Vergleich einer neuen Zweinutzungs- mit einer Legehybride bzgl. Tiergerechtheit und Legeleistung

Aim of the study was to compare a new dual purpose hybrid strain (Lohmann dual, LD) with a layer strain (Lohmann Brown plus, LB) under organic conditions. 400 Laying hens were kept in 2 mobile houses (2 x 4 groups of 50 hens). Egg production and egg weight were higher in LBP. LBP hens used the pasture more than LD. However, foraging was the main activity in both strains. LD hens showed less behavioral activity within the hen house. Plumage condition was better in LBP

Stochastic reconstruction of spatio-temporal rainfall patterns by inverse hydrologic modelling

Knowledge of spatio-temporal rainfall patterns is required as input for distributed hydrologic models used for tasks such as flood runoff estimation and modelling. Normally, these patterns are generated from point observations on the ground using spatial interpolation methods. However, such methods fail in reproducing the true spatio-temporal rainfall pattern, especially in data-scarce regions with poorly gauged catchments, or for highly dynamic, small-scale rainstorms which are not well recorded by existing monitoring networks. Consequently, uncertainties arise in distributed rainfall–runoff modelling if poorly identified spatio-temporal rainfall patterns are used, since the amount of rainfall received by a catchment as well as the dynamics of the runoff generation of flood waves is underestimated. To address this problem we propose an inverse hydrologic modelling approach for stochastic reconstruction of spatio-temporal rainfall patterns. The methodology combines the stochastic random field simulator Random Mixing and a distributed rainfall–runoff model in a Monte Carlo framework. The simulated spatio-temporal rainfall patterns are conditioned on point rainfall data from ground-based monitoring networks and the observed hydrograph at the catchment outlet and aim to explain measured data at best. Since we infer a three-dimensional input variable from an integral catchment response, several candidates for spatio-temporal rainfall patterns are feasible and allow for an analysis of their uncertainty. The methodology is tested on a synthetic rainfall–runoff event on sub-daily time steps and spatial resolution of 1&thinsp;km2 for a catchment partly covered by rainfall. A set of plausible spatio-temporal rainfall patterns can be obtained by applying this inverse approach. Furthermore, results of a real-world study for a flash flood event in a mountainous arid region are presented. They underline that knowledge about the spatio-temporal rainfall pattern is crucial for flash flood modelling even in small catchments and arid and semiarid environments.</p

Befragung zum Status-Quo der Tierhaltung bei 287 süddeutschen Bio-Betrieben (Demeter- und Bioland)[Inquiry to the status quo of livestock husbandry in organic farms in southern germany]

Fazit: Die gegenüber früheren Untersuchungen gestiegenen Bestandsgrößen weisen darauf hin, daß sich im ökologischen Landbau ein ähnlicher Strukturwandel wie in der konventionellen Landwirtschaft vollzieht (Wachstum und Spezialisierung). Die Auswertung zeigt ferner, daß die Betriebe zunehmend bemüht sind, bereits jetzt den zukünftigen Haltungsvorschriften der EU-Verordnung zu entsprechen. Fast alle Betriebe führen Weidegang durch und trotz der relativ geringen Bestandsgröße haben die meisten Betriebe bereits Laufställe; Auslaufmöglichkeiten fallen demgegenüber allerdings noch ab

Stochastic Reconstruction and Interpolation of Precipitation Fields Using Combined Information of Commercial Microwave Links and Rain Gauges

For the reconstruction and interpolation of precipitation fields, we present the application of a stochastic approach called Random Mixing. Generated fields are based on a data set consisting of rain gauge observations and path-averaged rain rates estimated using Commercial Microwave Link (CML) derived information. Precipitation fields are received as linear combination of unconditional spatial random fields, where the spatial dependence structure is described by copulas. The weights of the linear combination are optimized such that the observations and the spatial structure of the precipitation observations are reproduced. The innovation of the approach is that this strategy enables the simulation of ensembles of precipitation fields of any size. Each ensemble member is in concordance with the observed path-averaged CML derived rain rates and additionally reflects the observed rainfall variability along the CML paths. The ensemble spread allows additionally an estimation of the uncertainty of the reconstructed precipitation fields. The method is demonstrated both for a synthetic data set and a real-world data set in South Germany. While the synthetic example allows an evaluation against a known reference, the second example demonstrates the applicability for real-world observations. Generated precipitation fields of both examples reproduce the spatial precipitation pattern in good quality. A performance evaluation of Random Mixing compared to Ordinary Kriging demonstrates an improvement of the reconstruction of the observed spatial variability. Random Mixing is concluded to be a beneficial new approach for the provision of precipitation fields and ensembles of them, in particular when different measurement types are combined

Wave-train induced unpinning of weakly anchored vortices in excitable media

A free vortex in excitable media can be displaced and removed by a wave-train. However, simple physical arguments suggest that vortices anchored to large inexcitable obstacles cannot be removed similarly. We show that unpinning of vortices attached to obstacles smaller than the core radius of the free vortex is possible through pacing. The wave-train frequency necessary for unpinning increases with the obstacle size and we present a geometric explanation of this dependence. Our model-independent results suggest that decreasing excitability of the medium can facilitate pacing-induced removal of vortices in cardiac tissue.Comment: Published versio

Phase annealing for the conditional simulation of spatial random fields

Simulated annealing (SA) is a popular geostatistical simulation method as it provides great flexibility. In this paper possible problems of conditioning its realizations are discussed. A statistical test to recognize whether the observations are well embedded in their simulated neighborhood or not is developed. A new simulated annealing method, phase annealing (PA), is presented which makes it possible to avoid poor embedding of observations. PA is based on the Fourier representation of the spatial field. Instead of the individual pixel values, phases corresponding to different Fourier components are modified (i.e. shifted) in order to match prescribed statistics. The method treats neighborhoods together and thus avoids singularities at observation locations. It is faster than SA and can be used for the simulation of high resolution fields. Examples demonstrate the applicability of the method

Computational efficient inverse groundwater modeling using Random Mixing and Whittaker–Shannon interpolation

Geostatistical inverse modeling problems can potentially be very high-dimensional and computationally expensive. Using the Random Mixing approach the dimensionality can be reduced, and any optimization algorithm can be applied to solve the inverse problem in this lower dimensional space. In order to reduce computational costs the standard optimization is replaced by a sequence of one-dimensional optimizations. In this one-dimensional space a simplified calculation of the objective function using Whittaker–Shannon interpolation is carried out. This procedure requires a significantly reduced number of forward model runs and it also guarantees monotonic convergence of the objective function. A synthetic and a real world example will be used to demonstrate the procedure and to evaluate the quality of the Whittaker–Shannon interpolation in comparison to the actual objective functions

Computational efficient inverse groundwater modeling using Random Mixing and Whittaker–Shannon interpolation

Geostatistical inverse modeling problems can potentially be very high-dimensional and computationally expensive. Using the Random Mixing approach the dimensionality can be reduced, and any optimization algorithm can be applied to solve the inverse problem in this lower dimensional space. In order to reduce computational costs the standard optimization is replaced by a sequence of one-dimensional optimizations. In this one-dimensional space a simplified calculation of the objective function using Whittaker–Shannon interpolation is carried out. This procedure requires a significantly reduced number of forward model runs and it also guarantees monotonic convergence of the objective function. A synthetic and a real world example will be used to demonstrate the procedure and to evaluate the quality of the Whittaker–Shannon interpolation in comparison to the actual objective functions