Evaluation of three different regional climate change scenarios for the application of a water balance model in a mesoscale catchment in Northeast Germany

Future climate changes might have some impacts on catchment hydrology. An assessment of such impacts on e.g. ground water recharge is required to derive adaptation strategies for future water resources management. The main objective of our study was an analysis of three different regional climate change scenarios for a catchment with an area of 2415 km² located in the Northeastern German lowlands. These data sets consist of the STAR-scenario with a time period 1951–2055, the WettReg-scenario covering the period 1961–2100 and the grid based REMO-scenario for the time span 1950–2100. All three data sets are based on the SRES scenario A1B of the IPCC. In our analysis, we compared the meteorological data for the control period obtained from the regional climate change scenarios with corresponding data measured at meteorological stations in the catchment. The results of this analysis indicated, that there are high differences between the different regional climate change scenarios regarding the temporal dynamics and the amount of precipitation. In addition, we applied a water balance model using input data obtained from the different climate change scenarios and analyzed the impact of these different input data on the model output groundwater recharge. The results of our study indicated, that these regional climate change scenarios due to the uncertainties in the projections of precipitation show only a limited suitability for hydrologic impact analysis used for the establishment of future concrete water management procedures in their present state

Simulating crop rotations and management across climatic zones in Europe – an intercomparison study using fifteen models

Process based crop simulation models are widely used to assess crop production under current or future climate conditions. Most studies on climate impacts on crop growth are so far focussed on single crops and single-year simulations. However, it is known that the position of crops within a rotation can influence crop growth significantly due to carry-over effects between seasons. We compared crop models on crop rotation effects from five sites across Central Europe providing in total data of 301 cropping seasons and treatments. Treatments comprised irrigation, nitrogen (N) fertilisation, atmospheric [CO2], tillage, residue management, cover crops and soils. Crop rotations were simulated with 15 crop models as single-year simulations and/or continuous simulations over whole crop rotations in “restricted calibration” runs. Lower RMSE between observed and simulated crop yields were obtained for continuous runs as compared to single-year runs. Relatively low carry-over effects were observed due to equilibration of soil water over winter and high N fertilisation levels. Consistently, a sub-set of models applied to an additional rainfed Mediterranean site reproduced larger carry-over effects of soil water. Irrigation, N supply, cover crops and atmospheric [CO2] showed clearer effects than tillage and crop residue management. Model performance varied distinctly between crops showing the necessity to provide experimental data for model calibration also for less prominent crops

Energiepflanzenanbau für Biogasanlagen: Das Zweikulturnutzungssystem – ganzjähriger Bodenschutz und Reduzierung von Nitratausträgen bei stabilen Erträgen

Die Folge einer einseitigen Energiepflanzenproduktion mit Mais wären zunehmende Anbauprobleme und Umweltgefährdungen (Bodenerosion, Nitratauswaschung, Krankheiten und Schädlinge, vermehrter Pflanzenschutzmitteleinsatz). Zweikulturnutzungssysteme stellen eine Alternative dar, die neben hohen und sicheren Erträgen Umweltbelastungen und Anbau-probleme reduzieren bzw. vermeiden. Der Beitrag stellt die Vorteile solcher Systeme im Hinblick auf die oben genannten Problemfelder vor und vergleicht die Erträge mit konventionellen Hauptfruchtsystemen

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

Is dry soil planting an adaptation strategy for maize cultivation in semi-arid Tanzania?

Agriculture has the greatest potential to lift the African continent out of poverty and alleviate hunger. Among the countries in sub-Saharan Africa, Tanzania has an abundance of natural resources and major agricultural potential. However, one of the most important constraints facing Tanzania’s agricultural sector is the dependence on unreliable and irregular weather, including rainfall. A strategy to cope with climate uncertainty in semi-arid regions is to proceed with the sowing of the crop before the onset of the rainy season. The advantage is that when the rains start, seeds are already in the soil and can begin immediately the process of germination. The objective of this paper was to assess the effectiveness of dry-soil planting for maize as an adaptation strategy in the context of a changing climate in Dodoma, a semi-arid region in Tanzania. For this assessment, the DSSAT crop model was used in combination with climate scenarios based on representative concentration pathways. A probability of crop failure of more than 80% can be expected when sowing occurs during the planting window (of 21 days) starting on 1st November. The next planting window we assessed, starting on 23rd November (which was still before the onset of rain), presented significantly lower probabilities of crop failure, indicating that sowing before the onset of the rainy season is a suitable adaptation strategy. Results also indicated that, despite not reaching the highest maize grain yields, fields prepared for dry-soil planting still produced adequate yields. The cultivation of several fields using the dry planting method is a strategy farmers can use to cope with low rainfall conditions, since it increases the chances of harvesting at least some of the cultivated fields. We conclude that dry-soil planting is a feasible and valid technique, even in scenarios of climate change, in order to provide acceptable maize yields in semi-arid Tanzania

Paper on model responses to selected adverse weather conditions

Based on the Trnka et al. (2015) study that indicated that heat and drought will be the most important stress factors for most of the European what area the further effort focused on these two extremes. The crop model HERMES has been tested for its ability to replicate correctly drought stress, heat stress and combination of both stresses. While data on the drought stress were available for both field and growth chambers, heat stress and its combination with heat stress was available only for the growth chambers. The modified version of the HERMES crop model was developed by Dr. Kersebaum and is being currently prepared for the journal paper publication.

Future area expansion outweighs increasing drought risk for soybean in Europe

The European Union is highly dependent on soybean imports from overseas to meet its protein demands. Individual Member States have been quick to declare self-sufficiency targets for plant-based proteins, but detailed strategies are still lacking. Rising global temperatures have painted an image of a bright future for soybean production in Europe, but emerging climatic risks such as drought have so far not been included in any of those outlooks. Here, we present simulations of future soybean production and the most prominent risk factors across Europe using an ensemble of climate and soybean growth models. Projections suggest a substantial increase in potential soybean production area and productivity in Central Europe, while southern European production would become increasingly dependent on supplementary irrigation. Average productivity would rise by 8.3% (RCP 4.5) to 8.7% (RCP 8.5) as a result of improved growing conditions (plant physiology benefiting from rising temperature and CO2 levels) and farmers adapting to them by using cultivars with longer phenological cycles. Suitable production area would rise by 31.4% (RCP 4.5) to 37.7% (RCP 8.5) by the mid-century, contributing considerably more than productivity increase to the production potential for closing the protein gap in Europe. While wet conditions at harvest and incidental cold spells are the current key challenges for extending soybean production, the models and climate data analysis anticipate that drought and heat will become the dominant limitations in the future. Breeding for heat-tolerant and water-efficient genotypes is needed to further improve soybean adaptation to changing climatic conditions

Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop growth models

We compared the performance of eight widely used, easily accessible and well-documented crop growth simulation models (APES, CROPSYST, DAISY, DSSAT, FASSET, HERMES, STICS and WOFOST) for winter wheat (Triticum aestivum L.) during 49 growing seasons at eight sites in northwestern, Central and southeastern Europe. The aim was to examine how different process-based crop models perform at the field scale when provided with a limited set of information for model calibration and simulation, reflecting the typical use of models for large-scale applications, and to present the uncertainties related to this type of model application. Data used in the simulations consisted of daily weather statistics, information on soil properties, information on crop phenology for each cultivar, and basic crop and soil management information. Our results showed that none of the models perfectly reproduced recorded observations at all sites and in all years, and none could unequivocally be labelled robust and accurate in terms of yield prediction across different environments and crop cultivars with only minimum calibration. The best performance regarding yield estimation was for DAISY and DSSAT, for which the RMSE values were lowest (1428 and 1603 kg ha−1) and the index of agreement (0.71 and 0.74) highest. CROPSYST systematically underestimated yields (MBE – 1186 kg ha−1), whereas HERMES, STICS and WOFOST clearly overestimated them (MBE 1174, 1272 and 1213 kg ha−1, respectively). APES, DAISY, HERMES, STICS and WOFOST furnished high total above-ground biomass estimates, whereas CROPSYST, DSSAT and FASSET provided low total above-ground estimates. Consequently, DSSAT and FASSET produced very high harvest index values, followed by HERMES and WOFOST. APES and DAISY, on the other hand, returned low harvest index values. In spite of phenological observations being provided, the calibration results for wheat phenology, i.e. estimated dates of anthesis and maturity, were surprisingly variable, with the largest RMSE for anthesis being generated by APES (20.2 days) and for maturity by HERMES (12.6). The wide range of grain yield estimates provided by the models for all sites and years reflects substantial uncertainties in model estimates achieved with only minimum calibration. Mean predictions from the eight models, on the other hand, were in good agreement with measured data. This applies to both results across all sites and seasons as well as to prediction of observed yield variability at single sites – a very important finding that supports the use of multi-model estimates rather than reliance on single model

Variability in the water footprint of arable crop production across European regions

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