Soybean Leaf Samples Research Articles

Unravelling Species Diversity and Pathogenicity of Fusarium spp. Associated with Soybean Leaf and Root in Heilongjiang Province, China.

Soybean (Glycine max L.) holds significant global importance and is extensively cultivated in Heilongjiang Province, China. Soybean can be infected by Fusarium species, causing root rot, seed decay, stem rot, and leaf blight. In 2021 to 2022, a field survey of soybean diseases was carried out in 11 regions of Heilongjiang Province, and 186 soybean leaves with leaf blight symptoms and 123 soybean roots with root rot symptoms were collected. Unexpectedly, a considerable number of Fusarium isolates were obtained not only from root samples but also from leaf samples. A total of 584 Fusarium isolates (416 from leaves and 168 from roots) were obtained and identified as 18 Fusarium species based on morphological features and multilocus phylogenetic analyses with tef1 and rpb2 sequences. Fusarium graminearum and Fusarium sp. 1 in FOSC were the dominant species within soybean leaf and root samples, respectively. Pathogenicity tests were conducted for all Fusarium isolates on both soybean leaves and roots. Results showed that F. graminearum, F. ipomoeae, F. citri, F. compactum, F. flagelliforme, F. acuminatum, and F. sporotrichioides were pathogenic to both soybean leaves and roots. F. solani, F. avenaceum, F. pentaseptatum, F. serpentinum, F. annulatum, and Fusarium sp. 1 in FOSC were pathogenic to soybean roots, not to leaves. To our knowledge, this is the first study to thoroughly investigate soybean-associated Fusarium populations in leaves and roots in Heilongjiang Province.

Fast and low-cost method for direct and simultaneous determination of nitrogen and carbon in soybean leaves using benchtop and portable near-infrared devices.

The current techniques for determining carbon and nitrogen content to provide information about the nutritional status of plants are time-consuming and expensive. For this reason, the objective of this study was to develop an analytical method for the direct and simultaneous determination of nitrogen and carbon elemental content in soybean leaves using near-infrared spectroscopy and compare the performance of conventional (1100-2500 nm spectral range) and portable equipment (1100-1700 nm spectral range). Partial least-squares regression models were developed using 27 soybean leaf samples collected during the 2021 harvest and applied for the simultaneous determination of carbon and nitrogen in 13 samples collected during the 2022 harvest. The root-mean-square error of prediction values for nitrogen and carbon were low (2.42 g kg-1 and 4.37 g kg-1 respectively) for the benchtop method yielded low but higher for the portable method (3.82 g kg-1 and 10.7 g kg-1 respectively). The benchtop method did not show significant differences when compared with the reference method for determining nitrogen and carbon. In contrast, the portable methodology showed potential as a screening method for determining nitrogen levels, particularly in fieldwork. The methodologies evaluated in this study were implemented and evaluated under real crop monitoring conditions, using independent sets of calibration and prediction samples. Their utilization enables the acquisition of cost-effective, safe analytical data aligning with the principles of green analytical chemistry. © 2023 Society of Chemical Industry.

A loop-mediated isothermal amplification assay for the rapid diagnosis of soybean rust caused by Phakopsora pachyrhizi

Soybean rust caused by the fungus Phakopsora pachyrhizi is one of the most destructive diseases to soybean production worldwide. For the rapid diagnosis and the control of soybean rust, we developed a loop-mediated isothermal amplification (LAMP) assay that targets a P. pachyrhizi-specific gene (Phapa_6409908) identified from a comparative genomic analysis of 61 Pucciniomycotina strains. Using a set of screened primers and the optimized reaction conditions of 62°C for 70 min, the LAMP assay can detect P. pachyrhizi while excluding other plant pathogens. The assay consistently showed high sensitivity in detecting low contents of P. pachyrhizi DNA (10 pg). We confirmed the practical application of this LAMP assay in diagnosing soybean rust using soybean leaf samples collected from fields in four cities of Sichuan Province, China, in 2020 and 2021. The developed LAMP assay provides a specific, sensitive, and rapid method for the diagnosis of soybean rust caused by P. pachyrhizi, which can assist in the prediction, prevention, and control of this major soybean disease.

Elimination of Leaf Angle Impacts on Plant Reflectance Spectra Using Fusion of Hyperspectral Images and 3D Point Clouds

During recent years, hyperspectral imaging technologies have been widely applied in agriculture to evaluate complex plant physiological traits such as leaf moisture content, nutrient level, and disease stress. A critical component of this technique is white referencing used to remove the effect of non-uniform lighting intensity in different wavelengths on raw hyperspectral images. However, a flat white tile cannot accurately reflect the lighting intensity variance on plant leaves, since the leaf geometry (e.g., tilt angles) and its interaction with the illumination severely impact plant reflectance spectra and vegetation indices such as the normalized difference vegetation index (NDVI). In this research, the impacts of leaf angles on plant reflectance spectra were summarized, and an improved image calibration model using the fusion of leaf hyperspectral images and 3D point clouds was built. Corn and soybean leaf samples were imaged at different tilt angles and orientations using an indoor desktop hyperspectral imaging system and analyzed for differences in the NDVI values. The results showed that the leaf's NDVI largely changed with angles. The changing trends with angles differed between the two species. Using measurements of leaf tilt angle and orientation obtained from the 3D point cloud data taken simultaneously with the hyperspectral images, a support vector regression (SVR) model was successfully developed to calibrate the NDVI values of pixels at different angles on a leaf to a same standard as if the leaf was laid flat on a horizontal surface. The R-squared values between the measured and predicted leaf angle impacts were 0.76 and 0.94 for corn and soybean, respectively. This method has a potential to be used in any general plant imaging systems to improve the phenotyping quality.

Reducing matrix effects on the quantification of Ca, Mg, and Fe in soybean leaf samples using calibration-free LIBS and one-point calibration

Matrix effects are one of the most difficult analytical challenges in laser-induced breakdown spectroscopy (LIBS), since signal intensity depends on both element concentration and matrix. For instance, the measurement of nutrients in plant leaves using LIBS is notably impacted by matrix effects because of differences in fertilizer application, weather, soil properties, and health levels. Thus, to overcome matrix effects on the quantification of nutrients in plant leaves, we propose the use of CF-LIBS, in which the intensity of emission lines is corrected using the one-point calibration (OPC) method. In this study, we analyzed thirty soybean leaf samples of cultivars from various locations and health levels with 0.5 wt% TiO2 (internal standard), for which the matrix effects were visible in the calibration curves. Normalization using a Ti II line increased the average r2 of the calibration curves for Ca II, Mg II, and Fe II from 0.24 to 0.73, but this was insufficient to adjust for the variations in plasma temperature and for the degree to which the elements were ionized. With the proposed models and using just one sample to calibrate the OPC factors, we obtained an accuracy >92% and an average r2 of 0.87 for the quantification of Ca, Mg, and Fe in relation to the atomic absorption spectroscopy (AAS). We then concluded that CF-LIBS with the OPC corrections might mitigate matrix effects in LIBS analysis of soybean leaf samples. More studies are needed to assess whether the OPC factors calibrated with soybean leaf samples can be applied to other species.

Direct determination of nutrient elements in plant leaves by double pulse laser-induced breakdown spectroscopy: evaluation of calibration strategies using direct and inverse models for matrix-matching.

This study aims to develop a single calibration model to determine nutrient elements directly (Ca, Mg, Mn, and P) in soybean and sugar cane leaf samples by double pulse laser-induced breakdown spectroscopy (DP LIBS). Matrix-matching calibration (MMC) was evaluated using direct and inverse models. Forty-five samples were used to build the calibration model (23 soybean leaves and 22 sugar cane leaves), and fifteen were used for the prediction test (8 soybean leaves and 7 sugar cane leaves) models. In the direct model, the analyte concentration in the sample is the independent variable, and the analytical signal is the dependent variable. In the inverse model, the analytical signal is the independent variable, and the analyte concentration in the sample is the dependent variable. In general, both models presented satisfactory results; however, the inverse model performed better. Emission lines used to propose calibration models were selected using a linear Pearson's correlation (R) strategy between each spectral point and the Ca, Mg, Mn, and P concentration measured by reference methods using inductively coupled plasma optical emission spectrometry (ICP OES). The root mean square errors of prediction (RMSEP) for the direct models were 0.60 g kg-1 to (Ca), 0.47 g kg-1 (Mg), 9.3 mg kg-1 to (Mn), and 0.28 g kg-1 to (P); for inverse model was 0.55 g kg-1 to (Ca), 0.39 g kg-1 (Mg), 10.5 mg kg-1 to (Mn), and 0.21 g kg-1 to (P). The calibration strategies proposed in this study may minimize matrix effects in direct solid analysis in soybean and sugar cane leaf samples, performing the determination of Ca, Mg, Mn, and P by DP LIBS using a single calibration model.

Primeiro registro de Rachiplusia nu (Guenée, 1852) (Lepidoptera: Noctuidae) sobrevivendo em soja Bt no Brasil

Loopers are important defoliators in several crop species. Among the loopers attacking non-Bt soybean cultivars, Chrysodeixis includens (Walker, [1858]) was the most common. The use of trangenic Bt soybean in appromately 70% of the soybean fields in Brazil led to a decrease in the loopers in these fields. However, technical field specialists and soybean producers recently observed Plusiinae larvae damaging transgenic Bt soybean, posing a risk over the Bt technology. Therefore, the aim of this study was to identify and confirm the catterpillar species feeding on transgenic Bt soybean fields in Brazil. Larvae and soybean leaf samples were collected in 12 soybean-producing locations from four Brazillian States. Soybean leaf samples were tested to confirm whether they were transgenic Bt. Larvae were reared in artificial diet and submitted to feeding trials on non-transgenic and transgenic Bt soybean leaves. The species identification was perfomed by amplicon sequencing of the cytochrome oxidase I (COI) gene of eight larvae per local. All 12 populations fed in the transgenic Bt soybean and the sequencing analysis of COI determined that they belong to Rachiplusia nu (Guenée, 1852) with 100% similarity to the COI sequences available in NCBI for this species. These results suggest that the R. nu species is not controlled by the genetic modification event present in the tested soybean variety and highlights the need for additional looper management control strategies, such as insecticide application, for controlling this insect pest in transgenic Bt soybean.

Weather based rules for yellow mosaic disease prediction on soybean in Madhya Pradesh

The yellow mosaic disease (YMD) found to be one of the major constraints in soybean production in Madhya Pradesh. For timely management of this disease and for reducing the calendar based prophylactic pesticides spray, this study was conducted to screen out critical weather limits for peak YMD incidence. Field experiments as well as farmers field survey were conducted during kharif seasons of 2015 to 2019 in four districts viz. Tikamgarh, Chhatarpur, Datia and Jabalpur of Madhya Pradesh to record spacio-temporal variability of whitefly and YMD on soybean. Isolation of DNA from the infected soybean leaf samples was executed as per the standardized protocol of DNA isolation and molecular level identification of mungbean yellow mosaic India virus (MYMIV) was carried out as well. It was noted that the preceding week’s weather conditions significantly influenced the peak YMD incidence during the study periods. It was observed that maximum temperature, sunshine hours, number of whitefly population were positively related with YMD at the significant level; while the rainfall, evening relative humidity were negatively related. A weather based rule was worked out for peak prediction of YMD in soybean during the previous two weeks period when the following conditions were satisfied: Whitefly population per plant was above 12, maximum temperature was above 33°C and passive phase of monsoon was for more than a week. The transmission rate was found to be higher when dry period coincide with maximum temperature above 33°C for greater than or equal to 5days. This weather based rule for peak YMD prediction was also validated.

Amplicon Sequencing Reveals Extensive Coinfections of Foliar Pathogens in Soybean.

Soybean (Glycine max) is one of the most economically important crops grown in North America and in other regions worldwide. However, the plant is susceptible to a variety of foliar pathogenic microorganisms, some of which are a significant threat to production. Several molecular and serological approaches are currently available to diagnose plant pathogens, but all have limitations including their capability to accurately detect coinfections of individual plants. We therefore used 16S and internal transcribed spacer amplicon sequencing to identify the suite of bacterial and fungal organisms infecting 96 soybean leaf samples collected throughout southern Manitoba, Canada, at two growth stages (V2/3 and R6). We were able to confirm the presence of pathogens previously known to our sampling regions, such as Septoria glycines, Alternaria alternata, and Pseudomonas spp. Importantly, we found that most of plants were infected by more than one putative pathogen, with 64% of V2/3 and 26% of R6 plants infected by three or more pathogens. Amplicon sequencing also indicated the presence of residual pathogens that infect crops other than soybean, as well as nonfoliar pathogens and nonpathogenic microorganisms. We discuss some of the benefits and drawbacks of using amplicon sequencing to detect foliar pathogens of soybean.

Soybean Viromes in the Republic of Korea Revealed by RT-PCR and Next-Generation Sequencing.

Soybean (Glycine max L.) is one of the most important crop plants in the Republic of Korea. Here, we conducted a soybean virome study. We harvested a total of 172 soybean leaf samples showing disease symptoms from major soybean-growing regions in the Republic of Korea. Individual samples were examined for virus infection by RT-PCR. Moreover, we generated eight libraries representing eight provinces by pooling samples and four libraries from single samples. RNA-seq followed by bioinformatics analyses revealed 10 different RNA viruses infecting soybean. The proportion of viral reads in each transcriptome ranged from 0.2 to 31.7%. Coinfection of different viruses in soybean plants was very common. There was a single dominant virus in each province, and this geographical difference might be related to the soybean seeds that transmit viruses. In this study, 32 viral genome sequences were assembled and successfully used to analyze the phylogenetic relationships and quasispecies nature of the identified RNA viruses. Moreover, RT-PCR with newly developed primers confirmed infection of the identified viruses in each library. Taken together, our soybean virome study provides a comprehensive overview of viruses infecting soybean in eight geographical regions in the Republic of Korea and four single soybean plants in detail.

Soybean Yield Response to Phosphorus Fertilization in an Oxisol under Long‐Term No‐Till Management

Core Ideas Plant available P extraction methods are compared The P accumulation in a long‐term NT system depends on P application rates Soil test is a good guide for fertilization to achieve optimum yields and minimum P buildup in the soil P content in soybean leaf samples is an indicator of soybean yield Phosphorus (P) fertilizer recommendations for crops must be improved to optimize crop yields and minimize the environmental impact of P. We evaluated the effects of continuous annual fertilization (double crops) on soil‐P availability by Mehlich‐1 (PM‐1) and resin (Presin), plant uptake, P responsive concentrations in the soil and soybean trifolioliate leaflets and the yields of two soybean (Glycine max L.) cultivars (BRS1010 and BRS1003) grown in a Brazilian Oxisol under 20 yr of no‐till (NT) management. Four treatments representing a combination of annual P rates (0, 20, 40, and 50 kg P ha–1) with a fixed K rate (40 kg ha–1) were evaluated. Resin‐extractable P was highly correlated with PM‐1 (0.86, p < 0.05) but Presin was nearly 2.5 times as much as PM‐1. The soil extractable P was linearly correlated with P contents in trifoliolate leaflets with r of 0.72 and 0.66 (both at p < 0.05) for PM‐1 and Presin, respectively. Maximum responsive concentrations of soil PM‐1 and Presin for the year studied were in the range of 13 ± 2.2 to 20 ± 5.7 and 30 ± 5 to 59 ± 19.2 mg P dm–3, respectively. Soybean yields increased 29% as the rates of annual‐P rate increased from 0 to 20 kg ha–1 and additional P application did not further improve yields. The trifoliolate leaflet P concentrations of both cultivars were correlated (r = 0.66, p < 0.05) with soybean yields. The maximum responsive concentration of P in the soybean trifoliolate was 2.8 ± 1.9 to 5.4 ± 2.8 g kg–1 at R2 stage. Our results confirm that soil test for plant available P and plant tissue test are important to guide P fertilizer recommendations.

First Report of Soybean yellow mottle mosaic virus on Soybean (Glycine max) in India

Soybean (Glycine max) is one of the most important crops as a source of high-quality protein and oil. In India, soybean area has expanded with the introduction of high-yielding varieties with wide adaptability, which also favored the development of new diseases with increased incidence. During 2016, 38 samples from initial varietal trials of soybean cultivars from India exhibiting symptoms such as mosaic, mottling, necrosis, and puckering were collected from the fields of the Indian Agricultural Research Institute, New Delhi, India, and tested for the presence of viruses, namely, Cowpea mild mottle virus, Groundnut bud necrosis virus, Soybean mosaic virus, Soybean vein necrosis virus, Soybean yellow mottle mosaic virus (SYMMV), and Tobacco streak virus. Crude sap from 3 of the 38 samples strongly reacted with the polyclonal antibodies raised against SYMMV in direct antibody coating ELISA (A₄₀₅ₙₘ= 1.2 to 1.8) (Sandra et al. 2015). To confirm the SYMMV infection, total RNA was extracted from the ELISA-positive soybean leaf samples showing mosaic and mottling symptoms using the SV Total RNA isolation system (Promega, Madison, WI) followed by reverse-transcription polymerase chain reaction (RT-PCR) with coat protein (CP) gene specific primers (forward, 5′-ATGAATTCATGAATGGAACAATGCTCACCGT-3′; reverse, 5′-CAAAGCTTAGTGTGTGGGTTTTAACTGGCGTTGTTA-3′) (Sandra et al. 2015). The RT-PCR product of 1,065 base pairs was cloned into TA cloning vector (Real Biotech Corporation), and the clones were sequenced in both orientations and deposited in GenBank (accession no. MG768974). Sequence analysis of the CP gene showed 98 to 100% identity with Indian isolates (KR260903, KR260902, and KP259608) followed by 82 and 92% similarity with Korean isolates of SYMMV at nucleotide and amino acid levels, respectively. Identity at the nucleotide and amino acid level of 29 to 42% and 16 to 25% was observed with the corresponding CP region of other carmoviruses. For further confirmation, French bean seedlings (cv. Pusa Parvati) were mechanically sap inoculated with an extract from an ELISA-positive soybean leaf sample, and these seedlings showed systemic chlorotic blotches after 10 to 12 days postinoculation, which were then confirmed by ELISA and RT-PCR as above. Previously, SYMMV was reported on soybean from South Korea and North America (Li et al. 2009; Nam et al. 2009). More recently, SYMMV was reported on mungbean and urdbean from India (Sandra et al. 2015). To our knowledge, this is the first report of natural infection of soybean by SYMMV in India and suggests that this newly identified SYMMV may be adapting to other leguminous host plant species.

First Report of Tomato spotted wilt virus Infecting Soybean in Korea

The soybean (Glycine max (L.) Merr.) is a legume with worldwide popularity which is native to East Asia. Soybeans are grown for their edible beans and used to create soy vegetable oil and fermented soy foods. To date, a large number of viruses that can infect soybeans have been identified (Hill et al. 2014). Tomato spotted wilt virus (TSWV) is a member of the genus Tospovirus in the family Bunyaviridae. TSWV causes serious diseases in many economically important plants including dicots and monocots. TSWV has been identified in soybeans in Iran (Golnaraghi et al. 2001) and Georgia (Nischwitz et al. 2006). In 2016, we collected soybean leaf samples displaying viral disease symptoms such as chlorotic, mosaic, mottling, stunting, and yellowing. 168 samples were collected and categorized by the region that they were collected in. Eight different libraries representing individual provinces were prepared for RNA sequencing using the TruSeq RNA Library Prep Kit v2 in accordance with the manufacturer's instruction...

Molecular characterization of a new soybean-infecting member of the genus Nepovirus identified by high-throughput sequencing.

The complete nucleotide sequence of a new soybean-infecting member of the genus Nepovirus (provisionally named "soybean latent spherical virus" [SLSV]) was identified by high-throughput sequencing of RNAs from soybean leaf samples from North Dakota, USA. The sequences of RNAs 1 (8,190nt) and 2 (5,788nt) were completed by rapid amplification of cDNA ends. Each contained a single long open reading frame and a 3' nontranslated region of greater than 1,500 nt. The predicted amino acid sequences of the two ORFs were most closely related to nepoviruses in subgroup C. Full-length cDNAs of RNAs 1 and 2 were cloned and used to inoculate soybean plants, which did not display obvious symptoms. These results suggest that SLSV represents a new species in the genus Nepovirus.

Reprint of “Novel mycoviruses discovered from metatranscriptomics survey of soybean phyllosphere phytobiomes”

Mycoviruses can be beneficial to plants in that they can debilitate pathogenic fungi thereby reducing the severity of associated plant diseases. Studies to date have focused primarily on culturable fungi that represent a fraction of natural fungal populations. The nonculturable fungi, however, can harbor diverse populations of mycoviruses that reduce plant disease or enhance resistance to abiotic stress. Metatranscriptome analysis of field-grown plant samples using high-throughput sequencing offers the possibility of unbiased detection and quantification of mycoviruses regardless of the culturability of their fungal hosts together with the complete associated microbial consortia. In this study, we describe the fungal viromes of the phyllosphere of production soybean fields in Illinois, USA by analyzing the metatranscriptomes of thousands of soybean leaf samples collected during the 2008 and 2009 growing seasons. The analyses identified 25 partial genome sequences that represented at least 22 mycovirus genomes, only one of which had been described previously. The novel mycovirus genomes showed similarity to 10 distinct lineages including the genera Alphapartitivirus, Botybirnavirus, Endornavirus, Mitovirus, Mycoflexivirus, Ourmiavirus, Totivirus, Victorivirus, family Tombusviridae, order Mononegavirales, and the recently proposed genus Gemycircularvirus. The present study adds to the wealth of mycoviruses associated with plant phytobiomes and establishes groundwork needed for further characterization of the viruses.

Molecular Diversity Analysis of Coat Protein Gene Encoded by Legume Begomoviruses and PCR Assay to Detect Yellow Mosaic Viruses Infecting Soybean in India

Aim: Coat protein (CP) genes encoded by Legume yellow mosaic viruses (LYMVs) were analysed to study molecular diversity and to devise effective PCR based assay to distinguish major Begomovirus species ( Mungbean yellow mosaic India virus and Mungbean yellow mosaic virus ) infecting soybean Design of the Study: All the known coat protein gene sequences encoded by begomoviruses causing yellow mosaic disease (YMD) in legumes were obtained from GenBank. YMD infected soybean leaf samples were collected from different parts of India during Kharif 2012 and species of virus infections identified using CP gene based primers in a PCR assay.

Novel mycoviruses discovered from metatranscriptomics survey of soybean phyllosphere phytobiomes

Mycoviruses can be beneficial to plants in that they can debilitate pathogenic fungi thereby reducing the severity of associated plant diseases. Studies to date have focused primarily on culturable fungi that represent a fraction of natural fungal populations. The nonculturable fungi, however, can harbor diverse populations of mycoviruses that reduce plant disease or enhance resistance to abiotic stress. Metatranscriptome analysis of field-grown plant samples using high-throughput sequencing offers the possibility of unbiased detection and quantification of mycoviruses regardless of the culturability of their fungal hosts together with the complete associated microbial consortia. In this study, we describe the fungal viromes of the phyllosphere of production soybean fields in Illinois, USA by analyzing the metatranscriptomes of thousands of soybean leaf samples collected during the 2008 and 2009 growing seasons. The analyses identified 25 partial genome sequences that represented at least 22 mycovirus genomes, only one of which had been described previously. The novel mycovirus genomes showed similarity to 10 distinct lineages including the genera Alphapartitivirus, Botybirnavirus, Endornavirus, Mitovirus, Mycoflexivirus, Ourmiavirus, Totivirus, Victorivirus, family Tombusviridae, order Mononegavirales, and the recently proposed genus Gemycircularvirus. The present study adds to the wealth of mycoviruses associated with plant phytobiomes and establishes groundwork needed for further characterization of the viruses.

Transcriptomics analyses of soybean leaf and root samples during water-deficit.

Drought being a major challenge for crop productivity and yield affects multigenic and quantitative traits. It is also well documented that water stress shows a cross talk with other abiotic stresses such as high temperature and high light intensities (Tripathi et al., 2013) [1]. In this report, we documented the details of the methods and quality controls used and considered in our time course-based transcriptome profile of soybean plants under water deficit conditions using microarray technology. The findings of this study are recently published by the Rushton lab in BMC Genomics for a comparative study of tobacco and Soybean (Rabara et al., 2015) [2]. The raw microarray data set is deposited in GEO database with accession number GSE49537.

Determining the Effects of Storage on Cotton and Soybean Leaf Samples for Hyperspectral Analysis

This paper studies the effect of storage techniques for transporting collected plant leaves from the field to the laboratory for hyperspectral analysis. The strategy of collecting leaf samples in the field for laboratory analysis is typically used when ground truthing is needed in remote sensing studies. Results indicate that the accuracy of hyperspectral measurements depends on a combination of storage technique (in a cooler or outside a cooler), time elapsed between collecting leaf samples in the field and measuring in the laboratory, and the plant species. A nonlinear model fitting method is proposed to estimate the spectrum of decaying plant leaves. This revealed that the reflectance of soybean leaves remained within the normal range for 45 min when the leaves were stored in a cooler, while soybean leaves stored outside a cooler remained within the normal range for 30 min. However, cotton leaves stored in a cooler decayed faster initially. Regardless of storage technique, results indicate that up to a maximum of 30 min can elapse between plant leaf sampling in the field and hyperspectral measurements in the laboratory. This study focused on cotton and soybean leaves, but the implication that time elapsing between sampling leaves and measuring their spectrum should be limited as much as possible can be applied to any study on other crop leaves. Results of the study also provide a guideline for crop storage limits when analyzing by laboratory hyperspectral sensing setting to improve the quality and reliability of data for precision agriculture.

First Report of Soybean vein necrosis virus in Soybean Fields of Oklahoma.

Soybean vein necrosis virus (SVNV) causes a new emerging disease of soybean that has been recorded in more than 10 states (1,2,3,4) of the United States, but so far no information is available about its presence in soybean crops of Oklahoma. Surveys of commercial soybean fields were conducted for soybean viruses during summer of 2012. A total of 327 samples were randomly collected from soybean fields in 11 counties. Symptoms typical of SVNV infections including leaf chlorosis and leaf-vein necrosis were observed on some soybean plants in the field (4). All soybean leaf samples were tested against SVNV polyclonal antisera obtained from AC Diagnostics, Inc. (Fayetteville, AR) by dot-immunobinding assay (DIBA) (1). Fifty-three samples reacted positively with SVNV antisera. Total RNA was extracted from three DIBA-positive samples collected from soybean plants in Choctaw County and tested by reverse transcription (RT)-PCR using SVNV-specific primers (forward primer 5'-ATGTTCTCTCTATAATAGCCA and reverse primer 5'-ACCCATAACAATTGATCAAGA-3') that were designed from the available sequence in the GenBank (Accession No. GU722317.1) to amplify a fragment from RNA1. A band of the expected size of 344 bp was observed on a 1% agarose gel in all three samples. The PCR products were purified using QIAquick PCR Purification Kit (QIAGEN, Valencia, CA), cloned (pGEM-T Easy Vector, Promega, Madison, WI) and sequenced in both directions. The consensus sequence of the 344-bp fragment was 99% identical with the corresponding region of RNA 1 of SVNV isolate 'Milan_TN' (Accession No. GU722317.1). These results confirmed the presence of SVNV in soybean fields, which are mostly located in Criage, Choctaw, Hughes, LeFlore, Mayes, Muskogee, McCurtain, Okmulgee, Ottawa, Rogers, and Sequoyah counties of Oklahoma. None of the samples collected from north central or western parts of the state were positive against SVNV. To our knowledge, this is the first report of SVNV in soybean crops in Oklahoma. Soybean is one of the major oil seed crops cultivated on approximately 200,000 hectares annually in Oklahoma and the presence of SVNV could pose a potential threat to the production of soybean in the future.