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- ItemClassifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change(Amsterdam [u.a.] : Elsevier, 2017) Fronzek, Stefan; Pirttioja, Nina; Carter, Timothy R.; Bindi, Marco; Hoffmann, Holger; Palosuo, Taru; Ruiz-Ramos, Margarita; Tao, Fulu; Trnka, Miroslav; Acutis, Marco; Asseng, Senthold; Baranowski, Piotr; Basso, Bruno; Bodin, Per; Buis, Samuel; Cammarano, Davide; Deligios, Paola; Destain, Marie-France; Dumont, Benjamin; Ewert, Frank; Ferrise, Roberto; François, Louis; Gaiser, Thomas; Hlavinka, Petr; Jacquemin, Ingrid; Kersebaum, Kurt Christian; Kollas, Chris; Krzyszczak, Jaromir; Lorite, Ignacio J.; Minet, Julien; Minguez, M. Ines; Montesino, Manuel; Moriondo, Marco; Müller, Christoph; Nendel, Claas; Öztürk, Isik; Perego, Alessia; Rodríguez, Alfredo; Ruane, Alex C.; Ruget, Françoise; Sanna, Mattia; Semenov, Mikhail A.; Slawinski, Cezary; Stratonovitch, Pierre; Supit, Iwan; Waha, Katharina; Wang, Enli; Wu, Lianhai; Zhao, Zhigan; Rötter, Reimund P.Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (−2 to +9°C) and precipitation (−50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.
- ItemPinning and trapped field in MgB2- and MT-YBaCuO bulk superconductors manufactured under pressure(Bristol : IOP Publ., 2016) Prikhna, T.; Eisterer, M.; Chaud, X.; Weber, H.W.; Habisreuther, T.; Moshchil, V.; Kozyrev, A.; Shapovalov, A.; Gawalek, W.; Wu, M.; Litzkendorf, D.; Goldacker, W.; Sokolovsky, V.; Shaternik, V.; Rabier, J.; Joulain, A.; Grechnev, G.; Boutko, V.; Gusev, A.; Shaternik, A.; Barvitskiy, P.The relevant pinning centers of Abrikosov vortices in MgB2-based materials are oxygen-enriched Mg-B-O inclusions or nanolayers and inclusions of MgBx (x>4) phases. The high critical current densities, jc, of 106 and 103A/cm2 at 1 and 8.5 T, respectively, at 20 K can be achieved in polycrystalline materials (prepared at 2 GPa) containing a large amount of admixed oxygen. Besides, oxygen can be incorporated into the MgB2 structure in small amounts (MgB1.5O0.5), which is supported by Auger studies and calculations of the DOS and the binding energy. The jc of melt textured YBa2Cu3O7-δ (or Y123)-based superconductors (MT-YBaCuO) depends not only on the perfectness of texture and the amount of oxygen in the Y123 structure, but also on the density of twins and micro-cracks formed during the oxygenation (due to shrinking of the c-lattice parameter). The density of twins and microcracks increases with the reduction of the distance between Y2BaCuO5 (Y211) inclusions in Y123. At 77 K jc=8·104 A/cm2 in self-field and jc=103 A/cm2 at 10 T were found in materials oxygenated at 16 MPa for 3 days with a density of twins of 22–35 per µm (thickness of the lamellae: 45-30 nm) and a density of micro-cracks of 200–280 per mm. Pinning can occur at the points of intersection between the Y123 twin planes and the Y211 inclusions. MTYBaCuO at 77 K can trap 1.4 T (38×38×17 mm, oxygenated at 0.1 MPa for 20 days) and 0.8 T (16 mm in diameter and 10 mm thick with 0.45 mm holes oxygenated at 10 MPa for 53 h). The sensitivity of MgB2 to magnetic field variations (flux jumps) complicates estimates of the trapped field. At 20 K 1.8 T was found for a block of 30 mm in diameter and a thickness of 7.5 mm and 1.5 T (if the magnetic field was increased at a rate of 0.1 T) for a ring with dimensions 24×18 mm and a thickness of 8 mm.
- ItemProcess verification of a hydrological model using a temporal parameter sensitivity analysis(Göttingen : Copernicus GmbH, 2015) Pfannerstill, M.; Guse, B.; Reusser, D.; Fohrer, N.
- ItemDirect prediction of site-specific lime requirement of arable fields using the base neutralizing capacity and a multi-sensor platform for on-the-go soil mapping(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2021-7-26) Vogel, Sebastian; Bönecke, Eric; Kling, Charlotte; Kramer, Eckart; Lück, Katrin; Philipp, Golo; Rühlmann, Jörg; Schröter, Ingmar; Gebbers, RobinLiming agricultural fields is necessary for counteracting soil acidity and is one of the oldest operations in soil fertility management. However, the best management practice for liming in Germany only insufficiently considers within-field soil variability. Thus, a site-specific variable rate liming strategy was developed and tested on nine agricultural fields in a quaternary landscape of north-east Germany. It is based on the use of a proximal soil sensing module using potentiometric, geoelectric and optical sensors that have been found to be proxies for soil pH, texture and soil organic matter (SOM), which are the most relevant lime requirement (LR) affecting soil parameters. These were compared to laboratory LR analysis of reference soil samples using the soil’s base neutralizing capacity (BNC). Sensor data fusion utilizing stepwise multi-variate linear regression (MLR) analysis was used to predict BNC-based LR (LRBNC) for each field. The MLR models achieved high adjusted R2 values between 0.70 and 0.91 and low RMSE values from 65 to 204 kg CaCO3 ha−1. In comparison to univariate modeling, MLR models improved prediction by 3 to 27% with 9% improvement on average. The relative importance of covariates in the field-specific prediction models were quantified by computing standardized regression coefficients (SRC). The importance of covariates varied between fields, which emphasizes the necessity of a field-specific calibration of proximal sensor data. However, soil pH was the most important parameter for LR determination of the soils studied. Geostatistical semivariance analysis revealed differences between fields in the spatial variability of LRBNC. The sill-to-range ratio (SRR) was used to quantify and compare spatial LRBNC variability of the nine test fields. Finally, high resolution LR maps were generated. The BNC-based LR method also produces negative LR values for soil samples with pH values above which lime is required. Hence, the LR maps additionally provide an estimate on the quantity of chemically acidifying fertilizers that can be applied to obtain an optimal soil pH value.