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The majority of cultural heritage is stored in archives, libraries and museum storage spaces. To reduce degradation risks, many archives adopt the use of archival boxes, among other means, to provide the necessary climate control and comply with strict legislation requirements regarding temperature and relative air humidity. A strict ambient indoor climate is assumed to provide adequate environmental conditions near objects. Guidelines and legislation provide requirements for ambient indoor climate parameters, but often do not consider other factors that influence the near-object environment, such as the use of archival boxes, airflow distribution and archival rack placement. This study aimed to provide more insight into the relation between the ambient indoor conditions in repositories and the hygrothermal conditions surrounding the collection. Comprehensive measurements were performed in a case study archive to collect ambient, local and near-object conditions. Both measurements and computational fluid dynamics (CFD) modeling were used to research temperature/relative humidity gradients and airflow distribution with a changing rack orientation, climate control strategy and supply as well as exhaust set-up in a repository. The following conclusions are presented: (i) supplying air from one air handling unit to multiple repositories on different floors leads to small temperature differences between them. Differences in ambient and local climates are noticed, (ii) archival boxes mute and delay variations in ambient conditions as expected—however, thermal radiation from the building envelope may have a large influence on the climate conditions in a box, (iii) adopting night reduction for energy conservation results in an increased influence of the external climate, with adequate insulation, this effect should be mitigated, and (iv) the specific locations of the supply air and extraction of air resulted in a vertical gradient of temperature and insufficient mixing of air, and adequate ventilation strategies should enhance sufficient air mixing in combination with the insulation of external walls, and gradient forming should be reduced.

International audience; Robust projections of climate impact on crop growth and productivity by crop models are key to designing effective adaptations to cope with future climate risk. However, current crop models diverge strongly in their climate impact projections. Previous studies tried to compare or improve crop models regarding the impact of one single climate variable. However, this approach is insufficient, considering that crop growth and yield are affected by the interactive impacts of multiple climate change factors and multiple interrelated biophysical processes. Here, a new comprehensive analysis was conducted to look holistically at the reasons why crop models diverge substantially in climate impact projections and to investigate which biophysical processes and knowledge gaps are key factors affecting this uncertainty and should be given the highest priorities for improvement. First, eight barley models and eight climate projections for the 2050s were applied to investigate the uncertainty from crop model structure in climate impact projections for barley growth and yield at two sites: Jokioinen, Finland (Boreal) and Lleida, Spain (Mediterranean). Sensitivity analyses were then conducted on the responses of major crop processes to major climatic variables including temperature, precipitation, irradiation, and CO2, as well as their interactions, for each of the eight crop models. The results showed that the temperature and CO2 relationships in the models were the major sources of the large discrepancies among the models in climate impact projections. In particular, the impacts of increases in temperature and CO2 on leaf area development were identified as the major causes for the large uncertainty in simulating changes in evapotranspiration, above-ground biomass, and grain yield. Our findings highlight that advancements in understanding the basic processes and thresholds by which climate warming and CO2 increases will affect leaf area development, crop evapotranspiration, photosynthesis, and grain formation in contrasting environments are needed for modeling their impacts.

Individual-based models (IBMs) informing public health policy should be calibrated to data and provide estimates of uncertainty. Two main components of model-calibration methods are the parameter-search strategy and the goodness-of-fit (GOF) measure; many options exist for each of these. This review provides an overview of calibration methods used in IBMs modelling infectious disease spread. We identified articles on PubMed employing simulation-based methods to calibrate IBMs informing public health policy in HIV, tuberculosis, and malaria epidemiology published between 1 January 2013 and 31 December 2018. Articles were included if models stored individual-specific information, and calibration involved comparing model output to population-level targets. We extracted information on parameter-search strategies, GOF measures, and model validation. The PubMed search identified 653 candidate articles, of which 84 met the review criteria. Of the included articles, 40 (48%) combined a quantitative GOF measure with an algorithmic parameter-search strategy-either an optimisation algorithm (14/40) or a sampling algorithm (26/40). These 40 articles varied widely in their choices of parameter-search strategies and GOF measures. For the remaining 44 (52%) articles, the parameter-search strategy could either not be identified (32/44) or was described as an informal, non-reproducible method (12/44). Of these 44 articles, the majority (25/44) were unclear about the GOF measure used; of the rest, only five quantitatively evaluated GOF. Only a minority of the included articles, 14 (17%) provided a rationale for their choice of model-calibration method. Model validation was reported in 31 (37%) articles. Reporting on calibration methods is far from optimal in epidemiological modelling studies of HIV, malaria and TB transmission dynamics. The adoption of better documented, algorithmic calibration methods could improve both reproducibility and the quality of inference in model-based epidemiology. There is a need for research comparing the performance of calibration methods to inform decisions about the parameter-search strategies and GOF measures. Author summary Calibration-that is, "fitting" the model to data-is a crucial part of using mathematical models to better forecast and control the population-level spread of infectious diseases. Evidence that the mathematical model is well-calibrated improves confidence that the model provides a realistic picture of the consequences of health policy decisions. To make informed decisions, Policymakers need information about uncertainty: i.e., what is the range of likely outcomes (rather than just a single prediction). Thus, modellers should also strive to provide accurate measurements of uncertainty, both for their model parameters and for their predictions. This systematic review provides an overview of the methods used to calibrate individual-based models (IBMs) of the spread of HIV, malaria, and tuberculosis. We found that less than half of the reviewed articles used reproducible, non-subjective calibration methods. For the remaining articles, the method could either not be identified or was described as an informal, non-reproducible method. Only one-third of the articles obtained estimates of parameter uncertainty. We conclude that the adoption of better-documented, algorithmic calibration methods could improve both reproducibility and the quality of inference in model-based epidemiology. WD was supported by grant 12L5816N from the Research Foundation - Flanders (FWO). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Hazelbag, CM (corresponding author), Stellenbosch Univ, South African DSI NRF Ctr Excellence Epidemiol Mo, Stellenbosch, South Africa. marijnhazelbag@sun.ac.za

Individual-based models (IBMs) informing public health policy should be calibrated to data and provide estimates of uncertainty. Two main components of model-calibration methods are the parameter-search strategy and the goodness-of-fit (GOF) measure; many options exist for each of these. This review provides an overview of calibration methods used in IBMs modelling infectious disease spread. We identified articles on PubMed employing simulation-based methods to calibrate IBMs informing public health policy in HIV, tuberculosis, and malaria epidemiology published between 1 January 2013 and 31 December 2018. Articles were included if models stored individual-specific information, and calibration involved comparing model output to population-level targets. We extracted information on parameter-search strategies, GOF measures, and model validation. The PubMed search identified 653 candidate articles, of which 84 met the review criteria. Of the included articles, 40 (48%) combined a quantitative GOF measure with an algorithmic parameter-search strategy–either an optimisation algorithm (14/40) or a sampling algorithm (26/40). These 40 articles varied widely in their choices of parameter-search strategies and GOF measures. For the remaining 44 (52%) articles, the parameter-search strategy could either not be identified (32/44) or was described as an informal, non-reproducible method (12/44). Of these 44 articles, the majority (25/44) were unclear about the GOF measure used; of the rest, only five quantitatively evaluated GOF. Only a minority of the included articles, 14 (17%) provided a rationale for their choice of model-calibration method. Model validation was reported in 31 (37%) articles. Reporting on calibration methods is far from optimal in epidemiological modelling studies of HIV, malaria and TB transmission dynamics. The adoption of better documented, algorithmic calibration methods could improve both reproducibility and the quality of inference in model-based epidemiology. There is a need for research comparing the performance of calibration methods to inform decisions about the parameter-search strategies and GOF measures., Author summary Calibration—that is, “fitting” the model to data—is a crucial part of using mathematical models to better forecast and control the population-level spread of infectious diseases. Evidence that the mathematical model is well-calibrated improves confidence that the model provides a realistic picture of the consequences of health policy decisions. To make informed decisions, Policymakers need information about uncertainty: i.e., what is the range of likely outcomes (rather than just a single prediction). Thus, modellers should also strive to provide accurate measurements of uncertainty, both for their model parameters and for their predictions. This systematic review provides an overview of the methods used to calibrate individual-based models (IBMs) of the spread of HIV, malaria, and tuberculosis. We found that less than half of the reviewed articles used reproducible, non-subjective calibration methods. For the remaining articles, the method could either not be identified or was described as an informal, non-reproducible method. Only one-third of the articles obtained estimates of parameter uncertainty. We conclude that the adoption of better-documented, algorithmic calibration methods could improve both reproducibility and the quality of inference in model-based epidemiology.

OpenMOLE is a scientific workflow engine with a strong emphasis on workload distribution. Workflows are designed using a high level Domain Specific Language (DSL) built on top of Scala. It exposes natural parallelism constructs to easily delegate the workload resulting from a workflow to a wide range of distributed computing environments. OpenMOLE hides the complexity of designing complex experiments thanks to its DSL. Users can embed their own applications and scale their pipelines from a small prototype running on their desktop computer to a large-scale study harnessing distributed computing infrastructures, simply by changing a single line in the pipeline definition. The construction of the pipeline itself is decoupled from the execution context. The high-level DSL abstracts the underlying execution environment, contrary to classic shell-script based pipelines. These two aspects allow pipelines to be shared and studies to be replicated across different computing environments. Workflows can be run as traditional batch pipelines or coupled with OpenMOLE's advanced exploration methods in order to study the behaviour of an application, or perform automatic parameter tuning. In this work, we briefly present the strong assets of OpenMOLE and detail recent improvements targeting re-executability of workflows across various Linux platforms. We have tightly coupled OpenMOLE with CARE, a standalone containerisation solution that allows re-executing on a Linux host any application that has been packaged on another Linux host previously. The solution is evaluated against a Python-based pipeline involving packages such as scikit-learn as well as binary dependencies. All were packaged and re-executed successfully on various HPC environments, with identical numerical results (here prediction scores) obtained on each environment. Our results show that the pair formed by OpenMOLE and CARE is a reliable solution to generate reproducible results and re-executable pipelines. A demonstration of the flexibility of our solution showcases three neuroimaging pipelines harnessing distributed computing environments as heterogeneous as local clusters or the European Grid Infrastructure (EGI).

The present paper describes a numerical and experimental testing of a thermal energy storage system (TES) that contains phase change material (PCM). The definitions and assumptions of the developed numerical models to assess the performance and to simulate the thermal energy behavior are presented. The numerical calculation was performed using a computational fluid dynamics (CFD) analysis, and a real scale test cell with two window shutters, one with and the other without phase change materials, was used to validate the numerical model. The windows shutters were applied into two similar compartments that were tested and the thermal behavior was analyzed. The numerical models are based on 2D analysis and the indoor temperature comparison between the experimental and the simulated model are evaluated for both compartment models. To validate and evaluate the data agreement of the numerical results were used the most common statistical indexes (based on ASHRAE, IPMVP) and FEMP). According to the used criteria acceptance, the results of the numerical model presented good agreement and reliability, and the numerical model was considered as calibrated with well prediction data. Comparing the temperature improvement and the time delay between both compartments, the compartment with the PCM window shutter (i) decreased the maximum indoor temperature up to 8.7% for the warming period, (ii) increased 16.7% the minimum indoor temperature for the night period and (iii) increased the time delay 1 h for the maximum temperature peak and 30 min for the minimum temperature peak, compared with the reference compartment. (C) 2016 Elsevier Ltd. All rights reserved.

An important paradigm in decision-making models is utility-maximization where most models do not include actors' motives. Fuzzy set theory on the other hand offers a method to simulate human decision-making. However, the literature describing expert-driven fuzzy logic models, rarely gives precise details on the methodology (to be) used. To fill the gap, this paper describes a methodology of 10 steps to model individual actor's drivers, motives, hereby taking into account the ecological, social and economic context. Testing the methodology on the composition of mixed farming systems in the Mekong Delta, Vietnam, showed that manual model development is not a waterfall approach but requires feedback loops, except for model implementation. Using feed-back loops, the proposed 10 step method allowed to include human drivers and motives other than utility-maximization and to maintain a degree of transparency hard to achieve when using automated procedures.

International audience; Cropping system models are powerful tools for regional impact assessment, but their input data requirements for large heterogeneous areas are difficult to fulfil. Hence, the objectives of this paper are to present low-data approaches for specifying detailed management data required by cropping system models, and for calibrating default crop parameters applied to 12 regions in the European Union (EU). Various downscaling and upscaling procedures for different data types are applied to address both objectives. The Agricultural Production and Externalities Simulator (APES) model is used for illustrative purposes. Combining easy-to-collect regional crop management information and expert knowledge enables to develop generic, expert-based rules for specifying crop management. Effects of these expert-based management rules on simulated yields and nitrogen leaching are illustrated using APES. Simulated yields of grain maize, soft wheat and durum wheat using default crop parameters for phenology are compared with crop yields observed in 12 EU regions. The accuracy of the simulated yields was variable, but generally poor. A regional calibration factor Kpheno is developed based on the temperature sum of the average sowing and harvest dates of the three crops in each region. Applying this calibration factor improved the simulated yields in all cases. Results suggest that it is possible to develop expert-based management rules and to capture yield variation across the EU by using the presented low-data approaches.

Weather data are essential inputs for crop growth models, which are primarily developed for field level applications using site-specific daily weather data. Daily weather data are often not available, especially when models are applied to large regions and/or for future projections. It is possible to generate daily weather data from aggregated weather data, such as average monthly weather data, e.g. through a linear interpolation method. But, due to the nonlinearity of many weather–crop relationships, results of simulations using linearly interpolated data will deviate from those with actual (daily) data. The objective of this study was to analyse the sensitivity of different modelling approaches to the temporal resolution of weather input data. We used spring wheat as an example and considered three combinations of summarized and detailed approaches to model leaf area index development and associated radiation interception and biomass productivity, reflecting the typical range of detail in the structure of most models. Models were run with actual weather data and with aggregated weather data from which day-to-day variation had been removed by linear interpolation between monthly averages. Results from different climatic regions in Europe show that simulated biomass differs between model simulations using actual or aggregated temperature and/or radiation data. In addition, we find a relationship between the sensitivity of an approach to interpolation of input data and the degree of detail in that modelling approach: increasing detail results in higher sensitivity. Moreover, the magnitude of the day-to-day variability in weather conditions affects the results: increasing variability results in stronger differences between model results. Our results have implications for the choice of a specific approach to model a certain process depending on the available temporal resolution of input data.

Mathematical models for the spread of foot and mouth disease (FMD) have been developed and used for a number of purposes in the recent literature. One important purpose is predicting the effect of strategies to combat between-farm epidemic spread, in support of decision-making on epidemic control. The authors briefly review the various modelling approaches, discussing the parameters used and how estimates may be obtained for these parameters. They emphasise that, in addition to the estimation of FMD transmission parameters, the choice of model structure (including the number and type of parameters used) is also crucial. Two gaps in the knowledge of FMD transmission, related to model construction and parameter quantification, are identified: transmission between different species and the way in which vaccination affects such transmission, and route-specific FMD transmission properties. In particular, the authors pay attention to the role that small-scale transmission experiments can play in bridging these gaps.

This study presents a fleet-based bioeconomic simulation model to the international mixed flatfish fishery in the North Sea. The model uses a Management Strategies Evaluation framework including a discrete choice model accounting for short-term temporal changes in effort allocation across fisheries. A simplified random utility model was used based on the expected revenue (or economic attractiveness) and two tradition parameters related to short and long term historical fishing patterns. All three parameters were significant. Even though reactions and adaptations vary between fleets, the estimated conservative behaviour of the main fleets led to only marginal effect at the stock level. The importance of accounting for fleet behaviour was then evaluated using an elasticity analysis to explore how increased weight of economic attractiveness contributes to changes in the biological output and positive increase in the economic performance of the individual fleets. This showed the existence of a window of sensitivity of the model to the behaviour assumptions. The study highlights the challenge of implementing an effort allocation model in a general framework of Management Strategies Evaluation for mixed-fisheries, and illustrates the necessary trade-offs between very detailed numerical relationships and the representation of aggregated processes.

During participatory prototyping activities in Vihiga, western Kenya, farmers designed what they considered to be the ideal farm [Waithaka, M.M., Thornton, P.K., Herrero, M., Shepherd, K.D., 2006. Bio-economic evaluation of farmers’ perceptions of viable farms in western Kenya. Agric. Syst. 90, 243–271]: one in which high productivity is achieved through optimising crop-livestock interactions. We selected four case study crop-livestock farms of different resource endowment (Type 1–4 – excluding the poorest farmers, Type 5, who do not own livestock) and quantified all relevant physical flows through and within them. With this information we parameterised a dynamic, farm-scale simulation model to investigate (i) current differences in resource use efficiencies and degree of crop-livestock interactions across farm types; and (ii) the impact of different interventions in farm Types 3 and 4 on producing the desired shifts in productivity towards the ideal farm. Assuming no resource constraints, changes in the current farm systems were introduced stepwise, as both intensification of external input use (fertilisers and fodder) and qualitative changes in the configuration of the farms (i.e. changing land use towards fodder production, improving manure handling and/or changing cattle breeds). In 10-year simulations of the baseline, current scenario using historical weather data the wealthiest farms Type 2 achieved food self-sufficiency (FSS) in 20% of the seasons due to rainfall variability, whereas the poorer Type 4 only achieved FSS in 0 to 30% of the seasons; soil organic C decreased during the simulations at annual rates of −0.54, −0.73, −0.85 and −0.84 t C ha−1 on farms of Type 1–4, respectively; large differences in productivity and recycling efficiency between farm types indicated that there is ample room to improve the physical performance of the poorer farms (e.g. light and water use efficiency was 2–3 times larger on wealthier farms). Simulating different intensification scenarios indicated that household FSS can be achieved in all farm types through input intensification, e.g. using P fertilisers at rates as small as 15 kg farm−1 season−1 (i.e. from 7 to 28 kg ha−1). Increasing the area under Napier grass from c. 20 to 40% and reducing the area of maize, beans and sweet potato in farms of Type 3 and 4 increased their primary productivity by c. 1 t ha−1 season−1, their milk production by 156 and 45 L season−1, respectively, but decreased the production of edible energy (by 2000 and 250 MJ ha−1 season−1) and protein (by 20 and 3 kg ha−1 season−1). By bringing in a more productive cow the primary productivity increased even further in Farm Type 3 (up to 5 t ha−1 season−1), as did milk production (up to c. 1000 L season−1), edible energy (up to c. 10,000 MJ ha−1 season−1) and protein (up to c. 100 kg ha−1 season−1). The impact of livestock management on the recycling of nutrients and on the efficiency of nutrient use at farm scale can be large, provided that enough nutrients are present in or enter the system to be redistributed. An increase in N cycling efficiency through improved manure handling from 25 to 50% would increase the amount of N cycled in the case study farms of Type 1 and 2 by only ca. 10 kg season−1, and only 1–2 kg season−1 in Type 3 and 4. The various alternatives simulated when disregarding resource constraints contributed to narrow the productivity and efficiency gaps between poorer and wealthier farms. However, the feasibility of implementing such interventions on a large number of farms is questionable. Implications for system (re-)design and intensification strategies are discussed.

International audience; Control systems design may be a difficult task when the system to be controlled is complex, poorly understood, and with limited observability. This is often the case of biological or physiological systems. In this paper, we present a model-based control design framework, which is adapted to the design of medical devices. This framework couples a control module, based on a classical PID controller, a model of the medical device, and a physiological model representing the cardiovascular responses to vagus nerve stimulation (VNS). An example is proposed in which the goal of the controller is to regulate instantaneous heart rate in real-time, by modulating the current delivered to the vagus nerve by the neuromodulator in an adaptive manner. Results of the definition of the control system with different controller parameters and for different model configurations are provided, showing the feasibility and usefulness of a model-based design in this context.

The environment in which crops will be grown in the future will change. CO 2 concentrations [CO 2 ] and temperatures ( T ) will probably increase and a decline of winter rainfall is predicted for south-west Australia. To be able to adapt crop systems to a changing climate it is important to know how different aspects of climate change affect agricultural production and how they interact. In a full factorial design we studied how higher T (2, 4 and 6 °C) elevated [CO 2 ] (525 and 700 ppm) and five different rainfall scenarios affected wheat yield and grain protein. Effects of climate change were simulated with the Agricultural Production Systems Simulator (APSIM-Nwheat) using transformed historic weather data. Fifty years of yield and grain protein concentrations were simulated for three soil types at different locations on a north–south transect within the wheatbelt of south-west Australia. Simulation results showed that there were complex interactions between different aspects of climate change on crop systems. Effects of higher temperatures, elevated [CO 2 ] and changed rainfall were in general not linear and differed significantly between soil types and location. Higher [CO 2 ] increased yield especially at drier sites while higher temperatures had a positive effect in the cooler and wetter southern part of the region. The main difference between soil types was that heavier clay soils are most vulnerable to reduced rainfall while sandy soils were more vulnerable to higher temperatures. Elevated [CO 2 ] reduced grain protein concentration and lower rainfall increased protein levels at all sites. Higher temperatures could both increase and decrease protein concentrations. In the southern, higher rainfall part of south-western Australia, yield and gross margin will increase for all likely future climate scenarios. In the drier part of the region, negative effects of 15% reduced rainfall can be compensated for by a 2 °C increase in temperature and 50% higher [CO 2 ] concentrations. However due to the non-linearity of climate change effects a 30% reduction in rainfall cannot be compensated for by higher temperatures and [CO 2 ].

Since the pioneering work of C.T. de Wit in the 1960s, the Wageningen group has built a tradition in developing and applying crop models. Rather than focusing on a few models, diversity is its trademark. Here we present an overview of the Wageningen crop and crop-soil modelling approaches along three criteria. The first criterion relates to the production situations the models are dealing with (i.e. potential, water and/or nutrient-limited, and actual production situations including pests, diseases and weeds). Second, models differ as a result of the objectives of model development, and hence required scale and degree of detail and comprehensiveness. Third, models have at least three potential application domains, i.e. research, education and support of learning and decision making processes. We describe both summary and more comprehensive modelling approaches for the major production situations. An overview of most of the Wageningen models is presented together with a more detailed description of LINTUL, SUCROS, ORYZA, WOFOST and INTERCOM. Illustrations for each of the three application domains are presented, i.e. plant type design, guiding experimental research, education, yield gap analysis, evaluation of manure policies, crop growth monitoring system and analysis and design of farming and regional land use systems. We discuss common issues of model verification, model validation, model validity and data requirements, and present information on software implementation, model and software documentation and distribution policy. Finally, we reflect upon the Wageningen modelling approaches and identify a number of key issues for future research. Major achievements of Wageningen modelling efforts include (1) a broad variety of approaches for modelling of systems at different scales and with different purposes; (2) their contribution to quantitative systems thinking in general, also for applications at higher hierarchical levels; (3) a strong linkage between crop modelling and higher education, both at undergraduate, graduate and post-doctoral level. To continually increase our understanding of crops and production systems a diversified approach must be cherished. At the same time we conclude that focus is required on a limited number of modules in a more integrated modelling framework for the benefit of analysing, evaluating and designing cropping systems. This review may be instrumental in the development of such an integrated framework.

* Background and Aims: Genetic markers can be used in combination with ecophysiological crop models to predict the performance of genotypes. Crop models can estimate the contribution of individual markers to crop performance in given environments. The objectives of this study were to explore the use of crop models to design markers and virtual ideotypes for improving yields of rice (Oryza sativa) under drought stress. * Methods: Using the model GECROS, crop yield was dissected into seven easily measured parameters. Loci for these parameters were identified for a rice population of 94 introgression lines (ILs) derived from two parents differing in drought tolerance. Marker-based values of ILs for each of these parameterswere estimated fromadditive allele effects of the loci, and were fed to the model in order to simulate yields of the ILs grown under well-watered and drought conditions and in order to design virtual ideotypes for those conditions. * Key Results: To account for genotypic yield differences, itwas necessary to parameterize the model for differences in an additional trait ‘total crop nitrogen uptake’ (Nmax) among the ILs. Genetic variation in Nmax had the most significant effect on yield; five other parameters also significantly influenced yield, but seed weight and leaf photosynthesis did not. Using the marker-based parameter values, GECROS also simulated yield variation among 251 recombinant inbred lines of the same parents. The model-based dissection approach detected more markers than the analysis using only yield per se. Model-based sensitivity analysis ranked all markers for their importance in determining yield differences among the ILs. Virtual ideotypes based on markers identified by modelling had 10–36% more yield than those based on markers for yield per se. * Conclusions: This study outlines a genotype-to-phenotype approach that exploits the potential value of markerbased crop modelling in developing new plant types with high yields. The approach can provide more markers for selection programmes for specific environments whilst also allowing for prioritization. Crop modelling is thus a powerful tool for marker design for improved rice yields and for ideotyping under contrasting conditions.

[EN] The agricultural sector faces the challenge of ensuring food security without an excessive burden on the environment. Simulationmodels provide excellent instruments for researchers to gainmore insight into relevant processes and best agricultural practices and provide tools for planners for decision making support. The extent to which models are capable of reliable extrapolation and prediction is important for exploring new farming systems or assessing the impacts of future land and climate changes. A performance assessmentwas conducted by testing six detailed state-of-the-artmodels for simulation of nitrate leaching (ARMOSA, COUPMODEL, DAISY, EPIC, SIMWASER/STOTRASIM, SWAP/ANIMO) for lysimeter data of the Wagna experimental field station in Eastern Austria, where the soil is highly vulnerable to nitrate leaching. Three consecutive phases were distinguished to gain insight in the predictive power of themodels: 1) a blind test for 2005 2008 in which only soil hydraulic characteristics, meteorological data and information about the agricultural management were accessible; 2) a calibration for the same period in which essential information on field observations was additionally available to the modellers; and 3) a validation for 2009 2011 with the corresponding type of data available as for the blind test. A set of statistical metrics (mean absolute error, root mean squared error, index of agreement,model efficiency, root relative squared error, Pearson's linear correlation coefficient) was applied for testing the results and comparing the models. None of the models performed good for all of the statistical metrics. Models designed for nitrate leaching in high-input farming systems had difficulties in accurately predicting leaching in low-input farming systems that are strongly influenced by the retention of nitrogen in catch crops and nitrogen fixation by legumes. An accurate calibration does not guarantee a good predictive power of the model. Nevertheless all models were able to identify years and crops with high- and low-leaching rates., This research was made possible by the GENESIS project of the EU 7th Framework Programme (Project No. 226536; FP7-ENV-2008-1). We are grateful for the experimental data provided by Joanneum Raum (Graz, Austria). The modelling team of Democritus University of Thrace would like to thank Per-Erik Jansson (Royal Institute of Technology, Stockholm, Sweden) for his valuable help during the application of Coup Model.

Modeling of integrated urban water systems (IUWS) has seen a rapid development in recent years. Models and software are available that describe the process dynamics in sewers, wastewater treatment plants (WWTPs), receiving water systems as well as at the interfaces between the submodels. Successful applications of integrated modeling are, however, relatively scarce. One of the reasons for this is the lack of high-quality monitoring data with the required spatial and temporal resolution and accuracy to calibrate and validate the integrated models, even though the state of the art of monitoring itself is no longer the limiting factor. This paper discusses the efforts to be able to meet the data requirements associated with integrated modeling and describes the methods applied to validate the monitoring data and to use submodels as software sensor to provide the necessary input for other submodels. The main conclusion of the paper is that state of the art monitoring is in principle sufficient to provide the data necessary to calibrate integrated models, but practical limitations resulting in incomplete data-sets hamper widespread application. In order to overcome these difficulties, redundancy of future monitoring networks should be increased and, at the same time, data handling (including data validation, mining and assimilation) should receive much more attention.

Compositional data, such as soil texture, are hard to deal with in the geosciences as standard statistical methods are often inappropriate to analyse this type of data. Especially in sensitivity analysis, the closed character of the data is often ignored. To that end, we developed a method to assess the local sensitivity of a model output with resect to a compositional model input. We adapted the finite difference technique such that the different parts of the input are perturbed simultaneously while the closed character of the data is preserved. This method was applied to a hydrologic model and the sensitivity of the simulated soil moisture content to local changes in soil texture was assessed. Based on a high number of model runs, in which the soil texture was varied across the entire texture triangle, we identified zones of high sensitivity in the texture triangle. In such zones, the model output uncertainty induced by the discrepancy between the scale of measurement and the scale of model application, is advised to be reduced through additional data collection. Furthermore, the sensitivity analysis provided more insight into the hydrologic model behaviour as it revealed how the model sensitivity is related to the shape of the soil moistureretention curve.

Mathematical models for the spread of foot and mouth disease (FMD) have been developed and used for a number of purposes in the recent literature. One important purpose is predicting the effect of strategies to combat between-farm epidemic spread, in support of decision-making on epidemic control. The authors briefly review the various modelling approaches, discussing the parameters used and how estimates may be obtained for these parameters. They emphasise that, in addition to the estimation of FMD transmission parameters, the choice of model structure (including the number and type of parameters used) is also crucial. Two gaps in the knowledge of FMD transmission, related to model construction and parameter quantification, are identified: transmission between different species and the way in which vaccination affects such transmission, and route-specific FMD transmission properties. In particular, the authors pay attention to the role that small-scale transmission experiments can play in bridging these gaps.

Impacts of climate variability and climate change on regional crop yields are commonly assessed using process-based crop models. These models, however, simulate potential and water limited yields, which do not always relate to observed yields. The latter are largely influenced by crop management, which varies by farm and region. Data on specific management strategies may be obtained at the field level, but at the regional level information about the diversity in management strategies is rarely available and difficult to be considered adequately in process-based crop models. Alternatively, understanding the factors influencing management may provide helpful information to improve simulations at the regional level. In this study, we aim to identify factors at the regional level that explain differences between observed and simulated yields. Observed yield data were provided by the Farm Accountancy Data Network (FADN) and Eurostat. The Crop Growth Monitoring System (CGMS), based on the WOFOST model, was used to simulate potential and water limited maize yields in the EU15 (i.e., the old member states of the European Union). Differences between observed and simulated maize yields were analysed using regression models including: (i) climatic factors (temperature and precipitation), (ii) farm size, (iii) farm intensity, (iv) land use, (v) income and (vi) subsidies. We assumed that the highest yields observed in a region were close to the yield potential as determined by climate and considered the average regional yields as also influenced by management. Model performance was analysed with respect to spatial and temporal yield variability. Results indicate that for potential yield, the model performed unsatisfactory in southern regions, where high temperatures increased observed yields which was in contrast to model simulations. When considering management effects, we find that especially irrigation and the maize area explain much of the differences between observed and simulated yields across regions. Simulations of temporal yield variability also diverted from observed data of which about 80% could be explained by the climatic factors (35%) and farm characteristics (50%) considered in the analysis. However, effects of specific factors differed depending on the regions. Accordingly, we propose different groups of regions with factors related to management which should be considered to improve regional yield simulations with CGMS.

The use of a hierarchy in growth factors (defining, limiting and reducing growth factors), as developed for plant production has shown its usefulness in the analysis and design of plant production systems. This hierarchy presents a theoretical framework for the analysis of biophysical conditions in plant production. We hypothesize that analysis and design of agricultural land use systems is facilitated by development of a similar set of production ecological concepts for animal production, as livestock is often part of such systems. In this paper we present such a hierarchy. We identify growth defining (temperature, daylength, animal genetic characteristics), limiting (water and feed quantity and quality) and reducing (diseases, pollutants and other conditions leading to sub-optimal wellbeing) factors, determining the production of an individual animal, in parallel to their definition for crop production, and aggregate this production to herd scale. We discuss how management intervenes with these factors. Application of the production ecological concepts in design of land use systems ensures that all production systems are based on the prevailing biophysical characteristics and that intrinsic system properties are taken into account. This approach also provides a valuable framework for yield-gap analysis, explaining current production levels, and identifying constraining factors, for setting the research agenda by identifying knowledge gaps and for educational purposes. We illustrate application of the concepts in the exploration and design of alternative animal and mixed plant-animal production systems with two examples. The first example relates to potential production in intensive dairy farming in a temperate climate and the second to feed-limited cattle production in the tropics. (C) 2003 Elsevier Science Ltd. All rights reserved.

Experiments and models are described that quantify the functional responses of the larval and adult stages of the sevenspot ladybeetle, Coccinella septempunctata L. toward mixed stage populations of the cotton aphid, Aphis gossypii Glover, in cotton. In the laboratory, functional responses were measured for five beetle stages and three size groups of prey at five temperatures (15 to 35degreesC). The 75 resulting functional responses were each characterized by a search rate (cm(2)/ predator/d) and a handling rate (prey/ predator/ d). Both search and handling rates increased with predator stage. Search rates increased, while handling rates decreased, with prey size. Search rate increased linearly with temperature, while handling rate showed an optimum response to temperature. Field cage studies were conducted to verify whether the search rates determined in the laboratory were valid under field conditions. Search rates of predators derived from observations in field cages yielded parameter estimates that were similar to those found in the laboratory. A comprehensive model, allowing for effects of temperature, stage distribution of the aphid population, and plant leaf area on predation rate, was then constructed to calculate predation by larvae and adults of C. septempunctata on multi stage populations of prey. This model gave good correspondence to the cage observations if two-sided leaf area was input into the model as search substrate for the predator. The model appears suitable for calculating predation rates of C. septempunctata on A. gossypii under field conditions. A sensitivity analysis of the functional response model shows the crucial effect of crop leaf area on predation.

Besides foundry facilities, CAD-tools are also required to move microsystems from research prototypes to an industrial market. CAD tools of microelectronics have been developed for more than 20 years, both in the field of circuit design tools and in the area of TCAD tools. Usually a microelectronics engineer is involved only in one side of the design: either he deals with application design or he is participating in the manufacturing design, but not in both. This is one point that is to be followed in case of microsystem design, if higher level of design productivity is expected. Another point is that certain standards should also be established in case of microsystem design too: based on selected technologies a set of standard components should be pre-designed and collected in a standard component library. This component library should be available from within microsystem design frameworks which might be well established by a proper configuration and extension of existing IC design frameworks. A very important point is the development, of proper simulation models of microsystem components that are based on e.g. the FEM results of the pre-design phase and are provided in the form of an analog VHDL script.