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The aim of TERENO (TERrestrial ENvironmental Observatories) is to collect long-term observation data on the hydrosphere, biosphere, pedosphere, lower atmosphere and anthroposphere along multiple spatial and temporal gradients in climate sensitive regions across Germany. The lysimeter-network SOILCan was installed as a part of TERENO between March and December 2010 within the four observatories. It represents a long-term large-scale experiment to study the effects of climate and management changes in terrestrial ecosystems, with particular focus on the impact of these changes on water, energy and matter fluxes into groundwater and atmosphere. SOILCan primarily focuses on soil hydrology, the carbon and nutrient cycle and plant species diversity. Time series measurements of states and fluxes at high spatial and temporal resolution in the soil and biosphere are combined with remote sensing information for the development and calibration of process-based models simulating impacts of climate change in soil processes at field to regional scale. Within the framework of SOILCan, 132 fully automated lysimeter systems were installed at 14 highly equipped experimental field sites across the four TERENO observatories. Relevant state variables of grassland and arable ecosystems are monitored characterizing climate, hydrology and matter fluxes into the atmosphere and within the hydrosphere as well as plant species diversity. Lysimeters are either being operated at or near their original sampling location or were transferred within or between the four TERENO observatories thereby using temperature and rainfall gradients to mimic future climatic conditions (space for time), which allow measuring impacts of climate change on terrestrial ecosystems. The lysimeters are cultivated as grassland (intensive, extensive and non-used) or arable land, the latter with a standardized crop rotation of winter wheat—winter barley—winter rye—oat. This publication describes the general design of the SOILCan experiment including a comprehensive description of the pedological characteristics of the different sites and presents a few exemplary results from the first years of operation.

A leaf stomatal conductance model was combined with a hydrological tree and soil water flow model and a spatially explicit three‐dimensional canopy light model. The model was applied to single, old‐growth Fagus sylvatica L. trees, and the measured daily values of stem sap flux could be reproduced with a normalized root mean square error of 0.10 for an observation period of 32 days in the summer of 2009. The high temporal resolution of the model also makes it possible to simulate the diurnal dynamics of transpiration, stem sap flux, and root water uptake. We applied new data‐processing algorithms to information from terrestrial laser scans to represent the canopies of the functional‐structural model. The high spatial resolution of the root and branch geometry and connectivity makes the detailed modeling of the water usage of single trees possible and allows for the analysis of the interaction between single trees and the influence of the canopy light regime on the water flow inside the xylem. In addition to the laser scans of the observed trees, the model needs tree‐species‐specific physiological input parameters, which are easy to obtain. The model can be applied at various sites and to different tree species, allowing the up‐scaling of the water usage of single trees to the total transpiration of mixed stands. We extend an individual‐based functional‐structural model of tree hydrodynamics Coupling of a light regime model to a stomatal conductance model Model test using stem sap flux and LIDAR measurements

Agriculture is a largely technical endeavour involving complicated managerial decision-making that affects crop performance. Farm-level modelling integrates crop models with agent behaviour to account for farmer decision-making and complete the representation of agricultural systems. To replicate an important part of agriculture in Central Europe a crop model was calibrated for a unique region's predominant crops: winter wheat, winter and spring barley, silage maize and winter rapeseed. Their cultivation was then simulated over multiple decades at daily resolution to test validity and stability, while adding the dimension of agent behaviour in relation to environmental and economic conditions. After validation against regional statistics, simulated future weather scenarios were used to forecast crop management and performance under anticipated global change. Farm management and crop genetics were treated as adaptive variables in the milieu of shifting climatic conditions to allow projections of agriculture in the study region into the coming decades.

A leaf stomatal conductance model was combined with a hydrological tree and soil water flow model and a spatially explicit three‐dimensional canopy light model. The model was applied to single, old‐growthFagus sylvaticaL. trees, and the measured daily values of stem sap flux could be reproduced with a normalized root mean square error of 0.10 for an observation period of 32 days in the summer of 2009. The high temporal resolution of the model also makes it possible to simulate the diurnal dynamics of transpiration, stem sap flux, and root water uptake. We applied new data‐processing algorithms to information from terrestrial laser scans to represent the canopies of the functional‐structural model. The high spatial resolution of the root and branch geometry and connectivity makes the detailed modeling of the water usage of single trees possible and allows for the analysis of the interaction between single trees and the influence of the canopy light regime on the water flow inside the xylem. In addition to the laser scans of the observed trees, the model needs tree‐species‐specific physiological input parameters, which are easy to obtain. The model can be applied at various sites and to different tree species, allowing the up‐scaling of the water usage of single trees to the total transpiration of mixed stands. Key Points We extend an individual‐based functional‐structural model of tree hydrodynamics Coupling of a light regime model to a stomatal conductance model Model test using stem sap flux and LIDAR measurements

BACKGROUND AND AIMS: Patterns and variations in concentration of carbon-based secondary compounds in plant tissues have been explained by means of different complementary and, in some cases, contradictory plant defence hypotheses for more than 20 years. These hypotheses are conceptual models which consider environmental impacts on plant internal demands. In the present study, a mathematical model is presented, which converts and integrates the concepts of the 'Growth-Differentiation Balance' hypothesis and the 'Protein Competition' model into a dynamic plant growth model, that was tested with concentration data of polyphenols in leaves of juvenile apple, beech and spruce trees. The modelling approach is part of the plant growth model PLATHO that considers simultaneously different environmental impacts on the most important physiological processes of plants. METHODS: The modelling approach for plant internal resource allocation is based on a priority scheme assuming that growth processes have priority over allocation to secondary compounds and that growth-related metabolism is more strongly affected by nitrogen deficiency than defence-related secondary metabolism. KEY RESULTS: It is shown that the model can reproduce the effect of nitrogen fertilization on allocation patterns in apple trees and the effects of elevated CO₂ and competition in juvenile beech and spruce trees. The analysis of model behaviour reveals that large fluctuations in plant internal availability of carbon and nitrogen are possible within a single vegetation period. Furthermore, the model displays a non-linear allocation behaviour to carbon-based secondary compounds. CONCLUSIONS: The simulation results corroborate the underlying assumptions of the presented modelling approach for resource partitioning between growth-related primary metabolism and defence-related secondary metabolism. Thus, the dynamical modelling approach, which considers variable source and sink strengths of plant internal resources within different phenological growth stages, presents a successful translation of existing concepts into a dynamic mathematical model.

This simulation study is based on a lysimeter experiment with juvenile beech trees (Fagus sylvatica L.) which were grown under ambient or doubled ambient atmospheric ozone concentrations. The aim of the study was to analyze the role of differences in soil properties, differences in initial biomass and ozone impacts on observed plant growth variability at the eight lysimeters of this experiment. For this purpose, we established a new simulation model based on the model system Expert-N by coupling soil water and nitrogen transport models with the plant growth model PLATHO, which was already tested and applied for juvenile beech. In order to parameterize the soil model, for all lysimeters soil hydraulic parameters as well as carbon and nitrogen stocks were measured. Simulation results reveal that the observed decreased growth rates under elevated ozone are due to ozone impacts on plant growth, whereas the high plant growth variability between lysimeters is to a major part the consequence of differences in soil hydraulic properties. Differences in initial biomass are of minor importance to explain plant growth variability in this experiment.

Projections of climate change impacts on crop yields are inherently uncertain(1). Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate(2). However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models(1,3) are difficult(4). Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking.

Nitrous oxide (N2O) flux simulations by four models were compared with year-round field measurements from five temperate agricultural sites in three countries. The field sites included an unfertilized, semi-arid rangeland with low N2O fluxes in eastern Colorado, USA; two fertilizer treatments (urea and nitrate) on a fertilized grass ley cut for silage in Scotland; and two fertilized, cultivated crop fields in Germany where N2O loss during the winter was quite high. The models used were daily trace gas versions of the CENTURY model, DNDC, ExpertN, and the NASA-Ames version of the CASA model. These models included similar components (soil physics, decomposition, plant growth, and nitrogen transformations), but in some cases used very different algorithms for these processes. All models generated similar results for the general cycling of nitrogen through the agro-ecosystems, but simulated nitrogen trace gas fluxes were quite different. In most cases the simulated N2O fluxes were within a factor of about 2 of the observed annual fluxes, but even when models produced similar N2O fluxes they often produced very different estimates of gaseous N loss as nitric oxide (NO), dinitrogen (N2), and ammonia (NH3). Accurate simulation of soil moisture appears to be a key requirement for reliable simulation of N2O emissions. All models simulated the general pattern of low background fluxes with high fluxes following fertilization at the Scottish sites, but they could not (or were not designed to) accurately capture the observed effects of different fertilizer types on N2O flux. None of the models were able to reliably generate large pulses of N2O during brief winter thaws that were observed at the two German sites. All models except DNDC simulated very low N2O fluxes for the dry site in Colorado. The US Trace Gas Network (TRAGNET) has provided a mechanism for this model and site intercomparison. Additional intercomparisons are needed with these and other models and additional data sets; these should include both tropical agro-ecosystems and new agricultural management techniques designed for sustainability.

Agricultural soils are a major source of the greenhouse gas nitrous oxide (N2O). Nitrous oxide emission models can be used to predict the effectiveness of N2O mitigation strategies; however, these models require rigorous testing before they can be used with confidence. Expert-N, a modular process based N2O emission model, was tested to determine its ability at predicting nitrogen (N) cycling in the soil–plant–atmosphere system under Canadian agroclimatic conditions. Ancillary data and N2O emissions were collected/measured from a corn cultivated clay-loam soil that was under different tillage and red clover treatments. The treatments were conventional till (CT) with and without red clover (rc) underseeded in the previous year's wheat crop (CT-Crc and CT-C, respectively), and no till (NT) with and without red clover underseeded in the previous year's wheat crop (NT-Crc and NT-C, respectively). Expert-N provided good estimates of N2O emissions, and predictions correlated well (positive) with the measured emissions (r 2∼ 0.55–0.83). There was no statistically significant difference between measured and predicted daily emissions. The predicted emissions, integrated over the growing season (25 May–4 October, 1995), were 0.56, 0.57, 0.62, and 0.62 kg N2O-N ha−1 for CT-C, CT-Crc, NT-C, and NT-Crc, respectively. The measured emissions over the same period were 1.29, 1.07, 0.96, and 1.04 kg N2O-N ha−1 for CT-C, CT-Crc, NT-C, and NT-Crc, respectively. The modelled emissions underestimated the integrated measured emissions by ∼ 35–55%; however, the integrated measured emissions had an estimated uncertainty of ±35%. The model provided good predictions of the soil temperatures, moisture contents, and soil nitrate levels with no significant difference from the measured data. Correlations between modelled and measured values for these soil properties in the first 30 cm soil layer were positive and high with r 2 0.71–0.93.

Crop models are essential tools for assessing the threat of climate change to local and global food production(1). Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature(2). Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32 degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degrees C of further temperature increase and become more variable over space and time.