Modeling cotton (Gossypium spp.) leaves and canopy using computer aided geometric design (CAGD)

The research presented here develops a geometrically accurate model of cotton crop canopies that can be used to explore changes in canopy microenvironment and physiological function with leaf structure. We develop an accurate representation of the leaves, including changes in three-dimensional folding and orientation with age and cultivar. Photogrammetrical analysis of leaf surfaces is used to generate measured points at known positions. Interpolation of points located on the surface of the cotton leaves is then performed with a tensor product interpolants model that generates a generic leaf shape. Dynamic changes in leaf shape and canopy position over the growing season are based on measurements of cotton canopies in the field, and are used to modulate the generic leaf shape. The simulated leaves populate a canopy element based on statistical distributions from measured crop canopies. The simulation is found to give a good representation of cotton canopy leaves, adequately capturing the three-dimensional structure of the leaves and changes in leaf shape and size over the growing season. The simulated canopy accurately estimates leaf area index, except for the earliest measurement period prior to canopy closure. The application of the CAGD algorithm for representing cotton leaf and canopy geometry, and the technique for changing the leaves’ spatial position, size and shape through time of four representative cotton canopies is found to be a useful tool for developing a realistic crop canopy. We use leaf area index (LAI) as a measure of the accuracy of model-predicted LAI values in comparison to LAI in crop canopies in situ, obtaining r² values ranging from 0.82 to 0.92. The level of detail captured in the model could contribute greatly to future studies of physiological function and biophysical dynamics within a crop canopy.     

Development and optimization of an Agro-BGC ecosystem model for C4 perennial grasses

Extrapolating simulations of bioenergy crop agro-ecosystems beyond data-rich sites requires biophysically accurate ecosystem models and careful estimation of model parameters not available in the literature. To increase biophysical accuracy we added C₄ perennial grass functionality and agricultural practices to the Biome-BGC (BioGeochemical Cycles) ecosystem model. This new model, Agro-BGC, includes enzyme-driven C₄ photosynthesis, individual live and dead leaf, stem, and root carbon and nitrogen pools, separate senescence and litter fall processes, fruit growth, optional annual seeding, flood irrigation, a growing degree day phenology with a killing frost option, and a disturbance handler that simulates nitrogen fertilization, harvest, fire, and incremental irrigation. To obtain spatially generalizable vegetation parameters we used a numerical method to optimize five unavailable parameters for Panicum virgatum (switchgrass) using biomass yield data from three sites: Mead, Nebraska, Rockspring, Pennsylvania, and Mandan, North Dakota. We then verified simulated switchgrass yields at three independent sites in Illinois (IL). Agro-BGC is more accurate than Biome-BGC in representing the physiology and dynamics of C₄ grass and management practices associated with agro-ecosystems. The simulated two-year average mature yields with single-site Rockspring optimization have Root Mean Square Errors (RMSE) of 70, 152, and 162 and biases of 43, −87, 156 g carbon m⁻² for Shabbona, Urbana, and Simpson IL, respectively. The simulated annual yields in June, August, October, December, and February have RMSEs of 114, 390, and 185 and biases of −19, −258, and 147 g carbon m⁻² for Shabbona, Urbana, and Simpson IL, respectively. These RMSE and bias values are all within the largest 90% confidence interval around respective IL site measurements. Twenty-four of twenty-six simulated annual yields with Rockspring optimization are within 95% confidence intervals of Illinois site measurements during the mature fourth and fifth years of growth. Ten of eleven simulated two-year average mature yields with Rockspring optimization are within 65% confidence intervals of Illinois site measurements and the eleventh is within the 95% confidence interval. Rockspring optimized Agro-BGC achieves accuracies comparable to those of two previously published models: Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) and Integrated Farm System Model (IFSM). Agro-BGC suffers from static vegetation parameters that can change seasonally and as plants age. Using mature plant data for optimization mitigates this deficiency. Our results suggest that a multi-site optimization scheme using mature plant data from more sites would be adequate for generating spatially generalizable vegetation parameters for simulating mature bioenergy crop agro-ecosystems with Agro-BGC.     

Recruitment and attrition of associated plants under a shading crop canopy: Model selection and calibration

Associated plant and animal diversity provides ecosystem services within crop production systems. The importance of the maintenance or restoration of diversity is therefore increasingly acknowledged. Here we study the population dynamics of associated annual plants (‘weeds’) during the growth of a crop in a season and introduce a minimal model to characterize the recruitment and attrition of the associated plants under the influence of shading by the crop. A mechanistically based, logistic, light interception model was parameterized with light interception measurements in two single crops (barley and rye) and in mixtures of these cereals with peas. Population dynamics data were collected for the annuals Papaver rhoeas, Centaurea cyanus, Chrysanthemum segetum, and Misopates orontium. A minimal population dynamics model was identified for each annual plant species, using system identification techniques as model selection and calibration.     

A simplified approach to implement forest eco-hydrological properties in regional hydrological modelling

The topic of this paper is a simplified model for simulating the hydrological properties of forest stands based on a robust computation of the temporal LAI (leaf area index) dynamics. The approach allows the simulation of all hydrologically relevant processes. It includes interception of precipitation and transpiration of forest stands with and without groundwater in the rooting zone. The model also considers phenology, mortality and simple management practice. It was implemented as a module in the eco-hydrological model SWIM (Soil and Water Integrated Model). The approach was tested on Scots pine (Pinus sylvestris) and common oak (Quercus robur and Q. petraea).The results demonstrate a good simulation of annual biomass increase and LAI and satisfactory simulation of litter production (annual mean value). A comparison of the date of May sprout for Scots pine and leaf unfolding for Oak (1980–1990) with observed data of the DWD (German Weather Service) shows a good reproduction of the temporal dynamic. The daily simulation of transpiration shows an excellent correlation of r = 0.81 for the year 1998 but only r = 0.65 for 1999. The interception losses were also simulated and compared with weekly observed data showing satisfactory results in the vegetation periods and annual sums, but worse agreement in autumn and spring time. A regional assessment study was done in the federal state of Brandenburg (Germany) to test the applicability and multi-criteria evaluation capabilities of the approach on the landscape and catchments scale using forest data, daily river discharge and regional water balance.     

Modelling coupled peach tree-aphid population dynamics and their control by winter pruning and nitrogen fertilization

Nitrogen fertilization and winter pruning are commonly used to control crop production in peach [Prunus persica (L.) Batsch] orchards. They are also known to affect the dynamics of Myzus persicae (Sulzer) (Homoptera: Aphididae) aphid populations via bottom-up regulation processes. Interactions between crops and pests can cause complex system behaviour in response to management practices. An integrated approach will therefore improve the understanding of the effects of these two cultural practices on aphid and peach performances.We developed a simulation model that describes the cultural control of interacting peach tree and aphid population dynamics. It uses the principles of common trophic models while gathering available knowledge and explicit assumptions on peach and aphid functioning and the effects of cultural practices.The model was able to qualitatively reproduce the system behaviour observed in the field. It accounted for actions and feedback such as stimulation of foliar growth by winter pruning, consecutive aphid population increase, subsequent damage to foliage, and partial compensatory growth of foliage. The model also reproduced low losses in fruit production due to aphid infestations. However, it called for further integration of ‘long-term’ effects. Analysis of the model showed the complexity of peach tree and aphid responses to leaf N × winter pruning interactions. Simulations indicated that fruit production losses remained low within a range of realistic values of leaf N and pruning intensity, whereas manipulating peach and aphid dynamics, their interactions and their relationships to practices could result in higher losses.The model is useful to evaluate the relevance of cultural practices for a bottom-up regulation of aphid dynamics in crop-pest management. After considering other control methods and fruit quality, it can be used to find a combination of practices that optimises trade-offs between fruit production and environmental conservation goals. A modelling approach that links crop growth and pest population dynamics and integrates management practice effects has strong potential for improving crop-pest management in an integrated crop production context.     

The MONICA model: Testing predictability for crop growth, soil moisture and nitrogen dynamics

A fundamentally revised version of the HERMES agro-ecosystem model, released under the name of MONICA, was calibrated and tested to predict crop growth, soil moisture and nitrogen dynamics for various experimental crop rotations across Germany, including major cereals, sugar beet and maize. The calibration procedure also included crops grown experimentally under elevated atmospheric CO₂ concentration. The calibrated MONICA simulations yielded a median normalised mean absolute error (nMAE) of 0.20 across all observed target variables (n = 42) and a median Willmott's Index of Agreement (d) of 0.91 (median modelling efficiency (ME): 0.75). Although the crop biomass, habitus and soil moisture variables were all within an acceptable range, the model often underperformed for variables related to nitrogen. Uncalibrated MONICA simulations yielded a median nMAE of 0.27 across all observed target variables (n = 85) and a median d of 0.76 (median ME: 0.30), also showing predominantly acceptable results for the crop biomass, habitus and soil moisture variables. Based on the convincing performance of the model under uncalibrated conditions, MONICA can be regarded as a suitable simulation model for use in regional applications. Furthermore, its ability to reproduce the observed crop growth results in free-air carbon enrichment experiments makes it suited to predict agro-ecosystem behaviour under expected future climate conditions.     

Theoretical analysis of change in forest carbon use efficiency with stand development: A case study on Hinoki Cypress (Chamaecyparis obtusa (Sieb. et Zucc.) Endl.) plantation from the seedling stage

Changes in carbon use efficiency (CUE), which is defined as the ratio of net primary production (NPP) to gross primary production (GPP), were estimated for the aerial parts of the Hinoki Cypress (Chamaecyparis obtusa (Sieb. et Zucc.) Endl.) with respect to stand development. The analysis incorporated previously published data from the early stages of stand development, namely the seedling stages of the cypress. For this analysis, a simple mathematical model to assess the changes in CUE was developed by incorporating data on physiological variables and mass of woody species. The CUE tended to increase with increases in the aboveground biomass of the stand, and then decreased gradually despite increases in the aboveground biomass. The CUE-value (0.28, 0.39) of the seedling stage was lower than that (0.33-0.58) of the young or mature trees. To examine the effect of physiological variables and mass on CUE, the ratios of the specific respiration rate to the specific photosynthetic rate (r/a) and the leaf biomass to the aboveground biomass or leaf mass ratio (y_L/y_T) were calculated. The low value of CUE at the seedling stage was due to the high ratio of specific respiration rate to specific photosynthetic rate r/a, but was not due to the high value of the leaf mass ratio y_L/y_T. In addition, the decline in CUE associated with older stages of stand development was due to the decreasing changes in y_L/y_T, and the r/a ratio did not influence the change in CUE.     

Integrating effects of leaf nitrogen,age, rank,and growth temperature into the photosynthesis-stomatal conductance model LEAFC3-N parameterised for barley (Hordeum vulgare L.)

A crucial challenge for including biophysical photosynthesis–transpiration models into complex crop growth models is to integrate the plasticity of photosynthetic processes that is related to factors like nitrogen (N) content, age, and rank of leaves, or to the adaptation of plants to growth temperature (T_g). Here we present a new version of the combined photosynthesis-stomatal conductance model LEAFC3-N [Müller, J., Wernecke, P., Diepenbrock, W., 2005. LEAFC3-N: a nitrogen sensitive extension of the CO₂ and H₂O gas exchange model LEAFC3 parameterised and tested for winter wheat (Triticum aestivum L.). Ecological Modelling 183, 183–210.] that was revised, extended and completely re-parameterised for barley (Hordeum vulgare L.) with special regard for these factors to facilitate the use of the model in ecophysiological studies and in crop modelling. The analysis is based on novel comprehensive data on photosynthetic CO₂ and light response curves measured at two oxygen concentrations and different temperatures on leaves of barley (H. vulgare L.) differing in leaf N and chlorophyll content. Plants were grown in climatic chambers or in the field at different N and T_g.We thoroughly revised the existing and introduced new nitrogen relations for key model parameters that account for a linear increase with leaf N of V_max, J_max, T_p, and R_dmax (maximum rates of carboxylation, electron transport, triose phosphate export, and mitochondrial respiration), a saturation-type increase of φ (quantum yield of electron transport), and a non-linear decrease of θ and m (curvature of the light dependence of electron transport rate, scaling factor of the stomata model). The adaptation of photosynthetic characteristics to T_g was included into the model by linear relations that were observed between T_g and the activation energy ΔH_a of the temperature response characteristics of V_max, J_max, and T_p as well as of the nitrogen dependency of these characteristics. Based on an analysis of diurnal time courses of gas exchange rates it was found necessary including not only the relation between leaf water potential (Ψ) and stomatal conductance as used originally in LEAFC3, but additional effects on V_max and J_max. With the above-listed extensions, the model was capable to reproduce the observed plasticity and the recorded diurnal time courses of gas exchange rates fairly well. Thus, we conclude that the new model version can be used under a broad range of conditions, both for ecophysiological studies and as a submodel of crop growth models. The results presented here for barley will facilitate adapting photosynthesis models like LEAFC3-N to other C₃-species as well. The modelling of the effects of drought stress should be further elaborated in future based on more specific experiments.     

Simulation of interannual responses of trembling aspen stands to climatic variation and insect defoliation in western Canada

A carbon-based model has been developed to simulate responses of trembling aspen (Populus tremuloides Michx.) stands to interannual climatic variation and insect defoliation. The model is designed for medium time scale (10–100 years) simulations and requires only daily maximum and minimum temperature and precipitation as meteorological inputs. The modelling approach is similar to FOREST-BGC but includes additional processes known to be important in deciduous forests. These include removal of leaf area during outbreaks of forest tent caterpillar (Malacosoma disstria Hbn.), phenological changes in leaf area index, storage and allocation of non-structural carbohydrate and the contribution of understorey vegetation to evapotranspiration. The model was used for simulations of growth and mortality of biomass carbon in two mature aspen forests located in the climatically dry transition zone between the boreal forest and prairie grassland regions of Saskatchewan, Canada. Model inputs of annual defoliation intensity were based on historic records of insect defoliation and the incidence of light-coloured tree rings in disks or cores collected from aspen at each of the two sites. At both sites, moderately good correlations (r²=0.47–0.54) were obtained between modelled interannual changes in stem carbon growth and observed interannual changes in stem basal area increment obtained from tree-ring analysis. Model outputs of stem biomass carbon were found to be highly sensitive to parameters describing seasonal leaf area duration, insect defoliation intensity, photosynthesis and root respiration and carbohydrate allocation to growth versus storage.     

Impact of generated solar radiation on simulated crop growth and yield

The availability of observed daily solar radiation (OSR) is restricted to recent years. Its estimation through different methods is necessary to develop long-term data sets for agricultural and environmental applications. The objective of this study was to analyze the impact of using generated daily solar radiation (GSR) on simulated growth and yield of cotton, maize, and peanut. Nine locations representing Georgia's major crop belt were selected. Daily weather data from the Georgia Automated Environmental Monitoring Network (AEMN), including solar radiation, maximum and minimum temperature, and precipitation, were duplicated. The OSR was removed from one set and then generated using a stochastic procedure. The Cropping System Models (CSM)-CROPGRO-Cotton, CERES-Maize, and CROPGRO-Peanut of the Decision Support System for Agrotechnology Transfer (DSSAT) v4 were used to simulate crop growth and yield at each location with both OSR and GSR and for rainfed and irrigated conditions. The statistical analysis included summary statistics, Pearson's coefficient of correlation, mean squared deviation (MSD) and its components, namely: squared bias (SB), squared difference between standard deviations (SDSD), lack of correlation weighted by the standard deviations (LCS), and regressions. Within locations, for the three crops under rainfed and irrigated conditions, GSR did not significantly affect simulated total evapotranspiration and aboveground biomass and yields. For the three crops, deviations of simulated water use and yields from GSR with respect to simulated water use and yields from OSR were lower for the rainfed than for the irrigated conditions. Yields from the CSM-CROPGRO-Cotton and -Peanut models had lower deviations than yields from the CSM-CERES-Maize model. LCS was the major component of the MSD suggesting that the extent of the difference between standard deviations of GSR and OSRG could affect the outputs of the crop models. Nevertheless, for most locations none of the MSD components of the GSR showed significant correlation with simulated yields and the overall performance of the models was not affected. It can be concluded based on the results of this study that GSR can be used as an input for crop model simulation models when OSR is not available.     

Modelling the impact of defoliation and leaf damage on forest plantation function and production

After presenting a short review of process-based model requirements to capture the plant dynamic response to defoliation, this paper describes the development and testing of a model of crown damage and defoliation for Eucalyptus. A model that calculates light interception and photosynthetic production for canopies that vary spatially and temporally in leaf area and photosynthetic properties is linked to the forest growth model CABALA. The process of photosynthetic up-regulation following defoliation is modelled with a simple conditional switch that triggers up-regulation when foliar damage or removal causes the ratio of functional leaf area to living tissue in the tree to change.We show that the model predicts satisfactorily when validated with trees of Eucalyptus nitens and Eucalyptus globulus from a range of sites of different ages, subject to different types of stress and different types of defoliation events (R² = 0.96 across a range of sites). However, the complexity of particular situations can cause the model to fail (e.g. very heavy defoliation events where branch death occurs).It is concluded that while the model will not cope with all situations, an appropriate level of generality has been captured to represent many of the physiological processes and feedbacks that occur following defoliation or leaf damage. This makes the model useful for guiding management interventions following pest attack and allows the development of scenarios including climate change impact analyses and decision-making on the merits of post-defoliation fertilisation to expedite recovery.     

Sensitivity of a crop growth simulation model to variation in LAI and canopy nitrogen used for run-time calibration

Run-time calibration, i.e. adjusting simulation results for field observations of model driving variables during run-time, may allow correcting for deviations between complex mechanistic simulation model results and actual field conditions. Leaf area index (LAI) and canopy nitrogen contents (LeafNWt) are the most important driving variables for these models, as they govern light interception and photosynthetic production capacity of the crop. Remote sensing may provide (spatial) data from which such information can be estimated. How, when and at what frequency such additional information is integrated in the simulation process may have various effects on the simulations. The objective of this study was to quantify the effects of different run-time calibration scenarios for final grain yield (FGY) simulations in order to optimize remote sensing image (RS) acquisition. The PlantSys model was calibrated on LAI and LeafNWt for maize in France and used to simulate maize crop growth in the Argentina and the USA, for which non-destructive estimates of LAI and leaf chlorophyll contents were acquired by optical measurement techniques. Leaf chlorophyll data were used to estimate LeafNWt. Due to its structure, the PlantSys model was more susceptible to run-time calibration with LeafNWt than with LAI. Run-time calibration with LAI showed the largest effect on FGY before and around flowering, and could mainly be related to maintenance respiration costs. Run-time calibration with LeafNWt showed the largest effect on FGY at and after flowering and could mainly be related to the change in effective radiation interception due to change in leaf life. The accuracy of LAI estimates showed a major effect on FGY for underestimations but was small in absolute sense. The accuracy of LeafNWt estimates had significant impact at all crop development stages, but was the strongest after flowering where crop growth and nitrogen uptake are less able to recuperate from changes in LeafNWt. In absolute sense, the effect on FGY was as strong as the accuracy of the LeafNWt estimates when applied in the early reproductive stages. Based on these results it was concluded that remotely sensed in-field variability of LAI and LeafNWt is valuable information that can be used to spatially differentiate model simulations. Run-time calibration at sub-field level may lead to more accurate simulation results for whole fields.     

Climate, canopy conductance and leaf area development controls on evapotranspiration in a boreal coniferous forest over a 10-year period: A united model assessment

The aim of this work was to test a process-based model (hydrological model combined with forest growth model) on the simulation of seasonal variability of evapotranspiration (ET) in an even-aged boreal Scots pine (Pinus sylvestris L.) stand over a 10 year period (1999-2008). The water flux components (including canopy transpiration (E_t) and evaporation from canopy (E_c) and ground surface (E_g) were estimated in order to output the long-term stand water budget considering the interaction between climate variations and stand development. For validation, half-hourly data on eddy water vapor fluxes were measured during the 10 growing seasons (May-September). The model predicted well the seasonal course of ET compared to the measured values, but slightly underestimated the water fluxes both in non-drought and drought (2000, 2003 and 2006) years. The prediction accuracy was, on average, higher in drought years. The simulated ET over the 10 years explained, on average, 58% of the daily variations and 84% of the monthly amount of ET. Water amount from E_t contributed most to the ET, with the fractions of E_t, E_c and E_g being, on average, 67, 11 and 23% over the 10-year period, respectively. Regardless of weather conditions, the daily ET was strongly dependent on air temperature (T_a) and vapor pressure deficit (D_a), but less dependent on soil moisture (W_s). On cloudy and rainy days, there was a non-linear relationship between the ET and solar radiation (R_o). During drought years, the model predicted lower daily canopy stomatal conductance (g_cs) compared with non-drought years, leading to a lower level of E_t. The modeled daily g_cs responded well to D_a and W_s. In the model simulation, the annual LAI increased by 35% between 1999 and 2008. The ratio of E_c: ET correlated strongly with LAI. Furthermore, LAI reduced the proportion of E_g as a result of the increased share of E_c and E_t and radiation interception. Although the increase of LAI affected positively E_t, the contribution of E_t in ET was not significantly correlated with LAI. To conclude, although the model predicted reasonably well the seasonal course of ET, the calculation time steps of different processes in the model should be homogenized in the future to increase the prediction accuracy.     

Modelling small groundwater systems: experiences from the Braunton Burrows and Ainsdale coastal dune systems,UK

Coastal dunes are delicate systems that are under threat from a variety of human and natural influences. Groundwater modelling can provide a better understanding of how these systems operate and can be a useful tool towards the effective management of a coastal dune system, e.g. by identifying strategically important locations for flora and fauna and guiding the planning of management operations through predicting impacts from climatic change, sea level rise and land use management. Most dune systems are small, typically of the size 10–100 km², compared with inland groundwater systems. Applying conventional groundwater modelling approaches to these small systems presents a number of challenges due to the local scale of the system and the fact that the system boundaries (sea, drains, ponds etc.) are close to the main body of the aquifer. In this paper, two case studies will be presented using different modelling approaches to understand the groundwater balance in two dune systems in the UK. The studies demonstrate that, although conventional hydraulic models can describe the general system behaviour, a fuller understanding of the recharge mechanisms and system boundaries is needed to represent adequately system dynamics of small groundwater systems.     

A proposal of an indicator for quantifying model robustness based on the relationship between variability of errors and of explored conditions

The evaluation of biophysical models is usually carried out by estimating the agreement between measured and simulated data and, more rarely, by using indices for other aspects, like model complexity and overparameterization. In spite of the importance of model robustness, especially for large area applications, no proposals for its quantification are available. In this paper, we would like to open a discussion on this issue, proposing a first approach for a quantification of robustness based on the variability of model error to variability of explored conditions ratio. We used modelling efficiency (EF) for quantifying error in model predictions and a normalized agrometeorological index (SAM) based on cumulated rainfall and reference evapotranspiration to characterize the conditions of application. Population standard deviations of EF and SAM were used to quantify their variability. The indicator was tested for models estimating meteorological variables and crop state variables. The values provided by the robustness indicator (I_R) were discussed according to the models’ features and to the typology and number of processes simulated. I_R increased with the number of processes simulated and, within the same typology of model, with the degree of overparameterization. No correlation were found between I_R and two of the most used indices of model error (RRMSE, EF). This supports its inclusion in integrated systems for model evaluation.     

A new hypothesis concerning the nature of small pelagic fish clusters: An individual-based modelling study of Sardinella aurita dynamics off West Africa

Coastal populations of small pelagic fish display nested aggregation levels. Above the level of the school structure, clusters are observed the nature of which has not been definitively determined. We hypothesized that these clusters corresponded to a materialisation of the microcohorts originating from successive spawnings of fish populations in their vital domain.A candidate individual-based model was developed to investigate this hypothesis. This model is based on pattern-oriented modelling of a concrete documented case: the dynamics of the round sardinella (Sardinella aurita) population living off the West African coasts and subject to environmental fluctuations caused by seasonal upwelling. The simulated agents were round sardinella microcohorts situated and moving in a discretised physical environment. The combined effects of environmental forcing (temperature, wind, retention) and inner biological dynamics (reproduction, growth and mortality, competition) condition the dynamics of this population.The modelled behaviour generated realistic dynamic patterns (population distribution, spawning zones, periods and plasticity, biomass fluctuations), which were obtained simultaneously and successfully compared with observations. The steady-state number of microcohorts obtained after simulation convergence was similar to the number of clusters observed in situ in this area for this population.The realism and diversity of the patterns simultaneously simulated suggested the cluster-microcohort equivalence hypothesis as a candidate framework accounting for the origin of the clusters observed in situ. Within this preliminary exploration, we discuss the consistency of the hypothesis and the accuracy of the model. If the correspondence between clusters and microcohorts proves to be real, it may be transient and progressively modified by other environmental factors. If stable over time, as simulated in the model, the number of observed clusters should be related to the number of spawning events in the species’ lifetime.     

Development and test of a crop growth model for application within a Global Change decision support system

When examining potential impacts of Global Change on water resources on the regional scale, spatial and temporal changes in crop water and nitrogen demand are of fundamental significance. State-of-the-art crop growth models are powerful tools to assess the response of crops to altered environmental conditions and cultivation practices. In this paper, the process-based, object-oriented and generic DANUBIA crop growth model is presented. To evaluate the performance of the model, a validation analysis is carried out by comparing modelled data with various field measurements of sugar beet, spring barley, maize, winter wheat and potato crops. Model performance statistics show that crop growth is efficiently simulated. The closest agreement between measured and modelled biomass and leaf area index is achieved for sugar beet and winter wheat. Additionally, the response of the model to changed nitrogen availability caused by cultivation practices is analysed and reveals good results. The results suggest that the model is a suitable tool for numerically assessing the consequences of Global Change on biomass production, water and nitrogen demand, taking into account the complex interplay of water, carbon and nitrogen fluxes in agro-ecosystems.