A Big Data Analytics Framework in Climate Smart Agriculture

Climate Smart Agriculture incorporates information on soils, bothers, maladies, costs and different variables to increment illustrative power. Creating atmosphere strong horticulture is fundamental to accomplishing future sustenance security and environmental change objectives. Through CSA application the ranchers can anticipate crop type (crop exhortation) which is suitable for accessible condition. Climate Smart farming has been generally used to express agricultural practices that will increment horticultural efficiency and nourishment security and to foresee rural items. CSA application have Climate information (like: Max Temp, Min Temp, Humidity, Rain fall, daylight, wind course, wind speed), Fertilizer and Soil Data's (like, Black Soil, Red Soil).The review aim to help farmers better adapt to temperature extremes, droughts or excess water in fields so that they can make better decisions for the environment and maximize production or profits. The data collection is an important role in the work process. Enabling farmers to head massive amounts of data collected through sensors to predict the best time to plant, what type of seed to use, and where to plant in order to improve yields, cut operational costs, and minimize environmental impact.Big data analytics provide new ways for businesses updates and requirement for updating and government to analyze unstructured data. Now a day, big data is one of the most important and challenging point in information technology world. It is executing very important role in future. Big data changes the way of world for management and use big amount of data Keywords: Climate Smart Agriculture, Big Data Analytics and Hadoop DOI: 10.7176/CEIS/10-6-01 Publication date:July 31st 201

CFGP: a web-based, comparative fungal genomics platform

Since the completion of the Saccharomyces cerevisiae genome sequencing project in 1996, the genomes of over 80 fungal species have been sequenced or are currently being sequenced. Resulting data provide opportunities for studying and comparing fungal biology and evolution at the genome level. To support such studies, the Comparative Fungal Genomics Platform (CFGP; http://cfgp.snu.ac.kr), a web-based multifunctional informatics workbench, was developed. The CFGP comprises three layers, including the basal layer, middleware and the user interface. The data warehouse in the basal layer contains standardized genome sequences of 65 fungal species. The middleware processes queries via six analysis tools, including BLAST, ClustalW, InterProScan, SignalP 3.0, PSORT II and a newly developed tool named BLASTMatrix. The BLASTMatrix permits the identification and visualization of genes homologous to a query across multiple species. The Data-driven User Interface (DUI) of the CFGP was built on a new concept of pre-collecting data and post-executing analysis instead of the ‘fill-in-the-form-and-press-SUBMIT’ user interfaces utilized by most bioinformatics sites. A tool termed Favorite, which supports the management of encapsulated sequence data and provides a personalized data repository to users, is another novel feature in the DUI

SMDB: Soybean Marker DataBase

Soybean Marker Database (SMDB) is a repository of important genomic information for soybean. At present several genomic databases are available for plants. Some of the important oilseeds plant databases are ATPID database, Castor Bean Genome Database, CGPDB, SoyBase, Legume Information System (LIS), Brassica database, Sinbase, etc. To gain comprehensive information from varied amount of resources, we developed  this database which provides general as well as specific information at universal level. Along with this it also furnishes gene level information for various functional categories such as transcription factor, disease resistant varieties, heat shock protein, genetically modified strain of soybean. The bunch of information available to researchers today increases in tremendous manner. Hence understanding the plant genome specific databases for acquiring specific information is the demand of time for crop improvement and  research programmes. SMDB is designed for the purpose of exploring potential gene differences in different plant genotypes, including genetically modified and disease resistant crops beneficial to the farmer who cultivate this crop. SMDB is publicly accessible for academic and research purpose at: http://www.bioinfoindia.org/smdb/

MediPlEx - a tool to combine in silico & experimental gene expression profiles of the model legume Medicago truncatula

Henckel K, Küster H, Stutz L, Goesmann A. MediPlEx - a tool to combine in silico and experimental gene expression profiles of the model legume Medicago truncatula. BMC Research Notes. 2010;3(1): 262.BACKGROUND:Expressed Sequence Tags (ESTs) are in general used to gain a first insight into gene activities from a species of interest. Subsequently, and typically based on a combination of EST and genome sequences, microarray-based expression analyses are performed for a variety of conditions. In some cases, a multitude of EST and microarray experiments are conducted for one species, covering different tissues, cell states, and cell types. Under these circumstances, the challenge arises to combine results derived from the different expression profiling strategies, with the goal to uncover novel information on the basis of the integrated datasets.FINDINGS:Using our new application, MediPlEx (MEDIcago truncatula multiPLe EXpression analysis), expression data from EST experiments, oligonucleotide microarrays and Affymetrix GeneChips can be combined and analyzed, leading to a novel approach to integrated transcriptome analysis. We have validated our tool via the identification of a set of well-characterized AM-specific and AM-induced marker genes, identified by MediPlEx on the basis of in silico and experimental gene expression profiles from roots colonized with AM fungi.CONCLUSIONS:MediPlEx offers an integrated analysis pipeline for different sets of expression data generated for the model legume Medicago truncatula. As expected, in silico and experimental gene expression data that cover the same biological condition correlate well. The collection of differentially expressed genes identified via MediPlEx provides a starting point for functional studies in plant mutants. MediPlEx can freely be used at http://www.cebitec.uni-bielefeld.de/mediplex

Mining climate data for shire level wheat yield predictions in Western Australia

Climate change and the reduction of available agricultural land are two of the most important factors that affect global food production especially in terms of wheat stores. An ever increasing world population places a huge demand on these resources. Consequently, there is a dire need to optimise food production. Estimations of crop yield for the South West agricultural region of Western Australia have usually been based on statistical analyses by the Department of Agriculture and Food in Western Australia. Their estimations involve a system of crop planting recommendations and yield prediction tools based on crop variety trials. However, many crop failures arise from adherence to these crop recommendations by farmers that were contrary to the reported estimations. Consequently, the Department has sought to investigate new avenues for analyses that improve their estimations and recommendations. This thesis explores a new approach in the way analyses are carried out. This is done through the introduction of new methods of analyses such as data mining and online analytical processing in the strategy. Additionally, this research attempts to provide a better understanding of the effects of both gradual variation parameters such as soil type, and continuous variation parameters such as rainfall and temperature, on the wheat yields. The ultimate aim of the research is to enhance the prediction efficiency of wheat yields. The task was formidable due to the complex and dichotomous mixture of gradual and continuous variability data that required successive information transformations. It necessitated the progressive moulding of the data into useful information, practical knowledge and effective industry practices. Ultimately, this new direction is to improve the crop predictions and to thereby reduce crop failures. The research journey involved data exploration, grappling with the complexity of Geographic Information System (GIS), discovering and learning data compatible software tools, and forging an effective processing method through an iterative cycle of action research experimentation. A series of trials was conducted to determine the combined effects of rainfall and temperature variations on wheat crop yields. These experiments specifically related to the South Western Agricultural region of Western Australia. The study focused on wheat producing shires within the study area. The investigations involved a combination of macro and micro analyses techniques for visual data mining and data mining classification techniques, respectively. The research activities revealed that wheat yield was most dependent upon rainfall and temperature. In addition, it showed that rainfall cyclically affected the temperature and soil type due to the moisture retention of crop growing locations. Results from the regression analyses, showed that the statistical prediction of wheat yields from historical data, may be enhanced by data mining techniques including classification. The main contribution to knowledge as a consequence of this research was the provision of an alternate and supplementary method of wheat crop prediction within the study area. Another contribution was the division of the study area into a GIS surface grid of 100 hectare cells upon which the interpolated data was projected. Furthermore, the proposed framework within this thesis offers other researchers, with similarly structured complex data, the benefits of a general processing pathway to enable them to navigate their own investigations through variegated analytical exploration spaces. In addition, it offers insights and suggestions for future directions in other contextual research explorations

Bioinformatics tools for crop research and breeding

Crop improvement has always been, but will be even more so in the twenty-first century, an information intensive process. For effective and efficient improvement, a range of activities from molecular biology to genetics to indirect selection must now be involved. The rate of progress made by any breeding programme depends as much on the efficient integration of information from these activities as it does on the activities themselves. Plant breeders are now realizing the importance of innovative approaches that include the use of a range of molecular methods and their outputs, and the possibilities of transferring this information from model species to cultivated crops. The use of these high throughput methods in model crops has already generated a large amount of public resources such as databases containing genetic resource, genomic and genetic information; tools for the effective analysis, data mining and visualization of such information; and semantic web resources for data integration. In this chapter, we highlight the role and contributions of bioinformatics to crop research and breeding by focusing on the bioinformatics resources that are available for crop science research and breeding, and indicating gaps that need to be bridged that will allow scientists to access, transfer and integrate data with eas