Bewertung der Chancen und Risiken des Energiepflanzenanbaus vor dem Hintergrund der Wasserrahmenrichtlinie und Ableitung erster Handlungsempfehlungen

Ein wesentliches Ziel der EU Wasserrahmenrichtlinie zur Sicherung der Grundwasserqualität ist die Einhaltung des 50 mg NO3/l Grenzwertes. Die Gestaltung von Anbauverfahren für Energiepflanzen hat wesentlichen Einfluss auf den Stickstoffaustrag in das Grundwasser. In diesem Artikel werden Haupt- und Zweitfruchtanbau sowie etablierte neue Fruchtarten untersucht. Die Anbauverfahren werden Bewertet auf Basis von Nmin-Werten nach der Ernte sowie vor Winter. Versuchsbasis sind 6-jährige Parzellenversuche an 8 Standorten. Ergebnisse zeigen, dass gerade bei Mais N-Einsparpotenziale vorhanden sind, die in i.d.R. auch ohne Ertragsverluste eine Senkung der Herbst Nmin Werte und somit der potenziell auswaschungsgefährdeten Stickstoffmengen vor Winter ermöglichen. Der Energiepflanzenanbau bietet durch die breite Palette einsetzbarer Kulturen die Möglichkeit, auf Grenzstandorten ergänzend zum Mais, verstärkt Winterungen wie Roggen und Triticale mit Ganzpflanzennutzung oder Sorghum-Arten anzubauen. Diese haben ein geringeres N-Düngeniveau, sowie das Potenzial die Nmin-Werte nach der Ernte und vor Winter zu senken. Die im Energiepflanzenanbau vorhandenen Potenziale zum grundwasserschonenden Wirtschaften müssen genutzt und in die Praxis getragen werden

Wassererosion auf Silomaisflächen – eine vergleichende Studie verschiedener Anbauverfahren

Der Anbau von Silomais als Substrat für Biogasanlagen hat in den letzten Jahren erheblich zugenommen. Dabei ist Silomais die mit Abstand am meisten eingesetzte Energiepflanze. Wie bei allen C4-Gräsern ist das Risiko für Wassererosion beim Maisanbau jedoch sehr hoch welches auf die langsame Jugendentwicklung mit geringer Bodenbedeckung bis in den Juli hinein, zurückzuführen ist. In diesem Artikel wird am Beispiel der Anbaufolge Winterweizen – Winterroggen als Winterzwischenfrucht – Silomais beschrieben werden, inwieweit der Anbau von Silomais als Haupt- oder Zweitfrucht die Gefährdung des Bodens durch Wassererosion beeinflusst

Ist Mais gleich Mais? Vergleich der Parametrisierung verschiedener Mais-Sorten am Modell MONICA

Aktuell werden verschiedene Mais-Sorten im Energiepflanzenanbau eingesetzt, darunter speziell für diese Nutzungsform gezüchtete Energiemais-Sorten. Die sortenspezifischen Unterschiede von Mais müssen auch in der Modellierung berücksichtigt werden. Zur Kalibrierung des Modells wurden Messdaten zu Trockenmasseertrag, Stickstoffkonzentration der oberirdischen Biomasse, Bodenbedeckungsgrad, Entwicklungsstadium (BBCH-Stadium), Bodenwassergehalt und Boden-Nmin verwendet. Zur Bewertung der Modellergebnisse wurden verschiedene statistische Indizes verwendet, die auf den Unterschiedenen zwischen beobachteten und gemessenen Werten basieren. Auf Basis der Messdaten des EVA Projekts konnten keine genetisch erklärbaren Abgrenzungen der vier Parametersätze für die untersuchten Mais-Sorten vorgenommen werden. Allerdings zeigte der Parametrisierungsversuch, dass mit Hilfe des Modells Umweltstressfaktoren identifiziert und quantifiziert werden können

Effects of input data aggregation on simulated crop yields in temperate and Mediterranean climates

The modelling exercise for this study was highly supported by partner universities and research institutes in the framework of the MACSUR project and financially supported by the German Federal Ministry of Education and Research BMBF (FKZ 2815ERA01J) in the framework of the funding measure “Soil as a Sustainable Resource for the Bioeconomy – BonaRes”, project “BonaRes (Module B): BonaRes Centre for Soil Research (FKZ BOMA03037514, 031B0026A and 031A608A) and by the Ministry of Agriculture and Food (BMEL) in the framework of the MACSUR project (FKZ 2815ERA01J). In addition, the relevant co-authors from the partner institutes are separately financed by their respective projects. AV, EC, and EL were supported by The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (220-2007-1218) and by the strategic funding ‘Soil-Water-Landscape’ from the faculty of Natural Resources and Agricultural Sciences (Swedish University of Agricultural Sciences). JC thank the INRA ACCAF metaprogramm for funding. KCK, CN, XS and TS were supported by MACSUR2 (FKZ 031B0039C). MK thanks for the funding by the UK BBSRC (BB/N004922/1) and the MAXWELL HPC team of the University of Aberdeen for providing equipment and support for the DailyDayCent simulations. FE acknowledges support by the German Science Foundation (project EW 119/5-1). GRM, TG, and FE thank Andreas Enders and Gunther Krauss (INRES, University of Bonn) for support. The authors also would like to acknowledge the support provided by the BMBF and the valuable comments of the scientists of the Institut für Nutzpflanzenwissenschaften und Ressourcenschutz (INRES), University of Bonn, Germany.Peer reviewedPostprin

Why do crop models diverge substantially in climate impact projections? A comprehensive analysis based on eight barley crop models

Robust projections of climate impact on crop growth and productivity by crop models are key to designing effective adaptations to cope with future climate risk. However, current crop models diverge strongly in their climate impact projections. Previous studies tried to compare or improve crop models regarding the impact of one single climate variable. However, this approach is insufficient, considering that crop growth and yield are affected by the interactive impacts of multiple climate change factors and multiple interrelated biophysical processes. Here, a new comprehensive analysis was conducted to look holistically at the reasons why crop models diverge substantially in climate impact projections and to investigate which biophysical processes and knowledge gaps are key factors affecting this uncertainty and should be given the highest priorities for improvement. First, eight barley models and eight climate projections for the 2050s were applied to investigate the uncertainty from crop model structure in climate impact projections for barley growth and yield at two sites: Jokioinen, Finland (Boreal) and Lleida, Spain (Mediterranean). Sensitivity analyses were then conducted on the responses of major crop processes to major climatic variables including temperature, precipitation, irradiation, and CO2, as well as their interactions, for each of the eight crop models. The results showed that the temperature and CO2 relationships in the models were the major sources of the large discrepancies among the models in climate impact projections. In particular, the impacts of increases in temperature and CO2 on leaf area development were identified as the major causes for the large uncertainty in simulating changes in evapotranspiration, above-ground biomass, and grain yield. Our findings highlight that advancements in understanding the basic processes and thresholds by which climate warming and CO2 increases will affect leaf area development, crop evapotranspiration, photosynthesis, and grain formation in contrasting environments are needed for modeling their impacts.Peer reviewe

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

This work was financially supported by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE), (2851ERA01J). FT and RPR were supported by FACCE MACSUR (3200009600) through the Finnish Ministry of Agriculture and Forestry (MMM). EC, HE and EL were supported by The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (220-2007-1218) and by the strategic funding ‘Soil-Water-Landscape’ from the faculty of Natural Resources and Agricultural Sciences (Swedish University of Agricultural Sciences) and thank professor P-E Jansson (Royal Institute of Technology, Stockholm) for support. JC, HR and DW thank the INRA ACCAF metaprogramm for funding and Eric Casellas from UR MIAT INRA for support. CB was funded by the Helmholtz project “REKLIM—Regional Climate Change”. CK was funded by the HGF Alliance “Remote Sensing and Earth System Dynamics” (EDA). FH was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) under the Grant FOR1695. FE and SS acknowledge support by the German Science Foundation (project EW 119/5-1). HH, GZ, SS, TG and FE thank Andreas Enders and Gunther Krauss (INRES, University of Bonn) for support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands

For spatial crop and agro-systems modelling, there is often a discrepancy between the scale of measured driving data and the target resolution. Spatial data aggregation is often necessary, which can introduce additional uncertainty into the simulation results. Previous studies have shown that climate data aggregation has little effect on simulation of phenological stages, but effects on net primary production (NPP) might still be expected through changing the length of the growing season and the period of grain filling. This study investigates the impact of spatial climate data aggregation on NPP simulation results, applying eleven different models for the same study region (∼34,000 km2), situated in Western Germany. To isolate effects of climate, soil data and management were assumed to be constant over the entire study area and over the entire study period of 29 years. Two crops, winter wheat and silage maize, were tested as monocultures. Compared to the impact of climate data aggregation on yield, the effect on NPP is in a similar range, but is slightly lower, with only small impacts on averages over the entire simulation period and study region. Maximum differences between the five scales in the range of 1–100 km grid cells show changes of 0.4–7.8% and 0.0–4.8% for wheat and maize, respectively, whereas the simulated potential NPP averages of the models show a wide range (1.9–4.2 g C m−2 d−1 and 2.7–6.1 g C m−2 d−1 for wheat and maize, respectively). The impact of the spatial aggregation was also tested for shorter time periods, to see if impacts over shorter periods attenuate over longer periods. The results show larger impacts for single years (up to 9.4% for wheat and up to 13.6% for maize). An analysis of extreme weather conditions shows an aggregation effect in vulnerability up to 12.8% and 15.5% between the different resolutions for wheat and maize, respectively. Simulations of NPP averages over larger areas (e.g. regional scale) and longer time periods (several years) are relatively insensitive to climate data aggregation. However, the scale of climate data is more relevant for impacts on annual averages of NPP or if the period is strongly affected or dominated by drought stress. There should be an awareness of the greater uncertainty for the NPP values in these situations if data are not available at high resolution. On the other hand, the results suggest that there is no need to simulate at high resolution for long term regional NPP averages based on the simplified assumptions (soil and management constant in time and space) used in this study

FAIRagro: Ein Konsortium in der Nationalen Forschungsdateninfrastruktur (NFDI) für Forschungsdaten in der Agrosystemforschung : Herausforderungen und Lösungsansätze für den Aufbau einer FAIRen Forschungsdateninfrastruktur

FAIRagro ist ein Konsortium in der Nationalen Forschungsdateninfrastruktur (NFDI) in Deutschland um Forschungsdaten der Agrosystemforschung FAIR – d. h. auffindbar (F), zugänglich (A), interoperabel (I) und für andere Forschende domänenübergreifend nachnutzbar (R) zu machen. In der deutschen Forschungslandschaft rund um nachhaltige Agrosysteme werden heterogene Forschungsdaten erhoben und nur zum Teil in existierenden Forschungsdatenrepositorien veröffentlicht. Das Spektrum der Datenformate erstreckt sich beispielsweise von Laborergebnissen, über Satellitenbilder bis hin zu qualitativen Interviews mit Landwirt:innen. Um diese Daten zukünftig für Forschende verschiedener Disziplinen besser auffindbar und nachnutzbar zu machen, wird FAIRagro eine Forschungsdateninfrastruktur (FDI) für die Agrosystemforschung einrichten, in der disziplinäre Dateninfrastrukturen miteinander verknüpft werden. Spezifische Herausforderungen im Forschungsdatenmanagement (FDM) fachlicher Disziplinen wie Pflanzenzüchtung, integrierter Pflanzenschutz oder Agrarrobotik werden als Use Cases in FAIRagro adressiert und für diese Lösungen entwickelt. Darüber hinaus wird FAIRagro ein Netzwerk aus direkten Ansprechpersonen für Fragen zum Forschungsdatenmanagement in der Agrosystem-Community bereitstellen. In Übereinstimmung mit den Zielsetzungen der NFDI und der European Open Science Cloud ist FAIRagro aktiv an der konzeptionellen Implementierung eines interoperablen Datenraums beteiligt

Effects of climate input data aggregation on modelling regional crop yields

Crop models can be sensitive to climate input data aggregation and this response may differ among models. This should be considered when applying field-scale models for assessment of climate change impacts on larger spatial scales or when coupling models across scales. In order to evaluate these effects systematically, an ensemble of ten crop models was run with climate input data on different spatial aggregations ranging from 1, 10, 25, 50 and 100 km horizontal resolution for the state of North Rhine-Westphalia, Germany. Models were minimally calibrated to typical sowing and harvest dates, and crop yields observed in the region, subsequently simulating potential, water-limited and nitrogen-limited production of winter wheat and silage maize for 1982-2011. Outputs were analysed for 19 variables (yield, evapotranspiration, soil organic carbon, etc.). In this study the sensitivity of the individual models and the model ensemble in response to input data aggregation is assessed for crop yield. Results show that the mean yield of the region calculated from climate time series of 1 km horizontal resolution changes only little when using climate input data of higher aggregation levels for most models. However, yield frequency distributions change with aggregation, resembling observed data better with increasing resolution. With few exceptions, these results apply to the two crops and three production situations (potential, water-, nitrogen-limited) and across models including the model ensemble, regardless of differences among models in simulated yield levels and spatial yield patterns. Results of this study improve the confidence of using crop models at varying scales

The chaos in calibrating crop models

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of system models and has an important impact on simulated values. Here we propose and illustrate a novel method of developing guidelines for calibration of system models. Our example is calibration of the phenology component of crop models. The approach is based on a multi-model study, where all teams are provided with the same data and asked to return simulations for the same conditions. All teams are asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices.HighlightsWe propose a new approach to deriving calibration recommendations for system modelsApproach is based on analyzing calibration in multi-model simulation exercisesResulting recommendations are holistic and anchored in actual practiceWe apply the approach to calibration of crop models used to simulate phenologyRecommendations concern: objective function, parameters to estimate, software usedCompeting Interest StatementThe authors have declared no competing interest