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Ruiz-Ramos, M. (2015). Simulating wheat adaptation to climate change in Europe using an ensemble approach with impact response surfaces (Vol. 5).
Abstract: Adaptation can reduce climate change risks to crop production and is best analyzed at local scales considering regional specificities. Uncertainty inherent in modelling adaptation options is due to climate projections, downscaling and imperfections of crop models. The challenge of making effective adaptation decisions requires powerful approaches for exploiting the potential of genotype by environment by management interactions, and for generating projections informed with uncertainty.Here we present a methodology that constructs impact response surfaces (IRSs) from an ensemble of crop models and applies these to explore the adaptation potential of rainfed winter wheat at Lleida (NE Spain) in a water-limited environment. The simulation experiment includes: 1) a systematic sensitivity analysis to changes to baseline temperature and precipitation (1981-2010) through a delta change approach that accounts for seasonal differences, 2) three levels of CO2 representing present-day and future conditions until 2050 (A1B scenario), and 3) soil profiles representative for the variable conditions around Lleida. The adaptation simulations represent adjusted management practices about sowing, supplementary irrigation, and the thermal and vernalisation requirements of cultivars used.A pre-selection of the adaptation options was done iteratively, in ranges supported by literature review of crop adaptation in the Mediterranean (e.g. shifts from current sowing date between -30 and +45 days). This procedure allowed to identify a limited number of effective and feasible adaptations to be evaluated combining IRSs and probabilistic projections of climate change. No Label
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Rötter, R. P., & Semenov, M. A. (2014). Development of methods for the probabilistic assessment of climate change impacts on crop production (Vol. 3).
Abstract: Various attempts have been made to determine the relative importance of uncertainties in climate change impact assessments stemming from climate projections and crop models, respectively, and to analyse yield outputs probabilistically. For example, in the ENSEMBLES project, probabilistic climate projections (Harris et al. 2010) have been applied in conjunction with impact response surfaces (IRS), constructed by using impact models, to estimate the future likelihood (risk) of exceeding critical thresholds of crop yield impact (see, Fronzek et al., 2011, for an explanation of the method). In this task, we aimed to further develop and operationalize these methods and testing them in different case study regions in Europe. The method combines results of a sensitivity analysis of (one or more) impact model(s) with probabilistic projections of future temperature and precipitation (Fronzek et al., 2011). Such an overlay is one way of portraying probabilistic estimates of future impacts. By further accounting for the uncertainties in crop and biophysical parameters (using perturbed parameter approaches), the outcome represents an ensemble of impact risk estimates, encapsulating both climate and crop model uncertainties. No Label
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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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Rolinski, S., & Sætnan, E. (2013). Uncertainties in climate change prediction and modelling (Vol. 1).
Abstract: As models become increasingly complex and integrated, uncertainty among model parameters, variables and processes become critical for evaluating model outcomes and predictions. A framework for understanding uncertainty in climate modelling has been developed by the IPCC and EEA which provides a framework for discussion of uncertainty in models in general. Here we report on a review of this framework along with the results of a survey of sources of uncertainty in livestock and grassland models. Along with the identification of key sources of uncertainty in livestock and grassland modelling, the survey highlighted the need for a development of a common typology for uncertainty. When collaborating across traditionally separate research fields, or when communicating with stakeholders, differences in understanding, interpretation or emphasis can cause confusion. Further work in MACSUR should focus on improving model intercomparison methods to better understand model uncertainties, and improve availability of high quality datasets which can reduce model uncertainties. No Label
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