Institut für Bodenkunde und Standortslehre

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Effects of elevated soil temperature and altered precipitation patterns on N-cycling and production of N2O and CO2 in an agricultural soil
(2016) Latt, Yadana Khin; Kandeler, Ellen
Both temperature and precipitation regimes are expected to change with climate change and are, at the same time, major environmental factors regulating biogeochemical cycles in terrestrial ecosystems. Therefore, crop water availability, soil nitrogen transformations, losses, and uptake by plants as well as CO2 emissions from soil are likely to be changed by climate change. Agriculture is known to be one of the most important human activities for releasing significant amounts of N2O and CO2 to the atmosphere. Due to global concern about the changing climate, there has been a great interest in reducing emissions of N2O and CO2 from agricultural soils. CO2 and N2O are produced in soil primarily by microbial processes. Their production and emissions from the soil are controlled by a number of environmental variables including inorganic N availability, soil temperature and water content. Agricultural management practices, such as irrigation, affect these environmental variables and thus have the potential to dramatically alter N2O and CO2 emissions from the soil. The present study is titled "Effects of elevated soil temperature and altered precipitation patterns on N cycling and production of N2O and CO2 in an agricultural soil". The objectives of this study were: to determine the effects of elevated soil temperature on N cycling in a winter wheat cropping system, to investigate the short-term response of N2O and CO2 fluxes during rewetting of soils after extended dry periods in summer, and to determine the effects of different degrees of rewetting on the CO2 emission peaks after rewetting in laboratory incubations. In the 1st experiment, we used the Hohenheim Climate Change (HoCC) experiment in Stuttgart, Germany, to test the hypothesis that elevated soil temperature will increase microbial N cycling, plant N uptake and wheat growth. In the HoCC experiment, soil temperature is elevated by 2.5°C at 4 cm depth. This experiment was conducted at non-roofed plots (1m x 1m) with ambient (Ta) and elevated (Te) soil temperature and with ambient precipitation. In 2012, winter wheat (Triticum aestivum) was planted. C and N concentrations in soil and aboveground plant fractions, soil microbial biomass C and N (Cmic and Nmic), mineral N content (NH4+ - N and NO3- - N), potential nitrification and enzymes involved in nitrogen cycling were analyzed at soil depths of 0-15 and 15-30 cm at five sampling dates. The plants were rated weekly for their phenological development and senescence behavior. We found that an increase in soil temperature by 2.5oC did not have a persistent effect on mineral N content and the activity of potential nitrification within the soil. Plant growth development also did not respond to increased soil temperature. However microbial biomass C and N, and some enzyme activities involved in N-cycling, tended to increase under elevated soil temperature. Overall, the results of this study suggested that soil warming by 2.5oC slightly stimulates soil N cycling but does not alter plant growth development. In the 2nd experiment, in 2013, the effects of a change in the amount and frequency of precipitation patterns on N2O and CO2 emissions were studied after the two dry periods in summer in the HoCC experiment. N2O and CO2 gas samples were taken from four subplots (1m x 1m) of each roofed plot exposed to ambient (Ta) or elevated (Te) soil temperature and four precipitation manipulations (ambient plot, reduced precipitation amount, reduced precipitation frequency, and reduced precipitation amount and frequency). We found that CO2 emissions were affected only by temperature, but not by precipitation pattern. It can be said that N2O and CO2 emissions after rewetting of dry soil were not altered by changing precipitation patterns during dry periods in summer. In the year 2014, using laboratory incubations, we also measured the short-term response of CO2 production to a rewetting of dry soil to different volumetric water contents for 24 hours. This study was conducted by manipulating microcosms with agricultural soil from the HoCC experimental site, which had been exposed to severe drought conditions of three months duration for each of the last six years. The results showed that CO2 production increased with increases in the water content of soils by rewetting at 5%, 15%, 25%, 35% and 45% VWC. With increasing water additions more peaks in CO2 production were detected and different temporal patterns of CO2 emission were affected by adding different amounts of water. It might be due to the fact that with greater water additions successively larger pore sizes were water filled and therefore different bacterial groups located in different pore size classes might have contributed to CO2 production. In summary, the results from field study suggested that climate warming will affect N cycling in soils in an agricultural cropping system. The results from both field and microcosm rewetting experiments contribute to a better understanding of C and N dynamics in soil by investigating the effect of varying soil water content on the emission of N2O and CO2.
Multi-objective and multi-variate global sensitivity analysis of the soil-crop model XN-CERES in Southwest Germany
(2021) Witte, Irene; Streck, Thilo
Soil-crop models enjoy ever-greater popularity as tools to assess the im- pact of environmental changes or management strategies on agricultural production. Soil-crop models are designed to coherently simulate the crop, nitrogen (N) and water dynamics of agricultural fields. However, soil-crop models depend on a vast number of uncertain model inputs, i.e., initial conditions and parameters. To assess the uncertainty in the simulation results (UCSR) and how they can be apportioned among the model inputs of the XN-CERES soil-crop model, an uncertainty and global sensitivity analysis (GSA) was conducted. We applied two different GSA methods, moment-independent and variance-based methods in the sense of the Factor Prioritization and the Factor Fixing setting. The former identifies the key drivers of uncertainty, i.e., which model input, if fixed to its true value, would lead to the greatest reduction of the UCSR. The latter identifies the model inputs that cannot be fixed at any value within their value range without affecting the UCSR. In total we calculated six sensitivity indices (SIs). The overall objective was to assess the cross-sub-model impact of parameters and the overall determinability of the XN-CERES applied on a deep loess soil profile in Southwest Germany. Therefore, we selected 39 parameters and 16 target variables (TGVs) to be included in the GSA. Furthermore, we assessed a weekly time series of the parameter sensitivities. The sub-models were crop, water, nitrogen and flux. In addition, we also compared moment-independent (MI) and variance-based (VB) GSA methods for their suitability for the two settings. The results show that the parameters of the TGVs of the four groups cannot be considered independently. Each group is impacted by the parameters of the other groups. Crop parameters are most important, followed by the Mualem van Genuchten (MvG) parameters. The nitrate (NO3-) content and the matric potential are the two TGVs that are most affected by the inter- action of parameters, especially crop and MvG parameters. However, the model output of these two TGVs is highly skewed and leptokrutic. Therefore, the variance is an unsuitable representation of the UCSR, and the reliability of the variance-based sensitivity indices SIVB is curtailed. Nitrogen group parameters play an overall minor role for the uncertainty of the whole XN-CERES, but nitrification rates can be calibrated on ammonium (NH4+) measurements. Considering the initial conditions shows the high importance of the initial NO3-; content. If it could be fixed, the uncertainty of crop groups’ TGVs, the matric potential and the N content in the soil could be reduced. Hence, multi-year predictions of yield suffer from uncertainty due to the simulated NO3-; content. Temporally resolved parameter show the big dependence between the crop’s development stage and the other 15 TGVs becomes visible. High temporally resolved measurements of the development stage are important to univocally estimate the crop parameters and reduce the uncertainty in the vegetative and generative biomass. Furthermore, potential periods of water and N-limiting situations are assessed, which is helpful for deriving management strategies. In addition, it become clear that measurement campaigns should be conducted at the simulation start and during the vegetation period to have enough information to calibrate the XN-CERES. Regarding the performance of the different GSA methods and the different SIs, we conclude that the sensitivity measure relying on the Kolmogorov-Smirnov metric (betaks) is most stable. It converges quickly and has no issues with highly skewed and leptokrutic model output distributions. The assessments of the first-effect index and the betaks provide information on the additivity of the model and parameters that cannot be fixed without impacting the simulation results. In summary, we could only identify three parameters that have no direct impact on any TGV at any time and are hence not determinable from any measurements of the TGVs considered. Furthermore, we can conclude that the groups’ parameters should not be calibrated independently because they always affect the uncertainty of the selected TGV directly or via interacting. However, no TGV is suitable to calibrate all parameters. Hence, the calibration of the XN-CERES requires measurements of TGVs from each group, even if the modeler is only interested in one specific TGV, e.g., yield. The GSA should be repeated in a drier climate or with restricted rooting depth. The convergence of the values for the Sobol indices remains an issue. Even larger sample sizes, another convergence criteria or graphical inspection cannot alleviate the issue. However, we can conclude that the sub-models of the XN-CERES cannot be considered in- dependently and that the model does what it is designed for: coherently simulating the crop, N and water dynamics with their interactions.

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