Spurenelementbelastung von Böden im Bergbaugebiet Gatumba, Ruanda

Die Böden des Coltan-Bergbaugebietes Gatumba Mining District in Ruanda sind potenzielle Quellen für die Freisetzung toxischer Spurenelemente. Insbesondere Technosole auf Haldenabraum, Hangwasser-beeinflusste Gleysole sowie Fluvisole in der Bergbauregion wurden auf pH-Wert, Kationenaustauschkapazität, Korngrößenzusammensetzung, Kohlenstoff- und Stickstoffgehalt sowie maßgeblich auf den Gehalt an Schwermetallen untersucht. Es handelt sich um saure und nährstoffarme, tropische Böden, deren Gehalte an toxischen Elementen unterhalb der Maßnahmewerte der Bundesboden-schutzverordnung (1999) und der Zielwerte der internationalen Hollandliste liegen. Von einer Gefährdung der Bevölkerung ist in diesem Gebiet nicht auszugehen, was aber in benachbarten Bergbaugebieten und speziellen hydrothermalen Zonen im Untergrund nicht auszuschließen ist

Electric current circuits in astrophysics

Cosmic magnetic structures have in common that they are anchored in a dynamo, that an external driver converts kinetic energy into internal magnetic energy, that this magnetic energy is transported as Poynting fl ux across the magnetically dominated structure, and that the magnetic energy is released in the form of particle acceleration, heating, bulk motion, MHD waves, and radiation. The investigation of the electric current system is particularly illuminating as to the course of events and the physics involved. We demonstrate this for the radio pulsar wind, the solar flare, and terrestrial magnetic storms

Crop growth and soil water fluxes at erosion‐affected arable sites: Using weighing lysimeter data for model intercomparison

Agroecosystem models need to reliably simulate all biophysical processes that control crop growth, particularly the soil water fluxes and nutrient dynamics. As a result of the erosion history, truncated and colluvial soil profiles coexist in arable fields. The erosion‐affected field‐scale soil spatial heterogeneity may limit agroecosystem model predictions. The objective was to identify the variation in the importance of soil properties and soil profile modifications in agroecosystem models for both agronomic and environmental performance. Four lysimeters with different soil types were used that cover the range of soil variability in an erosion‐affected hummocky agricultural landscape. Twelve models were calibrated on crop phenological stages, and model performance was tested against observed grain yield, aboveground biomass, leaf area index, actual evapotranspiration, drainage, and soil water content. Despite considering identical input data, the predictive capability among models was highly diverse. Neither a single crop model nor the multi‐model mean was able to capture the observed differences between the four soil profiles in agronomic and environmental variables. The model's sensitivity to soil‐related parameters was apparently limited and dependent on model structure and parameterization. Information on phenology alone seemed insufficient to calibrate crop models. The results demonstrated model‐specific differences in the impact of soil variability and suggested that soil matters in predictive agroecosystem models. Soil processes need to receive greater attention in field‐scale agroecosystem modeling; high‐precision weighable lysimeters can provide valuable data for improving the description of soil–vegetation–atmosphere process in the tested models

Proposal and extensive test of a calibration protocol for crop phenology models

Funding Information: Open Access funding enabled and organized by Projekt DEAL. This study was implemented as a co-operative project under the umbrella of the Agricultural Model Intercomparison and Improvement Project (AgMIP). This work was supported by the Academy of Finland through projects AI-CropPro (316172 and 315896) and DivCSA (316215) and Natural Resources Institute Finland (Luke) through a strategic project EFFI, the German Federal Ministry of Education and Research (BMBF) in the framework of the funding measure “Soil as a Sustainable Resource for the Bioeconomy - BonaRes”, project “BonaRes (Module B, Phase 3): BonaRes Centre for Soil Research, subproject B” (grant 031B1064B), the BonaRes project “I4S” (031B0513I) of the Federal Ministry of Education and Research (BMBF), Germany, the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2070 -390732324 EXC (PhenoRob), the Ministry of Education, Youth and Sports of Czech Republic through SustES - Adaption strategies for sustainable ecosystem services and food security under adverse environmental conditions (project no. CZ.02.1.01/0.0/0.0/16_019/000797), the Agriculture and Agri-Food Canada’s Project J-002303 “Sustainable crop production in Canada under climate change” under the Interdepartmental Research Initiative in Agriculture, the JPI FACCE MACSUR2 project, funded by the Italian Ministry for Agricultural, Food, and Forestry Policies (D.M. 24064/7303/15 of 6/Nov/2015), and the INRAE CLIMAE meta-program and AgroEcoSystem department. The order in which the donors are listed is arbitrary. Publisher Copyright: © 2023, The Author(s).A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. We propose an original, detailed approach for calibration of such models, which we refer to as a calibration protocol. The protocol covers all the steps in the calibration workflow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values, and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are “obligatory” parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their “usual” calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and reduced prediction error by 11%.Peer reviewe

Multi-model evaluation of phenology prediction for wheat in Australia

Predicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multi-modeling group mean and median had prediction errors nearly as small as the best modeling group. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. There was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques.Peer reviewe

How well do crop modeling groups predict wheat phenology, given calibration data from the target population?

International audiencePredicting phenology is essential for adapting varieties to different environmental conditions and for crop management. Therefore, it is important to evaluate how well different crop modeling groups can predict phenology. Multiple evaluation studies have been previously published, but it is still difficult to generalize the findings from such studies since they often test some specific aspect of extrapolation to new conditions, or do not test on data that is truly independent of the data used for calibration. In this study, we analyzed the prediction of wheat phenology in Northern France under observed weather and current management, which is a problem of practical importance for wheat management. The results of 27 modeling groups are evaluated, where modeling group encompasses model structure, i.e. the model equations, the calibration method and the values of those parameters not affected by calibration. The data for calibration and evaluation are sampled from the same target population, thus extrapolation is limited. The calibration and evaluation data have neither year nor site in common, to guarantee rigorous evaluation of prediction for new weather and sites. The best modeling groups, and also the mean and median of the simulations, have a mean absolute error (MAE) of about 3 days, which is comparable to the measurement error. Almost all models do better than using average number of days or average sum of degree days to predict phenology. On the other hand, there are important differences between modeling groups, due to model structural differences and to differences between groups using the same model structure, which emphasizes that model structure alone does not completely determine prediction accuracy. In addition to providing information for our specific environments and varieties, these results are a useful contribution to a knowledge base of how well modeling groups can predict phenology, when provided with calibration data from the target population

The Life Cycle of Active Region Magnetic Fields

International audienc