to inverse reflectance, Ground-UAV-Linear Spectral Mixture Model (G-UAV-LSMM) and Minimum Noise Fraction-Pixel Purity Index-Linear Spectral Mixture Model (MNF-PPI-LSMM). At wavelengths of 550, 660, 735 and 790 nm, which were obtained by UAV multispectral observations, we calculated the canopy abundance based on the two methods to acquire the inversion reflectance. The correlation of the inversion and measured reflectance values was stronger in G-UAV-LSMM than MNF-PPI-LSMM. We conclude that G-UAV-LSMM is the better model to obtain the canopy inversion reflectance.

Immunogenicity and protective efficacy of an EB66® cell culture-derived duck Tembusu virus vaccine.

The avian EB66® cell line, derived from duck embryonic stem cells, has been widely used for producing human and animal therapeutic proteins and vaccines. In current study we evaluated the potential use of EB66® cell line in a cell culture-derived duck Tembusu virus (DTMUV) vaccine development. After optimizing the growth conditions of DTMUV HB strain in EB66® cells, we successfully generated three batches of viruses with ELD50 titres of 105.9/0.1 ml, 105.3/0.1 ml and 105.5/0.1 ml, respectively, for using in the preparation of inactivated vaccines. The immunogenicity and protective efficacy of these EB66® cells-derived inactivated vaccines were examined in ducks. Results indicated that all three batches of vaccines induced haemagglutination-inhibition (HI) antibody response in immunized birds at 2 weeks after a single immunization. Immunized ducks and ducklings were protected against a virulent challenge at 4 weeks after a booster immunization. The duration of immunity was for 3-4 months after a booster immunization. These results demonstrated the feasibility of using EB66® cell line to grow up DTMUV for vaccine preparation. RESEARCH HIGHLIGHTS Duck Tembusu virus can be propagated in EB66® cells. EB66® cell-derived inactivated DTMUV vaccines are immunogenic and can provide protection against a virulent challenge. A long-lasting immunity is induced after a booster immunization.

Diagnosis of the Severity of Fusarium Head Blight of Wheat Ears on the Basis of Image and Spectral Feature Fusion.

Fusarium head blight (FHB), one of the most prevalent and damaging infection diseases of wheat, affects quality and safety of associated food. In this study, to realize the early accurate monitoring of FHB, a diagnostic model of disease severity was proposed based on the fusion features of image and spectral features. First, the hyperspectral image of FHB infected in the range of the 400-1000 nm spectrum was collected, and the color parameters of wheat ear and spot region were segmented based on image features. Twelve sensitive bands were extracted using the successive projection algorithm, gray-scale co-occurrence matrix, and RGB color model. Four texture features were extracted from each feature band image as texture variables, and nine color feature variables were extracted from R, G, and B component images. Texture features with high correlation and color features were selected to participate in the final model building parameters via correlation analysis. Finally, the particle swarm optimization support vector machine (PSO-SVM) algorithm was used to build the model based on the diagnosis model of disease severity of FHB with different combinations of characteristic variables. The experimental results showed that the PSO-SVM model based on spectral and color feature fusion was optimal. Moreover, the accuracy of the training and prediction set was 95% and 92%, respectively. The method based on fusion features of image and spectral features can accurately and effectively diagnose the severity of FHB, thereby providing a technical basis for the timely and effective control of FHB and precise application of a pesticide.

Simulation and Verification of Vertical Heterogeneity Spectral Response of Winter Wheat Based on the mSCOPE Model.

Vertical heterogeneity of the biochemical characteristics of crop canopy is important in diagnosing and monitoring nutrition, disease, and crop yield via remote sensing. However, the research on vertical isomerism was not comprehensive. Experiments were carried out from the two levels of simulation and verification to analyze the applicability of this recently development model. Effects of winter wheat on spectrum were studied when input different structure parameters (e.g., leaf area index (LAI)) and physicochemical parameters (e.g., chlorophyll content (Chla+b) and water content (Cw)) to the mSCOPE (Soil Canopy Observation, Photochemistry, and Energy fluxes) model. The maximum operating efficiency was 127.43, when the winter wheat was stratified into three layers. Meanwhile, the simulation results also proved that: the vertical profile of LAI had an influence on canopy reflectance in almost all bands; the vertical profile of Chla+b mainly affected the reflectivity of visible region; the vertical profile of Cw only affected the near-infrared reflectance. The verification results showed that the vegetation indexes (VIs) selected of different bands were strongly correlated with the parameters of the canopy. LAI, Chla+b and Cw affected VIs estimation related to LAI, Chla+b and Cw respectively. The Root Mean Square Error (RMSE) of the new-proposed NDVIgreen was the smallest, which was 0.05. Sensitivity analysis showed that the spectrum was more sensitive to changes in upper layer parameters, which verified the rationality of mSCOPE model in explaining the law that light penetration in vertical nonuniform canopy gradually decreases with the increase of layers.

Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images.

Crop yield is related to national food security and economic performance, and it is therefore important to estimate this parameter quickly and accurately. In this work, we estimate the yield of winter wheat using the spectral indices (SIs), ground-measured plant height (H), and the plant height extracted from UAV-based hyperspectral images (HCSM) using three regression techniques, namely partial least squares regression (PLSR), an artificial neural network (ANN), and Random Forest (RF). The SIs, H, and HCSM were used as input values, and then the PLSR, ANN, and RF were trained using regression techniques. The three different regression techniques were used for modeling and verification to test the stability of the yield estimation. The results showed that: (1) HCSM is strongly correlated with H (R2 = 0.97); (2) of the regression techniques, the best yield prediction was obtained using PLSR, followed closely by ANN, while RF had the worst prediction performance; and (3) the best prediction results were obtained using PLSR and training using a combination of the SIs and HCSM as inputs (R2 = 0.77, RMSE = 648.90 kg/ha, NRMSE = 10.63%). Therefore, it can be concluded that PLSR allows the accurate estimation of crop yield from hyperspectral remote sensing data, and the combination of the SIs and HCSM allows the most accurate yield estimation. The results of this study indicate that the crop plant height extracted from UAV-based hyperspectral measurements can improve yield estimation, and that the comparative analysis of PLSR, ANN, and RF regression techniques can provide a reference for agricultural management.

Estimation of Crop Growth Parameters Using UAV-Based Hyperspectral Remote Sensing Data.

Above-ground biomass (AGB) and the leaf area index (LAI) are important indicators for the assessment of crop growth, and are therefore important for agricultural management. Although improvements have been made in the monitoring of crop growth parameters using ground- and satellite-based sensors, the application of these technologies is limited by imaging difficulties, complex data processing, and low spatial resolution. Therefore, this study evaluated the use of hyperspectral indices, red-edge parameters, and their combination to estimate and map the distributions of AGB and LAI for various growth stages of winter wheat. A hyperspectral sensor mounted on an unmanned aerial vehicle was used to obtain vegetation indices and red-edge parameters, and stepwise regression (SWR) and partial least squares regression (PLSR) methods were used to accurately estimate the AGB and LAI based on these vegetation indices, red-edge parameters, and their combination. The results show that: (i) most of the studied vegetation indices and red-edge parameters are significantly highly correlated with AGB and LAI; (ii) overall, the correlations between vegetation indices and AGB and LAI, respectively, are stronger than those between red-edge parameters and AGB and LAI, respectively; (iii) Compared with the estimations using only vegetation indices or red-edge parameters, the estimation of AGB and LAI using a combination of vegetation indices and red-edge parameters is more accurate; and (iv) The estimations of AGB and LAI obtained using the PLSR method are superior to those obtained using the SWR method. Therefore, combining vegetation indices with red-edge parameters and using the PLSR method can improve the estimation of AGB and LAI.

Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery.

This study aims to efficiently estimate the crop water content of winter wheat using high spatial and temporal resolution satellite-based imagery. Synthetic-aperture radar (SAR) data collected by the Sentinel-1 satellite and optical imagery from the Sentinel-2 satellite was used to create inversion models for winter wheat crop water content, respectively. In the Sentinel-1 approach, several enhanced radar indices were constructed by Sentinel-1 backscatter coefficient of imagery, and selected the one that was most sensitive to soil water content as the input parameter of a water cloud model. Finally, a water content inversion model for winter wheat crop was established. In the Sentinel-2 approach, the gray relational analysis was used for several optical vegetation indices constructed by Sentinel-2 spectral feature of imagery, and three vegetation indices were selected for multiple linear regression modeling to retrieve the wheat crop water content. 58 ground samples were utilized in modeling and verification. The water content inversion model based on Sentinel-2 optical images exhibited higher verification accuracy (R = 0.632, RMSE = 0.021 and nRMSE = 19.65%) than the inversion model based on Sentinel-1 SAR (R = 0.433, RMSE = 0.026 and nRMSE = 21.24%). This study provides a reference for estimating the water content of wheat crops using data from the Sentinel series of satellites.

Automated Counting of Rice Panicle by Applying Deep Learning Model to Images from Unmanned Aerial Vehicle Platform.

The number of panicles per unit area is a common indicator of rice yield and is of great significance to yield estimation, breeding, and phenotype analysis. Traditional counting methods have various drawbacks, such as long delay times and high subjectivity, and they are easily perturbed by noise. To improve the accuracy of rice detection and counting in the field, we developed and implemented a panicle detection and counting system that is based on improved region-based fully convolutional networks, and we use the system to automate rice-phenotype measurements. The field experiments were conducted in target areas to train and test the system and used a rotor light unmanned aerial vehicle equipped with a high-definition RGB camera to collect images. The trained model achieved a precision of 0.868 on a held-out test set, which demonstrates the feasibility of this approach. The algorithm can deal with the irregular edge of the rice panicle, the significantly different appearance between the different varieties and growing periods, the interference due to color overlapping between panicle and leaves, and the variations in illumination intensity and shading effects in the field. The result is more accurate and efficient recognition of rice-panicles, which facilitates rice breeding. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a global scale.

Hyperspectral-based Estimation of Leaf Nitrogen Content in Corn Using Optimal Selection of Multiple Spectral Variables.

Accurate and dynamic monitoring of crop nitrogen status is the basis of scientific decisions regarding fertilization. In this study, we compared and analyzed three types of spectral variables: Sensitive spectral bands, the position of spectral features, and typical hyperspectral vegetation indices. First, the Savitzky-Golay technique was used to smooth the original spectrum, following which three types of spectral parameters describing crop spectral characteristics were extracted. Next, the successive projections algorithm (SPA) was adopted to screen out the sensitive variable set from each type of parameters. Finally, partial least squares (PLS) regression and random forest (RF) algorithms were used to comprehensively compare and analyze the performance of different types of spectral variables for estimating corn leaf nitrogen content (LNC). The results show that the integrated variable set composed of the optimal ones screened by SPA from three types of variables had the best performance for LNC estimation by the validation data set, with the values of R2, root means square error (RMSE), and normalized root mean square error (NRMSE) of 0.77, 0.31, and 17.1%, and 0.55, 0.43, and 23.9% from PLS and RF, respectively. It indicates that the PLS model with optimally multitype spectral variables can provide better fits and be a more effective tool for evaluating corn LNC.

Carbon-Filled Organic Phase-Change Materials for Thermal Energy Storage: A Review.

Phase-change materials (PCMs) are essential modern materials for storing thermal energy in the form of sensible and latent heat, which play important roles in the efficient use of waste heat and solar energy. In the development of PCM technology, many types of materials have been studied, including inorganic salt and salt hydrates and organic matter such as paraffin and fatty acids. Considerable research has focused on the relationship between the material structure and energy storage properties to understand the heat storage/emission mechanism involved in controlling the energy storage performance of materials. In this study, we review the application of various carbon-filled organic PCMs in the field of heat storage and describe the current state of this research.

[Effect of 18-ß Glycyrrhetinic Acid on the Endoplasmic Reticulum of Nasal Epithelial Cells in Allergic Rhinitis Model Rats].

OBJECTIVE: To explore the effect of 18-ß glycyrrhetinic acid (GA) on the endoplasmic reticulum of nasal epithelial cells in allergic rhinitis (AR) model rats. METHODS: Totally 96 Wistar rats were randomly divided into the blank group, the AR model group, the loratadine group, the GA group, 24 in each group. AR models were established by peritoneally injecting ovalbumin (OVA). Morphological scoring was performed. GA at 21. 6 mg/kg was intragastrically administered to rats in the GA group. Nasal mucosal tissues were taken for electron microscopic examinations at the second, fourth, sixth, and tenth week after drug intervention. RESULTS: The overlapping score was 2.10 ± 0.45 in the blank group, 5.10 ± 0.56 in the loratadine group, 5.10 ± 0.56 in the AR model group, 5.20 ± 0.78 in the GA group, showing statistical difference when compared with the blank group (P < 0.01). Results under transmission electron microscope showed that the number of the endoplasmic reticulum increased in the AR model group, with obvious cystic dilatation, a lot of vacuole formation, and degranulation. A large number of free ribosomes could be seen in cytoplasm. With persistent allergen exposure, changes mentioned above was progressively aggravated in the endoplasmic reticulum of nasal mucosal epithelium in the AR model group. But the dilation of endoplasmic reticulum, vacuole formation, and degranulation were relieved in the GA group, and got close to those of the blank group. CONCLUSION: 18-ß GA could improve the expansion, vacuolization, and degranulation of the endoplasmic reticulum of nasal epithelial cells in AR model rats.

[Estimating Leaf Area Index of Crops Based on Hyperspectral Compact Airborne Spectrographic Imager (CASI) Data].

The fast estimation of leaf area index (LAI) is significant for learning the crops growth, monitoring the disease and insect, and assessing the yield of crops. This study used the hyperspectral compact airborne spectrographic imager (CASI) data of Zhangye city, in Heihe River basin, on July 7, 2012, and extracted the spectral reflectance accurately. The potential of broadband and red-edge vegetation index for estimating the LAI of crops was comparatively investigated by combined with the field measured data. On this basis, the sensitive wavebands for estimating the LAI of crops were selected and two new spectral indexes (NDSI and RSI) were constructed, subsequently, the spatial distribution of LAI in study area was analyzed. The result showed that broadband vegetation index NDVI had good effect for estimating the LAI when the vegetation coverage is relatively lower, the R2 and RMSE of estimation model were 0. 52, 0. 45 (p<0. 01) , respectively. For red-edge vegetation index, CIred edge took the different crop types into account fully, thus it gained the same estimation accuracy with NDVI. NDSI(569.00, 654.80) and RSI(597.60, 654.80) were constructed by using waveband combination algorithm, which has superior estimation results than NDVI and CIred edge. The R2 of estimation model used NDSI(569.00, 654.80) was 0. 77(p<0. 000 1), it mainly used the wavebands near the green peak and red valley of vegetation spectrum. The spatial distribution map of LAI was made according to the functional relationship between the NDSI(569.00, 654.80) and LAI. After analyzing this map, the LAI values were lower in the northwest of study area, this indicated that more fertilizer should be increased in this area. This study can provide technical support for the agricultural administrative department to learn the growth of crops quickly and make a suitable fertilization strategy.

[Estimating Winter Wheat Nitrogen Vertical Distribution Based on Bidirectional Canopy Reflected Spectrum].

The vertical distribution of crop nitrogen is increased with plant height, timely and non-damaging measurement of crop nitrogen vertical distribution is critical for the crop production and quality, improving fertilizer utilization and reducing environmental impact. The objective of this study was to discuss the method of estimating winter wheat nitrogen vertical distribution by exploring bidirectional reflectance distribution function (BRDF) data using partial least square (PLS) algorithm. The canopy reflectance at nadir, +/-50 degrees and +/- 60 degrees; at nadir, +/- 30 degrees and +/- 40 degrees; and at nadir, +/- 20 degrees and +/- 30 degrees were selected to estimate foliage nitrogen density (FND) at upper layer, middle layer and bottom layer, respectively. Three PLS analysis models with FND as the dependent variable and vegetation indices at corresponding angles as the explicative variables were. established. The impact of soil reflectance and the canopy non-photosynthetic materials, was minimized by seven kinds of modifying vegetation indices with the ratio R700/R670. The estimated accuracy is significant raised at upper layer, middle layer and bottom layer in modeling experiment. Independent model verification selected the best three vegetation indices for further research. The research result showed that the modified Green normalized difference vegetation index (GNDVI) shows better performance than other vegetation indices at each layer, which means modified GNDVI could be used in estimating winter wheat nitrogen vertical distribution

[The new method monitoring crop water content based on NIR-Red spectrum feature space].

Moisture content is an important index of crop water stress condition, timely and effective monitoring of crop water content is of great significance for evaluating crop water deficit balance and guiding agriculture irrigation. The present paper was trying to build a new crop water index for winter wheat vegetation water content based on NIR-Red spectral space. Firstly, canopy spectrums of winter wheat with narrow-band were resampled according to relative spectral response function of HJ-CCD and ZY-3. Then, a new index (PWI) was set up to estimate vegetation water content of winter wheat by improveing PDI (perpendicular drought index) and PVI (perpendicular vegetation index) based on NIR-Red spectral feature space. The results showed that the relationship between PWI and VWC (vegetation water content) was stable based on simulation of wide-band multispectral data HJ-CCD and ZY-3 with R2 being 0.684 and 0.683, respectively. And then VWC was estimated by using PWI with the R2 and RMSE being 0.764 and 0.764, 3.837% and 3.840%, respectively. The results indicated that PWI has certain feasibility to estimate crop water content. At the same time, it provides a new method for monitoring crop water content using remote sensing data HJ-CCD and ZY-3.

[Winter wheat GPC estimation based on leaf and canopy chlorophyll parameters].

The present study focused on the wheat harvest grain protein content (GPC) estimation based on wheat leaf and canopy chlorophyll parameters, SPAD and SFR, which were acquired by two hand-held instruments, SPAD and Multiplex 3. The wheat GPC estimate experiment was applied on a wheat field of the Scientific Observation and Experiment Field Station for Precision Agriculture at suburb of Beijing in 2012. The wheat leaf SPAD and canopy SFR value were measured in field for all 110 wheat sample points at five different wheat growth stages from April to June. The wheat plant sample for each point was then collected after the SPAD and SFR measurement and sent to lab for leaf nitrogen content (LNC) and canopy nitrogen density (CND) analysis. Analysis results showed that the correlation coefficients of wheat GPC with wheat CND were much higher than that from wheat tillering stage to early milking stage. They were similar at the wheat middle milking stage. While the wheat leaf SPAD value was highly correlated with wheat LNC at wheat tillering, heading and early milking stage. Wheat canopy chlorophyll parameters SFR were highly correlated with wheat CND at wheat tillering, jointing, heading and milking stage. It can be seen from the study that SFR is more sensitive to the wheat CND compared with wheat LNC. The analysis also indicated that leaf SPAD value at wheat tillering, heading and milking stage was highly correlated with wheat GPC and yield of grain protein (YGP). The wheat canopy parameters, SFR_G and SFR_R were significantly correlated with wheat GPC and YGP at wheat milking stage. Then the optimal GPC and YGP estimation model was established. The R2 of GPC estimation models established by SPAD and SFR_R are 0.426 and 0.497, and the standard errors of the estimate are 0.060% and 0.055%, respectively. The R2 of YGP estimation models established by SPAD and SFR_R are 0.366 and 0.386 and the standard errors of the estimate are 125.367 and 123.454 kg x ha(-1), respectively. The study reveals that SPAD value is a good indicator of single plant activity while SFR_G and SFR_R are better indicators for the wheat group activity. Wheat leaf SPAD value and canopy chlorophyll fluorescence information SFR are all feasible and valuable for GPC estimation before wheat harvesting.

[Estimation of organic matter content of north fluvo-aquic soil based on the coupling model of wavelet transform and partial least squares].

For improving the estimation accuracy of soil organic matter content of the north fluvo-aquic soil, wavelet transform technology is introduced. The soil samples were collected from Tongzhou district and Shunyi district in Beijing city. And the data source is from soil hyperspectral data obtained under laboratory condition. First, discrete wavelet transform efficiently decomposes hyperspectral into approximate coefficients and detail coefficients. Then, the correlation between approximate coefficients, detail coefficients and organic matter content was analyzed, and the sensitive bands of the organic matter were screened. Finally, models were established to estimate the soil organic content by using the partial least squares regression (PLSR). Results show that the NIR bands made more contributions than the visible band in estimating organic matter content models; the ability of approximate coefficients to estimate organic matter content is better than that of detail coefficients; The estimation precision of the detail coefficients fir soil organic matter content decreases with the spectral resolution being lower; Compared with the commonly used three types of soil spectral reflectance transforms, the wavelet transform can improve the estimation ability of soil spectral fir organic content; The accuracy of the best model established by the approximate coefficients or detail coefficients is higher, and the coefficient of determination (R2) and the root mean square error (RMSE) of the best model for approximate coefficients are 0.722 and 0.221, respectively. The R2 and RMSE of the best model for detail coefficients are 0.670 and 0.255, respectively.