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A major limitation in modeling global ozone (O3) vegetation damage has long been the reliance on empirical O3 sensitivity parameters derived from a limited number of species and applied at the level of plant functional types (PFTs), which ignore the large interspecific variations within the same PFT. Here, we present a major advance in large-scale assessments of O3 plant injury by linking the trait leaf mass per area (LMA) and plant O3 sensitivity in a broad and global perspective. Application of the new approach and a global LMA map in a dynamic global vegetation model reasonably represents the observed interspecific responses to O3 with a unified sensitivity parameter for all plant species. Simulations suggest a contemporary global mean reduction of 4.8% in gross primary productivity by O3, with a range of 1.1%-12.6% for varied PFTs. Hotspots with damages > 10% are found in agricultural areas in the eastern U.S., western Europe, eastern China, and India, accompanied by moderate to high levels of surface O3. Furthermore, we simulate the distribution of plant sensitivity to O3, which is highly linked with the inherent leaf trait trade-off strategies of plants, revealing high risks for fast-growing species with low LMA, such as crops, grasses and deciduous trees. [ABSTRACT FROM AUTHOR]

Land ecosystems are important sources and sinks of atmospheric components. In turn, air pollutants affect the exchange rates of carbon and water fluxes between ecosystems and atmosphere. However, these biogeochemical processes are usually not well presented in the Earth system models, limiting the explorations of interactions between land ecosystems and air pollutants from the regional to global scales. Here, we develop and validate the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) by upgrading the Yale Interactive terrestrial Biosphere model with process-based water cycles, fire emissions, wetland methane (CH4) emissions, and the trait-based ozone (O3) damages. Within the iMAPLE, soil moisture and temperature are dynamically calculated based on the water and energy balance in soil layers. Fire emissions are dependent on dryness, lightning, population, and fuel load. Wetland CH4 is produced but consumed through oxidation, ebullition, diffusion, and plant-mediated transport. The trait-based scheme unifies O3 sensitivity of different plant functional types (PFTs) with the leaf mass per area. Validations show correlation coefficients (R) of 0.59-0.86 for gross primary productivity (GPP) and 0.57-0.84 for evapotranspiration (ET) across the six PFTs at 201 flux tower sites, and yield an average R of 0.68 for CH4 emissions at 44 sites. Simulated soil moisture and temperature match reanalysis data with the high R above 0.86 and low normalized mean biases (NMB) within 7%, leading to reasonable simulations of global GPP (R=0.92, NMB=1.3%) and ET (R=0.93, NMB=-10.4%) against satellite-based observations for 2001-2013. The model predicts an annual global area burned of 507.1 Mha, close to the observations of 475.4 Mha with a spatial R of 0.66 for 1997-2016. The wetland CH4 emissions are estimated to be 153.45 Tg [CH4] yr-1 during 2000-2014, close to the multi-model mean of 148 Tg [CH4] yr-1. The model also shows reasonable responses of GPP and ET to the changes in diffuse radiation, and yields a mean O3 damage of 2.9% to global GPP. The iMAPLE provides an advanced tool for studying the interactions between land ecosystem and air pollutants. [ABSTRACT FROM AUTHOR]

microRNAs (miRNAs) play vital regulatory roles in many organisms through direct cleavage of transcripts, translational repression, or chromatin modification. Identification of miRNAs has been carried out in various plant species. However, no information is available for miRNAs from Panax ginseng, an economically significant medicinal plant species. Using the next generation high-throughput sequencing technology, we obtained 13,326,328 small RNA reads from the roots, stems, leaves and flowers of P. ginseng. Analysis of these small RNAs revealed the existence of a large, diverse and highly complicated small RNA population in P. ginseng. We identified 73 conserved miRNAs, which could be grouped into 33 families, and 28 non-conserved ones belonging to 9 families. Characterization of P. ginseng miRNA precursors revealed many features, such as production of two miRNAs from distinct regions of a precursor, clusters of two precursors in a transcript, and generation of miRNAs from both sense and antisense transcripts. It suggests the complexity of miRNA production in P. gingseng. Using a computational approach, we predicted for the conserved and non-conserved miRNA families 99 and 31 target genes, respectively, of which eight were experimentally validated. Among all predicted targets, only about 20% are conserved among various plant species, whereas the others appear to be non-conserved, indicating the diversity of miRNA functions. Consistently, many miRNAs exhibited tissue-specific expression patterns. Moreover, we identified five dehydration- and ten heat-responsive miRNAs and found the existence of a crosstalk among some of the stress-responsive miRNAs. Our results provide the first clue to the elucidation of miRNA functions in P. ginseng. [ABSTRACT FROM AUTHOR]

Precision agriculture is the trend of modern agriculture, and it is also one of the important ways to realize the sustainable development of agriculture. In order to meet the production requirements of precision agriculture--efficient use of agricultural resources, and improving the crop yields and quality--some necessary field information in crop growth environment needs to be collected and monitored. In this paper, a farmland information collection system is developed, which includes a portable farmland information collection device based on STM32 (a 32-bit comprehensive range of microcontrollers based on ARM Crotex-M3), a remote server and a mobile phone APP. The device realizes the function of portable and mobile collecting of multiple parameters farmland information, such as chlorophyll content of crop leaves, air temperature, air humidity, and light intensity. UM220-III (Unicore Communication Inc., Beijing, China) is used to realize the positioning based on BDS/GPS (BeiDou Navigation Satellite System, BDS/Global Positioning System, GPS) dual-mode navigation and positioning system, and the CDMA (Code Division Multiple Access, CDMA) wireless communication module is adopted to realize the real-time remote transmission. The portable multi-function farmland information collection system is real-time, accurate, and easy to use to collect farmland information and multiple information parameters of crops. [ABSTRACT FROM AUTHOR]

The leaf chlorophyll content is one of the most important factors for the growth of winter wheat. Visual and near-infrared sensors are a quick and non-destructive testing technology for the estimation of crop leaf chlorophyll content. In this paper, a new approach is developed for leaf chlorophyll content estimation of winter wheat based on visible and near-infrared sensors. First, the sliding window smoothing (SWS) was integrated with the multiplicative scatter correction (MSC) or the standard normal variable transformation (SNV) to preprocess the reflectance spectra images of wheat leaves. Then, a model for the relationship between the leaf relative chlorophyll content and the reflectance spectra was developed using the partial least squares (PLS) and the back propagation neural network. A total of 300 samples from areas surrounding Yangling, China, were used for the experimental studies. The samples of visible and near-infrared spectroscopy at the wavelength of 450,900 nm were preprocessed using SWS, MSC and SNV. The experimental results indicate that the preprocessing using SWS and SNV and then modeling using PLS can achieve the most accurate estimation, with the correlation coefficient at 0.8492 and the root mean square error at 1.7216. Thus, the proposed approach can be widely used for winter wheat chlorophyll content analysis. [ABSTRACT FROM AUTHOR]

Background: Digitalis purpurea is an important ornamental and medicinal plant. There is considerable interest in exploring its transcriptome. Results: Through high-throughput 454 sequencing and subsequent assembly, we obtained 23532 genes, of which 15626 encode conserved proteins. We determined 140 unigenes to be candidates involved in cardiac glycoside biosynthesis. It could be grouped into 30 families, of which 29 were identified for the first time in D. purpurea. We identified 2660 mRNA-like npcRNA (mlncRNA) candidates, an emerging class of regulators, using a computational mlncRNA identification pipeline and 13 microRNA-producing unigenes based on sequence conservation and hairpin structure-forming capability. Twenty five protein-coding unigenes were predicted to be targets of these microRNAs. Among the mlncRNA candidates, only 320 could be grouped into 140 families with at least two members in a family. The majority of D. purpurea mlncRNAs were species-specific and many of them showed tissue-specific expression and responded to cold and dehydration stresses. We identified 417 protein-coding genes with regions significantly homologous or complementary to 375 mlncRNAs. It includes five genes involved in secondary metabolism. A positive correlation was found in gene expression between protein-coding genes and the homologous mlncRNAs in response to cold and dehydration stresses, while the correlation was negative when protein-coding genes and mlncRNAs were complementary to each other. Conclusions: Through comprehensive transcriptome analysis, we not only identified 29 novel gene families potentially involved in the biosynthesis of cardiac glycosides but also characterized a large number of mlncRNAs. Our results suggest the importance of mlncRNAs in secondary metabolism and stress response in D. purpurea. [ABSTRACT FROM AUTHOR]