Rötter, R. P. (2014). Cross-cutting uncertainties in MACSUR impact projections. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Projections into the future, such as climate change impact projections on crop production for a given region, or, on global food prices and trade are inherently uncertain. Uncertainty does not fall within a single discipline but is dealt with by a wide variety of disciplines, themes and problem domains. Model uncertainty pertaining to the impact modelling chain from climate via crop and livestock to economic and trade modelling is only part of the overall uncertainty*. There is also scenario uncertainty and many other known and unknown “unknowns”1 to be considered in efforts such as MACSUR and its themes (CropM, LiveM, TradeM) to advance model-based integrated assessment of climate change risk assessment for agriculture and food security. Propagation of uncertainties along the climate change impact modelling chain has been portrayed as “uncertainty cascade” 2. We will present different basic approaches for evaluating uncertainty in models. So far, studies addressing quantification and reporting of uncertainties in impact projections still largely focus on two major sources, i.e. the shares originating from climate modelling and from crop modelling. However, a more comprehensive treatment of uncertainty and how it is reported is urgently needed.
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Rötter, R. (2013). Improving capacity of current crop models for simulating impacts of climatic extremes..
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Rötter, R. (2015). Challenges for CropM in integrated (regional) assessment of climate change risks to food production (Vol. 4).
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Rötter, R. (2015). Crop yield variance and yield gap analysis for evaluating technological innovations under climate change: the case of Finnish barley (Vol. 5).
Abstract: The quest for sustainable intensification of agricultural systems has recently triggered research on determining and closing the gaps between farmers’ actual and potential crop yields that can be obtained under optimal management. This so-called “yield gap” is then taken as a yardstick for indicating the potential of technological innovations in agricultural production. In this paper, we argue that in order to assess risks and opportunities for technological innovations we need extra information on crop yield variances in different production situations.Starting point is to assess farmers’ actual yields using data in sufficient quality and resolutions. Crop simulation models are then applied to quantify crop yield potentials and their variances in a changing environment. Resultant information allows ex ante evaluation of innovations that aim at increasing and stabilizing yields.Here we present this approach for barley cultivation in Finland for observed (1981-2010) and future climate (projected for three time periods centered around 2025, 2055 and 2085). Mean and median levels, variances and probabilities of simulated potential and water-limited and observed farmers’ yields are generated for two contrasting regions for analysing production risks and assessing the effectiveness of alternative technologies. As farmers show different levels of risk-aversion, which influence their investments in technological innovations, a so-called ‘normal management mode’ is defined. Employing this then shows how future yields and yield variances are likely to develop under normal management. On this basis, we finally identify which future innovations have the potential to maintain or increase barley yields at acceptable risk levels. No Label
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Rötter, P., Palosuo, T., Semenov, M., Ruiz-Ramos, M., Tao, F., Fronzek, S., et al. (2014). Designing new cereal cultivars as an adaptation measure using crop model ensembles..
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