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SourceAvailable from: Aviva Peeters[Show abstract] [Hide abstract]
ABSTRACT: Spatial Decision Support Systems (SDSS) combine Decision Support Systems (DSS) and Geographical Information Systems (GIS) to support decision makers in managing complex spatial problems. Such problems are commonly evaluated based on multiple criteria, consist of a large number of decision alternatives, involve uncertainty in their decisions and have therefore been adopted to manage various complex environmental processes such as natural hazards and water resources. An SDSS must be able, in addition to providing requirements common to any DSS, to support the input and storage of spatial data and the application of spatial analysis methods and to build prediction models that can simulate dynamic spatial processes and evaluate the effect of different future scenarios for decision-making. To support farmers in efficient precision management of tree crops, a GIS-based SDSS is under construction within the framework of the '3D-Mosaic' project which aims at optimizing water consumption in tree plantations by employing precision agriculture methods. Tree plantation test sites in Turkey and in Germany are currently monitored for plant and fruit growth under different environmental conditions and irrigation regimes using an autonomous mobile sensing and image analysis platform. Data is collected using various sensors, on a variety of plant-related and site-related variables. The constructed 3D GIS database is currently analyzed for variability using spatial statistic methods to assess the spatiotemporal patterns of a-biotic and plant related data. Recognized patterns will be used to develop algorithms for deriving: (a) tree adapted management zones and (b) site-specific irrigation maps. These maps will serve as the basis for developing SDSS interface to support farmers in orchard management. 1 INTRODUCTION The challenge to ensure food security within a global climate change together with increased concern to reduce the agricultural footprint on the environment while maintaining its economic viability pursued the evolvement of precision agriculture. Research and practice in precision agriculture aim at optimizing the management of sustainable agricultural production by addressing the spatial variability in plant properties and in their environmental conditions, such as in the soil. Remote sensing, information technology (IT) and geospatial methods are being harnessed to quantify the spatial variability in the agricultural plots (Corwin and Plant, 2005). This variability is the basis for developing management zones that optimize inputs, such as irrigation and fertilization, while improving the quantity and quality of yields and improve sustainability of agricultural practices. Spatial analysis is considered as the core of Geographical Information Systems (GIS) and consists of the methods and techniques for analyzing data in its spatial context (Longley, 2010). Spatial statistical methods can be used to recognize and quantify patterns and relations which vary in space, i.e. quantify the statistical significance of a recognized pattern (Mitchell, 2005). These features make spatial statistics suitable for analyzing and quantifying the spatial variability of various parameters related to crops and to their environmental conditions.
Cardiology 12/2014; 130(1):47. DOI:10.1159/000368981
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ABSTRACT: Herbicide resistant weeds are becoming increasingly common, threatening global food security. Here, we present BrIFAR: a new model system for the functional study of mechanisms of herbicide resistance in grass weeds. We have developed a large collection of Brachypodium accessions, the BrI collection, representing a wide range of habitats. Wide screening of the responses of the accessions to four major herbicide groups (PSII, ACCase, ALS/AHAS and EPSPS inhibitors) identified 28 herbicide-resistance candidate accessions. Target-site resistance to PSII inhibitors was found in accessions collected from habitats with a known history of herbicide applications. An amino acid substitution in the psbA gene (serine264 to glycine) conferred resistance and also significantly affected the flowering and shoot dry weight of the resistant accession, as compared to the sensitive accession. Non-target site resistance to ACCase inhibitors was found in accessions collected from habitats with a history of herbicide application and from a nature reserve. In-vitro enzyme activity tests and responses following pre-treatment with malathion (a cytochrome-P450 inhibitor) indicated sensitivity at the enzyme level, and give strong support to diclofop-methyl and pinoxaden enhanced detoxification as NTS resistance mechanism. BrIFAR can promote better understanding of the evolution of mechanisms of herbicide resistance and aid the implementation of integrative management approaches for sustainable agriculture.Plant Science 12/2014; 229:43–52. DOI:10.1016/j.plantsci.2014.08.013
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