Liwen Zhang

Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification

Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions: healthy panicles, empty panicles caused by Nilaparvata lugens St&Oal, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. Principal component analysis (PCA) was performed to obtain the principal component spectra (PCS) of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. Support vector classification (SVC) was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the independent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.

Detecting Agro-Droughts in Southwest of China Using MODIS Satellite Data

Abstract The normalized difference vegetation index (NDVI) has proven to be typically employed to assess terrestrial vegetation conditions. However, one limitation of NDVI for drought monitoring is the apparent time lag between rainfall deficit and NDVI response. To better understand this relationship, time series NDVI (2000–2010) during the growing season in Sichuan Province and Chongqing City were analyzed. The vegetation condition index (VCI) was used to construct a new drought index, time-integrated vegetation condition index (TIVCI), and was then compared with meteorological drought indices-standardized precipitation index (SPI), a multiple-time scale meteorological-drought index based on precipitation, to examine the sensitivity on droughts. Our research findings indicate the followings: (1) farmland NDVI sensitivity to precipitation in study area has a time lag of 16–24 d, and it maximally responds to the temperature with a lag of about 16 d. (2) We applied the approach to Sichuan Province and Chongqing City for extreme drought monitoring in 2006 and 2003, and the results show that the monitoring results from TIVCI are closer to the published China agricultural statistical data than VCI. Compared to VCI, the best results from TIVCI3 were found with the relative errors of −4.5 and 6.36% in 2006 for drought affected area and drought disaster area respectively, and 5.11 and −5.95% in 2003. (3) Compared to VCI, TIVCI has better correlation with the SPI, which indicates the lag and cumulative effects of precipitation on vegetation. Our finding proved that TIVCI is an effective indicator of drought detection when the time lag effects between NDVI and climate factors are taken into consideration.

Comprehensive Suitability Evaluation of Tea Crops Using GIS and a Modified Land Ecological Suitability Evaluation Model

Abstract Tea ( Camellia sinensis ) is one of the most valuable cash crops in southern China; however, the planting distribution of tea crops is not optimal and the production and cultivation regions of tea crops are restricted by law and custom. In order to evaluate the suitability of tea crops in Zhejiang Province, the annual mean temperature, the annual accumulated temperature above 10 °C, the frequency of extremely low temperature below −13 °C, the mean humidity from April to October, slope, aspect, altitude, soil type, and soil texture were selected from climate, topography, and soil factors as factors for land ecological evaluation by the Delphi method based on the ecological characteristics of tea crops. These nine factors were quantitatively analyzed using a geographic information system (GIS). The grey relational analysis (GRA) was combined with the analytic hierarchy process (AHP) to address the uncertainties during the process of evaluating the traditional land ecological suitability, and a modified land ecological suitability evaluation (LESE) model was built. Based on the land-use map of Zhejiang Province, the regions that were completely unsuitable for tea cultivation in the province were eliminated and then the spatial distribution of the ecological suitability of tea crops was generated using the modified LESE model and GIS. The results demonstrated that the highly, moderately, and non-suitable regions for the cultivation of tea crops in Zhejiang Province were 27 552.66, 42 724.64, and 26 507.97 km 2 , and accounted for 28.47%, 44.14%, and 27.39% of the total evaluation area, respectively. Validation of the method showed a high degree of coincidence with the current planting distribution of tea crops in Zhejiang Province. The modified LESE model combined with GIS could be useful in quickly and accurately evaluating the land ecological suitability of tea crops, providing a scientific basis for the rational distribution of tea crops and acting as a reference to land policy makers and land use planners.

Expression of Acc-royalisin gene from Royal Jelly of Chinese honeybee in Escherichia coli and its antibacterial activity.

Royalisin is an antibacterial peptide found in Royal Jelly. Two gene fragments of Chinese honeybee (Apis cerana cerana) head, 280 bp cDNA encoding pre-pro-Acc-royalisin (PPAR) of 95 amino acid residues, and 165 bp cDNA encoding mature Acc-royalisin (MAR) of 51 amino acid residues were cloned into the pGEX-4T-2 vector. They were then transformed individually into Escherichia coli for expression. Two expressed fusion proteins, glutathione S-transferase (GST)-PPAR of 36 kDa and GST-MAR of 32 kDa were obtained, which were cross reacted with GST antibody accounting for up to 16.3% and 15.4% of bacterial protein, respectively. In addition, 41% of GST-PPAR and nearly 100% of GST-MAR were soluble proteins. Both lysates of the two purified fusion proteins displayed antibacterial activities, similar to that of nisin against Gram-positive bacteria strains, Staphylococcus aureus, Bacillus subtilis and Micrococcus luteus. MAR peptide released from the thrombin-cleaved GST-MAR fusion protein has a stronger antibacterial activity than that of GST-MAR fusion protein.

Expression of recombinant AccMRJP1 protein from royal jelly of Chinese honeybee in Pichia pastoris and its proliferation activity in an insect cell line.

Major royal jelly protein 1 (MRJP1) is the most abundant member of the major royal jelly protein (MRJP) family of honeybee. Mature MRJP1 cDNA of the Chinese honeybee (Apis cerana cerana MRJP1, or AccMRJP1) was expressed in Pichia pastoris. SDS-PAGE showed that recombinant AccMRJP1 was identical in molecular weight to the glycosylated AmMRJP1 from the Western honeybee (Apis mellifera). Western blots probed with anti-AccMRJP1 antibody demonstrated that recombinant AccMRJP1 and soluble protein of the Western honeybee RJ (AmSPRJ) contained immunoreactive MRJP1. The 57 kDa protein in AmSPRJ contained an N-terminal amino sequence of N-I-L-R-G-E, which is identical to that previously characterized in AmMRJP1. The molecular weight of recombinant AccMRJP1 was decreased from 57 to 48 kDa after deglycosylation, indicating that AccMRJP1 was glycosylated. The recombinant AccMRJP1 significantly stimulated Tn-5B-4 cell growth, similar to AmSPRJ and fetal bovine serum, and affected cell shape and adhesion to the substrate.

Spatio-temporal reconstruction of air temperature maps and their application to estimate rice growing season heat accumulation using multi-temporal MODIS data *

The accumulation of thermal time usually represents the local heat resources to drive crop growth. Maps of temperature-based agro-meteorological indices are commonly generated by the spatial interpolation of data collected from meteorological stations with coarse geographic continuity. To solve the critical problems of estimating air temperature (Ta) and filling in missing pixels due to cloudy and low-quality images in growing degree days (GDDs) calculation from remotely sensed data, a novel spatio-temporal algorithm for Ta estimation from Terra and Aqua moderate resolution imaging spectroradiometer (MODIS) data was proposed. This is a preliminary study to calculate heat accumulation, expressed in accumulative growing degree days (AGDDs) above 10 °C, from reconstructed Ta based on MODIS land surface temperature (LST) data. The verification results of maximum Ta, minimum Ta, GDD, and AGDD from MODIS-derived data to meteorological calculation were all satisfied with high correlations over 0.01 significant levels. Overall, MODIS-derived AGDD was slightly underestimated with almost 10% relative error. However, the feasibility of employing AGDD anomaly maps to characterize the 2001–2010 spatio-temporal variability of heat accumulation and estimating the 2011 heat accumulation distribution using only MODIS data was finally demonstrated in the current paper. Our study may supply a novel way to calculate AGDD in heat-related study concerning crop growth monitoring, agricultural climatic regionalization, and agro-meteorological disaster detection at the regional scale.

Soil Moisture Monitoring Based on Land Surface Temperature-Vegetation Index Space Derived from MODIS Data

Abstract Soil moisture has been considered as one of the main indicators that are widely used in the fields of hydrology, climate, ecology and others. The land surface temperature-vegetation index (LST-VI) space has comprehensive information of the sensor from the visible to thermal infrared band and can well reflect the regional soil moisture conditions. In this study, 9 pairs of moderate-resolution imaging spectroradiometer (MODIS) products (MOD09A1 and MOD11A2), covering 5 provinces in Southwest China, were chosen to construct the LST-VI space, and then the spatial distribution of soil moisture in 5 provinces of Southwest China was monitored by the temperature vegetation dryness index (TVDI). Three LST-VI spaces were constructed by normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and modified soil-adjusted vegetation index (MSAVI), respectively. The correlations between the soil moisture data from 98 sites and the 3 TVDIs calculated by LST-NDVI, LST-EVI and LST-MSAVI, respectively, were analyzed. The results showed that TVDI was a useful parameter for soil surface moisture conditions. The TVDI calculated from the LST-EVI space (TVDIE) revealed a better correlation with soil moisture than those calculated from the LST-NDVI and LST-MSAVI spaces. From the different stages of the TVDIE space, it is concluded that TVDIE can effectively show the temporal and spatial differences of soil moisture, and is an effective approach to monitor soil moisture condition.

Expression of a bee venom phospholipase A2 from Apis cerana cerana in the baculovirus-insect cell

Bee venom phospholipase A2 (BvPLA2) is a lipolytic enzyme that catalyzes the hydrolysis of the sn-2 acyl bond of glycerophospholipids to liberate free fatty acids and lysophospholipids. In this work, a new BvPLA2 (AccPLA2) gene from the Chinese honeybee (Apis cerana cerana) venom glands was inserted into bacmid to construct a recombinant transfer vector. Tn-5B-4 (Tn) cells were transfected with the recombinant bacmid DNA for expression. Sodium dodecylsulfate-polyacrylamide gel electrophoresis (SDS-PAGE) analysis revealed a double band with molecular weights of 16 and 18 kDa. Products of hexahistidine AccPLA2 fusion protein accumulated up to 5.32% of the total cellular proteins. The AccPLA2 fusion protein was cross reactive with the anti-AmPLA2 (BvPLA2 of the European honeybee, Apis mellifera) polyclonal serum. The reaction resulted in a double glycosylation band, which agrees with the band generated by the native AmPLA2 in Western blot analysis. The PLA2 activity of the total extracted cellular protein in the hydrolyzing egg yolk is about 3.16 μmol/(min·mg). In summary, the recombinant AccPLA2 protein, a native BvPLA2-like structure with corresponding biological activities, can be glycosylated in Tn cells. These findings provided fundamental knowledge for potential genetic engineering to produce AccPLA2 in the pharmaceutical industry.