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Severini, S. |
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How volatile are farm incomes? The case of Italian farms |
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2015 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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4 |
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SP4-18 |
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TradeM International Workshop 2014 »Economics of integrated assessment approaches for agriculture and the food sector«, 25–27 November 2014, Hurdalsjø, Norway |
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MA @ admin @ |
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2208 |
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Baranowski, P.; Jedryczka, M.; Mazurek, W.; Babula-Skowronska, D.; Siedliska, A.; Kaczmarek, J. |
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Title |
Hyperspectral and thermal imaging of oilseed rape (Brassica napus) response to fungal species of the genus Alternaria |
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Journal Article |
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2015 |
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PLoS One |
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PLoS One |
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10 |
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3 |
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e0122913 |
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Keywords |
Algorithms; Alternaria/*pathogenicity; Brassica napus/microbiology/*physiology |
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In this paper, thermal (8-13 µm) and hyperspectral imaging in visible and near infrared (VNIR) and short wavelength infrared (SWIR) ranges were used to elaborate a method of early detection of biotic stresses caused by fungal species belonging to the genus Alternaria that were host (Alternaria alternata, Alternaria brassicae, and Alternaria brassicicola) and non-host (Alternaria dauci) pathogens to oilseed rape (Brassica napus L.). The measurements of disease severity for chosen dates after inoculation were compared to temperature distributions on infected leaves and to averaged reflectance characteristics. Statistical analysis revealed that leaf temperature distributions on particular days after inoculation and respective spectral characteristics, especially in the SWIR range (1000-2500 nm), significantly differed for the leaves inoculated with A. dauci from the other species of Alternaria as well as from leaves of non-treated plants. The significant differences in leaf temperature of the studied Alternaria species were observed in various stages of infection development. The classification experiments were performed on the hyperspectral data of the leaf surfaces to distinguish days after inoculation and Alternaria species. The second-derivative transformation of the spectral data together with back-propagation neural networks (BNNs) appeared to be the best combination for classification of days after inoculation (prediction accuracy 90.5%) and Alternaria species (prediction accuracy 80.5%). |
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1932-6203 |
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CropM, ft_macsur |
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MA @ admin @ |
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4549 |
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Author |
Wallach, D.; Rivington, M. |
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Title |
Identification and quantification of differences between models |
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Report |
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2015 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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6 |
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D-C4.2.2 |
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A major goal of crop model inter-comparison is model improvement, and an important intermediate step toward that goal is understanding in some detail how models differ, and the consequences of those differences. This report is intended as a first attempt at describing possible techniques for relating differences between model outputs to specific aspects of the models. No Label |
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MA @ admin @ |
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2101 |
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Ramirez-Villegas, J.; Watson, J.; Challinor, A.J. |
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Title |
Identifying traits for genotypic adaptation using crop models |
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Journal Article |
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2015 |
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Journal of Experimental Botany |
Abbreviated Journal |
J. Experim. Bot. |
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66 |
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12 |
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3451-3462 |
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Adaptation, Physiological/*genetics; Crops, Agricultural/*genetics; Environment; Genotype; *Models, Theoretical; *Quantitative Trait, Heritable; Climate change; crop models; genotypic adaptation; ideotypes; impacts |
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Genotypic adaptation involves the incorporation of novel traits in crop varieties so as to enhance food productivity and stability and is expected to be one of the most important adaptation strategies to future climate change. Simulation modelling can provide the basis for evaluating the biophysical potential of crop traits for genotypic adaptation. This review focuses on the use of models for assessing the potential benefits of genotypic adaptation as a response strategy to projected climate change impacts. Some key crop responses to the environment, as well as the role of models and model ensembles for assessing impacts and adaptation, are first reviewed. Next, the review describes crop-climate models can help focus the development of future-adapted crop germplasm in breeding programmes. While recently published modelling studies have demonstrated the potential of genotypic adaptation strategies and ideotype design, it is argued that, for model-based studies of genotypic adaptation to be used in crop breeding, it is critical that modelled traits are better grounded in genetic and physiological knowledge. To this aim, two main goals need to be pursued in future studies: (i) a better understanding of plant processes that limit productivity under future climate change; and (ii) a coupling between genetic and crop growth models-perhaps at the expense of the number of traits analysed. Importantly, the latter may imply additional complexity (and likely uncertainty) in crop modelling studies. Hence, appropriately constraining processes and parameters in models and a shift from simply quantifying uncertainty to actually quantifying robustness towards modelling choices are two key aspects that need to be included into future crop model-based analyses of genotypic adaptation. |
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0022-0957 1460-2431 |
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Review |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4645 |
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Author |
Holman, I. |
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Identifying where future landuse allocation in Europe is robust to climate and socio-economic uncertainty |
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2015 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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5 |
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Sp5-23 |
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The spatial distribution of future European landuse will be influenced by yield changes arising from climate change and changes in profitability as a consequence of socio-economic change (arising from changing food demand; prices; technology etc). To understand how these factors affect future land use allocation, a modelling system has been set up to predict agricultural land use across the EU under any scenario set of climate and socio- and techno-economic data. Metamodels of crop and forest yields, and optimal cropping and profit are derived from the outputs of the IMPEL, GOTILWA+, SFARMODand WaterGAP models. Profitability of each possible land use is modelled across the EU, assuming that use will change to the most profitable in the timescale being considered (2050). Land use in a grid is then allocated based on profit, with minimum profit thresholds set for intensive agriculture (arable or grassland), extensive agriculture, managed forest and finally unmanaged forest or unmanaged land. The European demand for food as a function of population, imports, food preferences and bioenergy, is a production constraint, as is irrigation water available. The model iterates prices until demand is satisfied (or cannot be met) and basin water usage for irrigation is not more than is available.This presentation describes the application of the modelling system across future climate change uncertainty space (as given by 60 combinations of downscaled 10’x10’ gridded climate outputs from 5 Global Climate Models, 3 climate sensitivities and 4 emissions scenario) under both baseline and four future socio-economic scenarios to identify those areas of Europe in which the spatial allocation of agricultural landcovers are robust to this uncertainty. No Label |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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MA @ admin @ |
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2138 |
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