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[Objectives]This paper aimed to research on the residue behavior of emamectin benzoate and pyriproxyfen in field corn and evaluate the chronic dietary risk, so as to provide suggestions for the rational use of emamectin benzoate and pyriproxyfen on corn. [Methods]In accordance with good agricultural practices(GAP), the field trials of 12% microemulsion(3% emamectin benzoate+9% pyriproxyfen, ME)on corn were carried out across 12 regions of main corn production in China. The formulation of 12% ME was sprayed once at the dosage of 45 g·hm-2 during the incubation period of corn borer eggs, and the recommended pre-harvest interval(PHI)was 21 days. Meanwhile, a method for determination of emamectin benzoate and pyriproxyfen in corn was developed based on QuEChERS and high-performance liquid chromatography-tandem mass spectrometry(HPLC-MS/MS), which was applied to detect the residue levels in field corn samples, and the chronic dietary risk was assessed based on field trial results. [Results]At the spiked levels of 0.01, 0.05, 0.1 and 1 mg·kg-1, the mean recoveries of emamectin B1a and pyriproxyfen in fresh corn, corn kernels and corn straw samples were 87%-106%, with relative standard deviations(RSD)of 2.3%-7.5% and limits of quantification(LOQ)all being 0.01 mg·kg-1. In the fresh corn and corn kernel samples harvested at 21 and 28 days after application, the terminal residue levels of emamectin B1a benzoate were less than 0.011 mg·kg-1, which were lower than the maximum residue limit(MRL)of emamectin benzoate in corn in China(fresh corn:0.05 mg·kg-1; corn kernel:0.05 mg·kg-1), and the terminal residue levels of pyriproxyfen were less than 0.01 mg·kg-1(At present, the MRL of pyriproxyfen on corn had not been set in China). The chronic risk quotients of emamectin benzoate and pyriproxyfen were 78.4% and 5.8%, respectively, which were both lower than 100%, indicating a low risk to consumer's health. [Conclusions]Under the GAP conditions of applying 12% microemulsion(3% emamectin benzoate+9% pyriproxyfen)on corn, the recommended PHI of 21 days was safe. The paper could also offer a reference for China to formulate the MRL of pyriproxyfen on corn. [ABSTRACT FROM AUTHOR]

The Diameter at Breast Height (DBH) (at a height of 1.3 m on the bole of a tree) has been one of the most important indicators during tree measurements in forestry resource inventory. However, the current DBH measurement cannot fully meet the requirement in recent years, due to the low portability, precision, efficiency, applicability, and stability, together with the complex operation, rudimentary software, high costs, and short range. In this study, an innovative device was developed to accurately, efficiently, and conveniently measure the tree DBH suitable for the complex tree shapes and the different diameter classes, while cost-saving in the office-field work survey. The specification of the device was as follows (size: 8.35 cm×5.80 cm×5.55 cm; weight: 230 g; resolution: 0.01 cm; linear range: 0-150 cm; battery capacity: 4 000 mAh input vatage: 3.7 V, output votuge: 5 V; micro-processor chip: STC15W4K48, 8 bits; encoder type: PD-1503-SDI, 12 bits). A Tunnel Magneto-Resistance (TMR) rotary encoder was also combined with the low-cost, small size, and light weight electro-mechanical structure, and high-resolution processing. As such, the measurement device was achieved in the electronization, digitization, portability, and integration of office and filed work for the tree DBH. A supporting system software was also developed, including the embedded software, mobile terminal application, and Web terminal application. In the process of an individual tree survey, the electro-mechanical structure of the device firstly converted the mechanical parameter of tree DBH to the magnetic signal, and then the magnetic signal was converted to an electrical signal. Secondly, the electrical signal was converted into the DBH measurement data using the processing integrated into the embedded software. Thirdly, the operation flow was better applied to measure the trees with special shapes and large diameters using multi-function key combinations. After all individual tree surveys, the DBH measurement data was transmitted by Bluetooth in the device to the Android application, and then uploaded to the database managed by the Web application. The measurement accuracy and operation efficiency of the device were verified to select the 196 standing trees with many tree species and a small sample plot of 42 standing trees in the Botanic Garden of Beijing Forestry University, China. The test results showed that the device presented a higher accuracy to measure the standing trees of different diameter classes than before. The total tree DBH measurement data from different diameter classes (weight: 1 092 g; resolution: 0.001 cm; linear range: 0-500 cm) indicated the mean absolute error (MAE) of 0.08 cm, Mean Absolute Percentage Error (MAPE) of 0.37%, Root Mean Square Error (RMSE) of 0.12 cm, and Relative Root Mean Square Error (RRMSE) of 0.54%, compared with an electronic draw-wire displacement sensor. In addition, a high measurement efficiency was achieved, where the average measurement time per person of each tree was 9.3 s from the efficiency test. The devices demonstrated nearly two times faster than the traditional diameter tape (weight: 42 g; resolution: 0.01 cm; linear range: 0-200 cm), while one time faster than the electronic draw-wire displacement sensor. Additionally, the price of the device was only 260 RMB, due to a 12 bits encoder (price: 135 RMB). In conclusion, this device behaved at a low cost and less labor consumption, fully meeting the technical requirement of accuracy for the Continuous Forest Inventory (CFI) in China. Therefore, the finding can provide broad application prospects in forestry resource inventory. [ABSTRACT FROM AUTHOR]

Objectives: Deep space antennas are widely used in deep space and radio astronomical observations, and their precise position coordinates are required for research needs. In many cases, this precise position is given by very long baseline interferometry (VLBI) based on an initial position with millimeter-scale accuracy. Global positioning system (GPS) alone is merely not sufficient to obtain the initial position of newly-built antennas. Therefore, a method using other objects as reference is needed to calibrate observatory positions, especially antenna arrays, and this paper presents such a method for deep space and radio astronomical observations. Methods: This method employs an azimuth-elevation telescope with an accurate position as a reference object to measure the center position of a target telescope. Other reference objects with satisfying position accuracy can also be used in this method. GPS receivers are adopted to establish a reference network with given scale and orientation by differential measurement. The next step is measuring the local positions of two telescopes (or objective and reference) in the reference network using total station instruments deployed in several different locations. Calculations are induced during the transfer of positions in the reference system to unify data from those measurements. The position of the target to the reference object in a local coordinate system is obtained. The final step is the calculation of the coordinates of the tar⁃ get telescope through the accurate position of the reference telescope. A series of processes are used to con⁃ vert coordinate positions from the local reference system to the international terrestrial reference frame 2014 (ITRF2014) reference system. Results: Experimental measurement was carried out to obtain the coordinates of the 40 m diameter radio telescope in national observatory of China (NAOC) Miyun observatory with the 50 m diameter radio telescope also there as a reference object. Total station measurement was con⁃ ducted at three locations around the reference telescope and four locations around the target telescope. Two reflecting prisms calibrated by GPS devices were used as scale and orientation reference. Total station targets were placed on azimuth and elevation structures of both telescopes. The target position data were obtained when the telescope rotated and tilted at multiple angles. More than 400 times of measurement were taken during a two ⁃ day field experiment. Round ⁃ fitting and sphere ⁃ fitting achieved by the trust-region method were used to process measured points, and the coordinates of the 40 m diameter radio telescope in NAOC Miyun observatory were given with a calibration error of 2.312 mm (root mean square error， RMSE) in the ITRF2014 system. Conclusions: This method provides center point location in mm or sub-mm-scale precision in complex terrain situations with simple tools and low time cost. The measurement of total station instruments towards reference objects or target objects could be the direct or centering measurement. This method can be applied to obtain positions of single or multiple antennas for observation and further calibration. Observatories can apply this method in complex terrain locations such as mountains, rocky fields, and even a meteor crater on an extra-earth planet. Subsequent observations show that this calibration has met the need. [ABSTRACT FROM AUTHOR]

The FAO-56 Penman-Monteith estimates that the ET0 method is widely used, but multiple meteorological data are required for calculation. Developing an alternative method that can provide accurate or at least close to the FAO-56 Penman-Monteith ET0 estimate is one of the research hotspots in the field with as little meteorological data as possible. In this paper, combined with Canonical Correlation Analysis (CCA) and k nearest neighbor algorithm (k-NN), the potential evapotranspiration calculation method (CCA-k-NN) of self-optimizing nearest neighbor algorithm is proposed, and the potential evapotranspiration is realized by using less meteorological data. Estimate. The core idea is to use the CCA algorithm to find the meteorological data most relevant to the potential evapotranspiration, to realize the dimensionality reduction of the meteorological data when the ET0 is estimated later, and then use the k-NN algorithm to estimate the ET0. The Northwest China region was selected as a use case. The regional meteorological data were divided into training dataset, verification dataset and test dataset from time and space scales. The ET0 was estimated by the method in the three types of datasets respectively, and the FAO-56 Penman-Monteith as a reference, evaluated the estimation accuracy and applicability of the CCA-k-NN method. The results show that the CCA-k-NN method has a high correlation with the FAO-56 Penman-Monteith (correlation coefficient greater than 0.9), and has good estimation accuracy. The root mean square error and Mean Absolute Error are both less than 1 mm·d-1. On the spatial scale, the Nash-Sutcliffe coefficient of efficiency is greater than 0.5, and the Nash-Sutcliffe coefficient of efficiency on the time scale is greater than 0.8, the CCA-k-NN method is applicable at both space and time scales. At the same time, compared with other alternative methods, the algorithm has low time complexity O(n), which can effectively reduce the time cost when calculating large amounts of data. The FAO-56 Penman-Monteith estimates that the ET0 method is widely used, but multiple meteorological data are required for calculation. Developing an alternative method that can provide accurate or at least close to the FAO-56 Penman-Monteith ET0 estimate is one of the research hotspots in the field with as little meteorological data as possible. In this paper, combined with Canonical Correlation Analysis (CCA) and k nearest neighbor algorithm (k-NN), the potential evapotranspiration calculation method (CCA-k-NN) of self-optimizing nearest neighbor algorithm is proposed, and the potential evapotranspiration is realized by using less meteorological data. Estimate. The core idea is to use the CCA algorithm to find the meteorological data most relevant to the potential evapotranspiration, to realize the dimensionality reduction of the meteorological data when the ET0 is estimated later, and then use the k-NN algorithm to estimate the ET0. The Northwest China region was selected as a use case. The regional meteorological data were divided into training dataset, verification dataset and test dataset from time and space scales. The ET0 was estimated by the method in the three types of datasets respectively, and the FAO-56 Penman-Monteith as a reference, evaluated the estimation accuracy and applicability of the CCA-k-NN method. The results show that the CCA-k-NN method has a high correlation with the FAO-56 Penman-Monteith (correlation coefficient greater than 0.9), and has good estimation accuracy. The root mean square error and Mean Absolute Error are both less than 1 mm·d-1. [ABSTRACT FROM AUTHOR]

This paper introduces a method which utilizes global navigation satellite system (GNSS) based precision point positioning (PPP) for conducting long-term tilt and settlement monitoring of high-rise buildings in North China. Two-year continuous GNSS observations from two high-rise buildings near a deep foundation pit in Beijing are used for the case study. The method comprises four steps: ① Calculating the geocentric coordinates (XYZ) of the GNSS antenna with respect to a global reference frame (e.g., IGS08) with the PPP method; ② transforming the geocentric coordinates to a regional reference frame − the North China reference frame (NChina16); ③ converting the geocentric XYZ coordinates to a station-centric East -North- Up (ENU) coordinates; ④ removing seasonal ground-surface deformation from the ENU time series and restoring the true displacements (tilt and settlement) of the building. Experimental result indicates that the PPP method is able to achieve 2-3 mm horizontal accuracy(root mean square error) and 6-9 mm vertical accuracy (root mean square error)for 24 - hour continuous observations in North China. The combination of PPP, NChina16, and the North China seasonal ground deformation model can perform millimeter-accuracy, nearreal time, and long term static structural health monitoring of high-rise buildings and other large-scale infrastructure projects in North China. [ABSTRACT FROM AUTHOR]

Estimating soil salinity is imperative for scheduling irrigation and remediating saline soil but difficult at large scales. Remote sensing can bridge this gap because of its advantages in low cost and large-area coverage; it has become an efficient method for assessing soil salinization in field. One issue in use of remote sensing to assess saline soil is the presence of plastic film mulch and bare soil because of their difference in reflecting waves in the spectral bands. In order to investigate the effect of plastic film mulch on soil salinity inversion using UAV multispectral remote sensing, we studied four plots with plastic film mulch at the Shahaoqu Irrigation area in the Hetao Irrigation District, Inner Mongolia of China. From each plot, we took soil samples and measured their salt contents from May to July. We also flew a drone to simultaneously take multispectral images of the sampling sites and extracted the spectral reflectance to calculate the spectral indices. Correlation analysis found that the S4, S6, SI1, SI2, SI3 and BI indices can be used to calculate soil salinity. Six-band spectral reflectances and six spectral indices obtained from different datasets were used as independent variables to calculate the salt content with the support vector machine (SVM), the back propagation neural network (BPNN) and the extreme learning machine (ELM), respectively, before and after the mulch film was removed. We compared the three models based on their determination coefficient (R²), root mean squared error (RMSE) and relative error (RE). The results showed that plastic film mulch did impact on soil salinity inversion. Although all three models could adequately estimate the soil salt contents before and after the film removal, they worked better after the film removal than before the film removal. Models based on the spectral indices were more accurate than those based on the spectral reflectances, and the accuracy of the inversely calculated salt content varied with sampling time and treatment. The inversion results based on monthly data differed from those based on by pooling all data. After the film was removed in June, the salt content estimated using the model was most accurate, with its associated R² and RMSE being 0.695 and 0.182 respectively for the spectral reflectance-based method, and 0.663 and 0.191respectively for the spectral indices-based method. The salt content estimated by BPNN was least accurate in May, with its associated R² and RMSE being 0.766 and 0.161 respectively for the spectral reflectance-based method, and 0.769, 0.162 respectively for the spectral indices-based method. Comparison of the three models revealed that ELM was most accurate, followed by SVM and BPNN, although their errors were within the tolerable range. In summary, this paper provides an effective method to inversely calculate soil salinization at large mulched farmland using UAV multispectral remote sensing. [ABSTRACT FROM AUTHOR]

GPT2w model is commonly used to calculate the meteorological parameters at certain location, such as temperature, weighted mean temperature, pressure and vapor pressure. It is also the public empiri- cal model for tropospheric delay with the best nominal accuracy. In this paper, meteorological sounding da- ta from 2013—2015 of 86 stations in China is used, which have participated the global meteorological ex- change. Precisions of meteorological parameters from GPT2w are examined and analyzed. It turns out that the average bias (Bias) and root mean square error (RMS) of temperature are 1.31 ℃ and 3.62 ℃ , respec- tively. For weighted mean temperature, the Bias is －1.58 K and the RMS is 4.07 K. For pressure and va- por pressure, the absolute values of Bias are smaller than 1 hPa, and the RMS are 6.98 hPa and 3.04 hPa, respectively. Using the data from 2006—2015, periodic characterization of the accuracy of different latitude models are analyzed. It turns out that the RMS of temperature, weighted mean temperature, pressure and vapor pressure shows certain periodic patterns, and differs with different latitude regions. In general, GPT2w model exhibits high precision and stability within the area of China. [ABSTRACT FROM AUTHOR]

Tomato has become the most cultivated and consumed vegetable crop in the world, and China has become one of the largest producers and consumers of tomatoes in the world. The pectin content in cell wall of tomato during ripening is closely related to fruit hardness, which is one of the important indicators to determine the maturity and reflect the quality of tomato. The requirement of tomato maturity classification and evaluation promotes the development of non-destructive, fast and accurate detection methods of tomato hardness.Hyperspectral imaging integrates spectroscopy and imaging technology in an analysis system, which transfers tomato maturity assessment from subjective, manual classification and measurement methods. Hyperspectral imaging has been widely used in the rapid acquisition of information to classify, detect or identify the quality of various fruits. A novel method for tomato hardness detection based on hyperspectral imaging and modified interval Random Frog was proposed in this paper. Firstly, hyperspectral images of 120 tomato samples in different mature periods were captured by hyperspectral imaging system covering near-infrared region (865.11nm-1 711.71nm). And the hardness data of tomato was obtained by texture analyzer. Secondly, the spectral data were pretreated by multiplicative scatter correction (MSC) and normalized preprocessing to eliminate noise and improve signal-to-noise ratio. The validity of the characteristic wavelength plays a crucial role in the prediction performance of the model. Therefore, we need an effective method to extract the effective wavelength to improve the accuracy of the model. Interval random frog (iRF) algorithm considers all possible spectral wavelengths and ranks all the wavelengths based on selected probability. But one of the disadvantages of this method is large number of iterations and slow convergence. In view of above disadvantages, the traditional iRF algorithm was optimized in terms of constructing initial variable subset method. A modified interval Random Frog (miRF)was proposed to extract the characteristic wavelength effectively. Finally, a prediction model was developed based on partial least squares regression (PLSR) method to detect tomato hardness. The results indicated that the convergence efficiency and accuracy of miRF has a significantly improvement compared with the iRF method. The iRF has selected 100 feature bands, accounting for 40.65% of the full band, and its runtime was 32.1min. miRF has selected 47 feature bands, accounting for 19.1% of the full band, and its runtime was 1.6 min. It can be seen that miRF greatly reduces the running time of the algorithm. The characteristic wavelengths selected by iRF and miRF methods were mainly distributed in 1 582 nm-1 655 nm, followed by 1 160 nm-1 190 nm and 1 353 nm-1 383 nm, indicating that above regions were sensitive bands to tomato hardness. In order to prove the effectiveness of the proposed algorithm, the results of miRF-PLSR were compared with those of iRF-PLSR and SPA-PLSR. The prediction set correlation coefficients (RP) of the SPA-PLSR model and the iRF-PLSR model were 0.803 9 and 0.936 6 respectively. And the RP of miRF-PLSR model was 0.968 5. The root mean square error (RMSEP) of the SPA-PLSR model and the iRF-PLSR model were 0.007 7 kg/mm2 and 0.004 4 kg/mm2respectively. And the RMSEP of miRF-PLSR model was 0.004 0 kg/mm2. The experiments results show that the miRF-PLSR model has the best prediction results in all models. [ABSTRACT FROM AUTHOR]

Alfalfa plays an important role in dairy farms for economic and ecological reasons in northwest China. In order to manage the complex relations between alfalfa growth and environmental issues accurately and timely, the alfalfa simulation model has been widely studied. An alfalfa growth simulation model based on water and nitrogen factors (ALFSIM-WN) was proposed. The model was divided into 3 sub-models, including crop dynamic sub-model, water balance sub-model and nitrogen balance sub-model using modular design method. It took alfalfa in Ningxia irrigation area of Yellow River as research object and could simulate the growth dynamics of alfalfa under different water supply and variable fertilization. It also could estimate the yield of alfalfa. In order to verify the accuracy and applicability of ALFSIM-WN, experiments were carried out at grassland experiment area of Maosheng Grass Limited Company in Yinchuan (northwest China, 3855N, 10606 E). The experimental alfalfa was Medicago sativa No.7. The irrigation methods included surface irrigation and subsurface drip irrigation. A split plot was designed as 2 experimental treatments. The 1st treatment was the irrigation amount which was divided into 5 irrigation levels including the surface irrigation (1 199 mm) and the subsurface drip irrigation (525, 600, 675, 750 mm). The 2nd treatment was nitrogen application rate which included 4 nitrogen application levels (0, 60, 120, 180 kg/hm2). A total of 17 treatments were designed in experiment. Different irrigation levels were carried out in batches according to different cuts and different growth period of alfalfa. Different nitrogen application levels were carried out in different cuts. Through 2 years experiments (2016-2017), model parameters had been determined using data from 2016, and growth parameters of alfalfa (such as growth period, leaf area index, soil moisture dynamics and yield) had been predicted based on gathering meteorological date, soil data and field management data of 2017. The comparison between simulated and observed was taken in this paper. The results showed that the simulated values by the model was in agreement with the trend of the observed values under different irrigation and nitrogen treatments, such as growth period, leaf area index, soil water content and yield of the alfalfa. Alfalfa in Ningxia irrigation area of Yellow River could harvest 3 to 4 times per year, the mean relative error of the growth period of alfalfa in 4 cuts simulated by the model was between 1.9% and 5.7%, which was consistent with the local growth period for forage. In addition, through analyzing the 4 cuts data in 2017, the mean relative error of leaf area index simulated by the model was between 2.3% and 17.6%, the mean relative error of soil water content simulated by the model was between 2.3% and 17.6%, and the mean relative error of yield simulated by the model was between 1.7% and 16.2%. The root mean square error of the leaf area index was between 0.09 and 0.44, the root mean square error of soil water content is between 0.009 and 0.039 cm3/cm3, and the root mean square error of yield was between 0.3 and 2.3 t/hm2. Therefore, the ALFSIM-WN had higher simulation accuracy in simulated growth period, leaf area index, soil water content and yield, indicating that the model has good application in Ningxia irrigation area of Yellow River and can be used as an effective simulation tool for alfalfa growth in forage planting. [ABSTRACT FROM AUTHOR]

The paper setted two scenarios of A and B based on a model of multivariate empirical probability distributions (MVE) to forecast China's cereals production in a short run. Four measures of the mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and Theil's inequality coefficient (Theft IC) were used to test ex post simulation fit. Result indicates that the MVE model's short-term prediction accuracy is better simulated, and if the deadline of historical data is closer to the starting time, the statistic fit is better. The paper also finds that according to historical data from 2000 to 2008 (B scenario), the simulated mean values of China's cereals production in 2010 and 2011 are 532.88 million tons and 538.87 million tons, and the standard deviation values are 19.67 million tons and 20.01 million tons, respectively. [ABSTRACT FROM AUTHOR]