|
|
Ben Touhami, H., & Bellocchi, G. (2015). Bayesian calibration of the Pasture Simulation model (PaSim) to simulate European grasslands under water stress. Ecological Informatics, 30, 356–364.
Abstract: As modeling becomes a more widespread practice in the agro-environmental sciences, scientists need reliable tools to calibrate models against ever more complex and detailed data. We present a generic Bayesian computation framework for grassland simulation, which enables parameter estimation in the Bayesian formalism by using Monte Carlo approaches. We outline the underlying rationale, discuss the computational issues, and provide results from an application of the Pasture Simulation model (PaSim) to three European grasslands. The framework was suited to investigate the challenging problem of calibrating complex biophysical models to data from altered scenarios generated by precipitation reduction (water stress conditions). It was used to infer the parameters of manipulated grassland systems and to assess the gain in uncertainty reduction by updating parameter distributions using measurements of the output variables.
|
|
|
|
Podhora, A., Helming, K., Adenäuer, L., Heckelei, T., Kautto, P., Reidsma, P., et al. (2013). The policy-relevancy of impact assessment tools: Evaluating nine years of European research funding. Environmental Science & Policy, 31, 85–95.
Abstract: Since 2002, the European Commission has employed the instrument of ex-ante impact assessments (IA) to help focus its policy-making process on implementing sustainable development. Scientific tools should play an essential role of providing the evidence base to assess the impacts of alternative policy options. To identify the contribution of research funding for IA tool development, this paper analysed the variety of IA tools designed in projects funded by European Framework Programmes (FPs) 6 and 7. The paper is based on project information available on the European Cordis website, individual project websites and a verification of the results by the project coordinators. We analysed the projects from the interests of IA practitioners as tool users (European policy and impact areas addressed by the tools, jurisdictional application levels and tool categories). Out of the 7.781 projects funded in FP6 and FP7, 203 could be identified that designed tools for the IA process. Nearly half of them applied to environmental, agricultural and transport policy areas. Within these areas, the tools primarily addressed environmental impact areas, less economic and least social impact areas. The IA tools focused on European policies. Models represented the largest tool category, whereas approximately half of the tools could not be clearly categorized. Concerning our analysis criteria, the tool descriptions available on the internet were often unclear and thus may limit the application potential of the tools because of a mismatch of technical terms and categorisation criteria between tool providers and tool users. Future IA tools require a joint political and scientific typology and a narrowing of the gaps, e.g., with view to multi-jurisdictional application and a clear reference to the steps of the IA process. (C) 2013 Elsevier Ltd. All rights reserved.
|
|
|
|
Ewert, F., Rötter, R. P., Bindi, M., Webber, H., Trnka, M., Kersebaum, K. C., et al. (2015). Crop modelling for integrated assessment of risk to food production from climate change. Env. Model. Softw., 72, 287–303.
Abstract: The complexity of risks posed by climate change and possible adaptations for crop production has called for integrated assessment and modelling (IAM) approaches linking biophysical and economic models. This paper attempts to provide an overview of the present state of crop modelling to assess climate change risks to food production and to which extent crop models comply with IAM demands. Considerable progress has been made in modelling effects of climate variables, where crop models best satisfy IAM demands. Demands are partly satisfied for simulating commonly required assessment variables. However, progress on the number of simulated crops, uncertainty propagation related to model parameters and structure, adaptations and scaling are less advanced and lagging behind IAM demands. The limitations are considered substantial and apply to a different extent to all crop models. Overcoming these limitations will require joint efforts, and consideration of novel modelling approaches.
|
|
|
|
Angulo, C., Gaiser, T., Rötter, R. P., Børgesen, C. D., Hlavinka, P., Trnka, M., et al. (2014). ‘Fingerprints’ of four crop models as affected by soil input data aggregation. European Journal of Agronomy, 61, 35–48.
Abstract: • Systematic analysis of the influence of spatial soil data resolution on simulated regional yields and total growing season evapotranspiration. • The responses of four crop models of different complexity are compared. • Differences between models are larger than the effect of the chosen spatial soil data resolution. • Low influence of soil data resolution due to: high precipitation amount, methods for calculating water retention and method of data aggregation. The spatial variability of soil properties is an important driver of yield variability at both field and regional scale. Thus, when using crop growth simulation models, the choice of spatial resolution of soil input data might be key in order to accurately reproduce observed yield variability. In this study we used four crop models (SIMPLACE<LINTUL-SLIM>, DSSAT-CSM, EPIC and DAISY) differing in the detail of modeling above-ground biomass and yield as well as of modeling soil water dynamics, water uptake and drought effects on plants to simulate winter wheat in two (agro-climatologically and geo-morphologically) contrasting regions of the federal state of North-Rhine-Westphalia (Germany) for the period from 1995 to 2008. Three spatial resolutions of soil input data were taken into consideration, corresponding to the following map scales: 1:50 000, 1:300 000 and 1:1 000 000. The four crop models were run for water-limited production conditions and model results were evaluated in the form of frequency distributions, depicted by bean-plots. In both regions, soil data aggregation had very small influence on the shape and range of frequency distributions of simulated yield and simulated total growing season evapotranspiration for all models. Further analysis revealed that the small influence of spatial resolution of soil input data might be related to: (a) the high precipitation amount in the region which partly masked differences in soil characteristics for water holding capacity, (b) the loss of variability in hydraulic soil properties due to the methods applied to calculate water retention properties of the used soil profiles, and (c) the method of soil data aggregation. No characteristic “fingerprint” between sites, years and resolutions could be found for any of the models. Our results support earlier recommendation to evaluate model results on the basis of frequency distributions since these offer quick and better insight into the distribution of simulation results as compared to summary statistics only. Finally, our results support conclusions from other studies about the usefulness of considering a multi-model approach to quantify the uncertainty in simulated yields introduced by the crop growth simulation approach when exploring the effects of scaling for regional yield impact assessments.
|
|
|
|
Kanellopoulos, A., Reidsma, P., Wolf, J., & van Ittersum, M. K. (2014). Assessing climate change and associated socio-economic scenarios for arable farming in the Netherlands: An application of benchmarking and bio-economic farm modelling. European Journal of Agronomy, 52, 69–80.
Abstract: Future farming systems are challenged to adapt to the changing socio-economic and bio-physical environment in order to remain competitive and to meet the increasing requirements for food and fibres. The scientific challenge is to evaluate the consequences of predefined scenarios, identify current “best” practices and explore future adaptation strategies at farm level. The objective of this article is to assess the impact of different climate change and socio-economic scenarios on arable farming systems in Flevoland (the Netherlands) and to explore possible adaptation strategies. Data Envelopment Analysis was used to identify these current “best” practices while bio-economic modelling was used to calculate a number of important economic and environmental indicators in scenarios for 2050. Relative differences between yields with and without climate change and technological change were simulated with a crop bio-physical model and used as a correction factors for the observed crop yields of current “best” practices. We demonstrated the capacity of the proposed methodology to explore multiple scenarios by analysing the importance of drivers of change, while accounting for variation between individual farms. It was found that farmers in Flevoland are in general technically efficient and a substantial share of the arable land is currently under profit maximization. We found that climate change increased productivity in all tested scenarios. However, the effects of different socio-economic scenarios (globalized and regionalized economies) on the economic and environmental performance of the farms were variable. Scenarios of a globalized economy where the prices of outputs were simulated to increase substantially might result in increased average gross margin and lower average (per ha) applications of crop protection and fertilizers. However, the effects might differ between different farm types. It was found that, the abolishment of sugar beet quota and changes of future prices of agricultural inputs and outputs in such socio-economic scenario (i.e. globalized economy) caused a decrease in gross margins of smaller (in terms of economic size) farms, while gross margin of larger farms increased. In scenarios where more regionalized economies and a moderate climate change are assumed, the future price ratios between inputs and outputs are shown to be the key factors for the viability of arable farms in our simulations. (C) 2013 Elsevier B.V. All rights reserved.
|
|
