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Olesen, J. E., Sharif, B., Plauborg, F., Yin, X., Bindi, M., Doro, L., et al. (2016). Comparison of wheat models and their sensitivity towards tillage and N fertilization with different calibration approaches.. Berlin (Germany).
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Ewert, F., Rötter, R. P., Bindi, M., Webber, H., Trnka, M., Kersebaum, K., et al. (2015). Crop modelling for integrated assessment of risk to food production from climate change (Vol. 6).
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. No Label
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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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Kollas, C., Kersebaum, K. C., Nendel, C., Manevski, K., Müller, C., Palosuo, T., et al. (2015). Crop rotation modelling—A European model intercomparison. European Journal of Agronomy, 70, 98–111.
Abstract: • First model inter-comparison on crop rotations. • Continuous simulation of multi-year crop rotations yields outperformed single-year simulation. • Low accuracy of yield predictions in less commonly modelled crops such as potato, radish, grass vegetation. • Multi-model mean prediction was found to minimise the likely error arising from single-model predictions. • The representation of intermediate crops and carry-over effects in the models require further research efforts.
Diversification of crop rotations is considered an option to increase the resilience of European crop production under climate change. So far, however, many crop simulation studies have focused on predicting single crops in separate one-year simulations. Here, we compared the capability of fifteen crop growth simulation models to predict yields in crop rotations at five sites across Europe under minimal calibration. Crop rotations encompassed 301 seasons of ten crop types common to European agriculture and a diverse set of treatments (irrigation, fertilisation, CO2 concentration, soil types, tillage, residues, intermediate or catch crops). We found that the continuous simulation of multi-year crop rotations yielded results of slightly higher quality compared to the simulation of single years and single crops. Intermediate crops (oilseed radish and grass vegetation) were simulated less accurately than main crops (cereals). The majority of models performed better for the treatments of increased CO2 and nitrogen fertilisation than for irrigation and soil-related treatments. The yield simulation of the multi-model ensemble reduced the error compared to single-model simulations. The low degree of superiority of continuous simulations over single year simulation was caused by (a) insufficiently parameterised crops, which affect the performance of the following crop, and (b) the lack of growth-limiting water and/or nitrogen in the crop rotations under investigation. In order to achieve a sound representation of crop rotations, further research is required to synthesise existing knowledge of the physiology of intermediate crops and of carry-over effects from the preceding to the following crop, and to implement/improve the modelling of processes that condition these effects.
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Nguyen, T. P. L., Seddaiu, G., & Roggero, P. P. (2019). Declarative or procedural knowledge? Knowledge for enhancing farmers’ mitigation and adaptation behaviour to climate change. Journal of Rural Studies, 67, 46–56.
Abstract: Climate change poses a major challenge for farmers, but agricultural sustainability, mitigation, and adaptation can effectively decrease climate impacts on agricultural systems. Changes in farming practices are necessary to reduce emissions and to adapt to climate change. However, such modifications to common practices depend, to a large extent, on farmers’ knowledge and attitudes towards climate risks. An empirical study of farmers’ attitudes and knowledge of climate change mitigation and adaptation practices is useful to understand how farmers’ knowledge influences their attitudes and practices towards climate change mitigation and adaptation. Based on a case study characterised by four agricultural farming systems (extensive dairy sheep, intensive dairy cattle, horticultural farming, and rice farming) in the Province of Oristano in Italy, this study contains an investigation of (i) farmers’ knowledge of climate change causes and effects, how they construct such knowledge, and how they adapt to the phenomenon; (ii) what and how are farmers’ attitudes towards climate change causes are shaped under their contextual social interests and values; and (iii) if their practices in responding to climate variability are influenced by their constructed knowledge. The research results showed that farmers’ declarative knowledge of climate change did not affect their adaptation practices but directed farmers’ attitudes towards climate change causes. The findings also underscore the necessity of facilitating social learning spaces for enhancing virtuous behaviours towards climate change mitigation and the sharing and co-production of procedural knowledge for developing shared sustainable climate adaptation practices at the farm level.
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