Filtern
Volltext vorhanden
- nein (6)
Dokumenttyp
Sprache
- Englisch (6)
Gehört zur Bibliographie
- ja (6)
Schlagworte
- Climate change (1)
- Crop modelling (1)
- Equivocality (1)
- Feature selection (1)
- Germany (1)
- Land-use (1)
- Machine learning (1)
- Process-based models (1)
- Random forests (1)
- Soil N2O emissions (1)
Institut
- Institut für Biochemie und Biologie (6) (entfernen)
Landscapes can be viewed as spatially heterogeneous areas encompassing terrestrial and aquatic domains. To date, most landscape carbon (C) fluxes have been estimated by accounting for terrestrial ecosystems, while aquatic ecosystems have been largely neglected. However, a robust assessment of C fluxes on the landscape scale requires the estimation of fluxes within and between both landscape components. Here, we compiled data from the literature on C fluxes across the air–water interface from various landscape components. We simulated C emissions and uptake for five different scenarios which represent a gradient of increasing spatial heterogeneity within a temperate young moraine landscape: (I) a homogeneous landscape with only cropland and large lakes; (II) separation of the terrestrial domain into cropland and forest; (III) further separation into cropland, forest, and grassland; (IV) additional division of the aquatic area into large lakes and peatlands; and (V) further separation of the aquatic area into large lakes, peatlands, running waters, and small water bodies These simulations suggest that C fluxes at the landscape scale might depend on spatial heterogeneity and landscape diversity, among other factors. When we consider spatial heterogeneity and diversity alone, small inland waters appear to play a pivotal and previously underestimated role in landscape greenhouse gas emissions that may be regarded as C hot spots. Approaches focusing on the landscape scale will also enable improved projections of ecosystems’ responses to perturbations, e.g., due to global change and anthropogenic activities, and evaluations of the specific role individual landscape components play in regional C fluxes. WIREs Water 2016, 3:601–617. doi: 10.1002/wat2.1147
The global warming potential of nitrous oxide (N2O) and its long atmospheric lifetime mean its presence in the atmosphere is of major concern, and that methods are required to measure and reduce emissions. Large spatial and temporal variations means, however, that simple extrapolation of measured data is inappropriate, and that other methods of quantification are required. Although process-based models have been developed to simulate these emissions, they often require a large amount of input data that is not available at a regional scale, making regional and global emission estimates difficult to achieve. The spatial extent of organic soils means that quantification of emissions from these soil types is also required, but will not be achievable using a process-based model that has not been developed to simulate soil water contents above field capacity or organic soils. The ECOSSE model was developed to overcome these limitations, and with a requirement for only input data that is readily available at a regional scale, it can be used to quantify regional emissions and directly inform land-use change decisions. ECOSSE includes the major processes of nitrogen (N) turnover, with material being exchanged between pools of SOM at rates modified by temperature, soil moisture, soil pH and crop cover. Evaluation of its performance at site-scale is presented to demonstrate its ability to adequately simulate soil N contents and N2O emissions from cropland soils in Europe. Mitigation scenarios and sensitivity analyses are also presented to demonstrate how ECOSSE can be used to estimate the impact of future climate and land-use change on N2O emissions.
Crop model intercomparison studies have mostly focused on the assessment of predictive capabilities for crop development using weather and basic soil data from the same location. Still challenging is the model performance when considering complex interrelations between soil and crop dynamics under a changing climate. The objective of this study was to test the agronomic crop and environmental flux-related performance of a set of crop models. The aim was to predict weighing lysimeter-based crop (i.e., agronomic) and water-related flux or state data (i.e., environmental) obtained for the same soil monoliths that were taken from their original environment and translocated to regions with different climatic conditions, after model calibration at the original site. Eleven models were deployed in the study. The lysimeter data (2014-2018) were from the Dedelow (Dd), Bad Lauchstadt (BL), and Selhausen (Se) sites of the TERENO (TERrestrial ENvironmental Observatories) SOILCan network. Soil monoliths from Dd were transferred to the drier and warmer BL site and the wetter and warmer Se site, which allowed a comparison of similar soil and crop under varying climatic conditions. The model parameters were calibrated using an identical set of crop- and soil-related data from Dd. Environmental fluxes and crop growth of Dd soil were predicted for conditions at BL and Se sites using the calibrated models. The comparison of predicted and measured data of Dd lysimeters at BL and Se revealed differences among models. At site BL, the crop models predicted agronomic and environmental components similarly well. Model performance values indicate that the environmental components at site Se were better predicted than agronomic ones. The multi-model mean was for most observations the better predictor compared with those of individual models. For Se site conditions, crop models failed to predict site-specific crop development indicating that climatic conditions (i.e., heat stress) were outside the range of variation in the data sets considered for model calibration. For improving predictive ability of crop models (i.e., productivity and fluxes), more attention should be paid to soil-related data (i.e., water fluxes and system states) when simulating soil-crop-climate interrelations in changing climatic conditions.
Large-scale crop yield failures are increasingly associated with food price spikes and food insecurity and are a large source of income risk for farmers. While the evidence linking extreme weather to yield failures is clear, consensus on the broader set of weather drivers and conditions responsible for recent yield failures is lacking. We investigate this for the case of four major crops in Germany over the past 20 years using a combination of machine learning and process-based modelling. Our results confirm that years associated with widespread yield failures across crops were generally associated with severe drought, such as in 2018 and to a lesser extent 2003. However, for years with more localized yield failures and large differences in spatial patterns of yield failures between crops, no single driver or combination of drivers was identified. Relatively large residuals of unexplained variation likely indicate the importance of non-weather related factors, such as management (pest, weed and nutrient management and possible interactions with weather) explaining yield failures. Models to inform adaptation planning at farm, market or policy levels are here suggested to require consideration of cumulative resource capture and use, as well as effects of extreme events, the latter largely missing in process-based models. However, increasingly novel combinations of weather events under climate change may limit the extent to which data driven methods can replace process-based models in risk assessments.
Provisioning a sufficient stable source of food requires sound knowledge about current and upcoming threats to agricultural production. To that end machine learning approaches were used to identify the prevailing climatic and soil hydrological drivers of spatial and temporal yield variability of four crops, comprising 40 years yield data each from 351 counties in Germany. Effects of progress in agricultural management and breeding were subtracted from the data prior the machine learning modelling by fitting smooth non-linear trends to the 95th percentiles of observed yield data. An extensive feature selection approach was followed then to identify the most relevant predictors out of a large set of candidate predictors, comprising various soil and meteorological data. Particular emphasis was placed on studying the uniqueness of identified key predictors. Random Forest and Support Vector Machine models yielded similar although not identical results, capturing between 50% and 70% of the spatial and temporal variance of silage maize, winter barley, winter rapeseed and winter wheat yield. Equally good performance could be achieved with different sets of predictors. Thus identification of the most reliable models could not be based on the outcome of the model study only but required expert's judgement. Relationships between drivers and response often exhibited optimum curves, especially for summer air temperature and precipitation. In contrast, soil moisture clearly proved less relevant compared to meteorological drivers. In view of the expected climate change both excess precipitation and the excess heat effect deserve more attention in breeding as well as in crop modelling.
Identifying the chemical mechanisms behind soil carbon bound in organo-mineral complexes is necessary to determine the degree to which soil organic carbon is stabilized belowground. Analysis of delta C-13 and delta N-15 isotopic signatures of stabilized OM fractions along with soil mineral characteristics may yield important information about OM-mineral associations and their processing history. We anlayzed the delta C-13 and delta N-15 isotopic signatures from two organic matter (OM) fractions along with soil mineral proxies to identify the likely binding mechanisms involved. We analyzed OM fractions hypothesized to contain carbon stabilized through organo-mineral complexes: (1) OM separated chemically with sodium pyrophosphate (OM(PY)) and (2) OM occluded in micro-structures found in the chemical extraction residue (OM(ER)). Because the OM fractions were separated from five different soils with paired forest and arable land use histories, we could address the impact of land use change on carbon binding and processing mechanisms. We used partial least squares regression to analyze patterns in the isotopic signature of OM with established mineral and chemical proxies indicative for certain binding mechanisms. We found different mechanisms predominate in each land use type. For arable soils, the formation of OM(PY)-Ca-mineral associations was identified as an important OM binding mechanism. Therefore, we hypothesize an increased stabilization of microbial processed OM(PY) through Ca2+ interactions. In general, we found the forest soils to contain on average 10% more stabilized carbon relative to total carbon stocks, than the agricultural counter part. In forest soils, we found a positive relationship between isotopic signatures of OM(PY) and the ratio of soil organic carbon content to soil surface area (SOC/SSA). This indicates that the OM(PY) fractions of forest soils represent layers of slower exchange not directly attached to mineral surfaces. From the isotopic composition of the OM(ER) fraction, we conclude that the OM in this fraction from both land use types have undergone a different pathway to stabilization that does not involve microbial processing, which may include OM which is highly protected within soil micro-structures.