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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.
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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.
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Dumont, B., Basso, B., Leemans, V., Bodson, B., Destain, J. - P., & Destain, M. - F. (2015). Systematic analysis of site-specific yield distributions resulting from nitrogen management and climatic variability interactions. Precision Agric., 16(4), 361–384.
Abstract: At the plot level, crop simulation models such as STICS have the potential to evaluate risk associated with management practices. In nitrogen (N) management, however, the decision-making process is complex because the decision has to be taken without any knowledge of future weather conditions. The objective of this paper is to present a general methodology for assessing yield variability linked to climatic uncertainty and variable N rate strategies. The STICS model was coupled with the LARS-Weather Generator. The Pearson system and coefficients were used to characterise the shape of yield distribution. Alternatives to classical statistical tests were proposed for assessing the normality of distributions and conducting comparisons (namely, the Jarque-Bera and Wilcoxon tests, respectively). Finally, the focus was put on the probability risk assessment, which remains a key point within the decision process. The simulation results showed that, based on current N application practice among Belgian farmers (60-60-60 kgN ha(-1)), yield distribution was very highly significantly non-normal, with the highest degree of asymmetry characterised by a skewness value of -1.02. They showed that this strategy gave the greatest probability (60 %) of achieving yields that were superior to the mean (10.5 t ha(-1)) of the distribution.
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Dumont, B., Leemans, V., Mansouri, M., Bodson, B., Destain, J. - P., & Destain, M. - F. (2014). Parameter identification of the STICS crop model, using an accelerated formal MCMC approach. Env. Model. Softw., 52, 121–135.
Abstract: This study presents a Bayesian approach for the parameters’ identification of the STICS crop model based on the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm. The posterior distributions of nine specific crop parameters of the STICS model were sampled with the aim to improve the growth simulations of a winter wheat (Triticum aestivum L) culture. The results obtained with the DREAM algorithm were initially compared to those obtained with a Nelder-Mead Simplex algorithm embedded within the OptimiSTICS package. Then, three types of likelihood functions implemented within the DREAM algorithm were compared, namely the standard least square, the weighted least square, and a transformed likelihood function that makes explicit use of the coefficient of variation (CV). The results showed that the proposed CV likelihood function allowed taking into account both noise on measurements and heteroscedasticity which are regularly encountered in crop modelling. (C) 2013 Elsevier Ltd. All rights reserved.
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Sieber, S., Amjath-Babu, T. S., Jansson, T., Müller, K., Tscherning, K., Graef, F., et al. (2013). Sustainability impact assessment using integrated meta-modelling: Simulating the reduction of direct support under the EU common agricultural policy (CAP). Land Use Policy, 33, 235–245.
Abstract: Assessing the impact of macro-level policy driven land use changes on regional sustainability is an important task that can facilitate complex decision making processes of introducing reforms. The research work demonstrates the ability of Sustainability Impact Assessment Tool (SIAT), a meta-model, in conducting ex ante spatially explicit cross sectoral impact assessments of changes in common agricultural policy (CAP). The meta-model is able to appraise impacts of CAP amendments on land use and their repercussions on multiple indicators of sustainability. The presented study comprehensively analyses the possible impacts of discontinuing direct financial support to farmers under CAP. The simulations of the meta-model are able to reveal the land use changes both at EU and regional levels as well as to bring forth the subsequent changes in a number of indicators representing the regional sustainability (for five case study regions). In a nutshell, the simulations indicate that a reduction in direct support brings in general, a decrease in farmed area, an increase in forested land, less fluctuation in natural vegetation coverage, increase in abandoned arable land area and negligible changes in built-up area despite regionally diverging land use trends. The simulated changes in sustainability indicators for the study regions in consequence to these land use changes show that the discontinuation of subsidies evokes responses that are in general climate friendly (reduction in methane and N2O emissions, diminishing energy use and reduction in global warming potential), economically beneficial (increase in gross value of agriculture) and socially desired (decrease in unemployment rate) as well as environmentally harmful (increase in pesticide use). Even though the appraisals of diversity indicators such as forest deadwood and farmland birds are not conclusive for all regions, the changes are positive for the former indicator and slightly negative for the latter in general. The trade-offs among these regional sustainability indicators using their directional associations are also presented for a comprehensive assessment of the impacts. (C) 2013 Elsevier Ltd. All rights reserved.
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