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

Abstract: Agriculture is facing increasing challenges under volatile commodity markets, on-going climate change with more frequent extreme weather events and tightened environmental constraints. Crop rotation is considered essential and may even gain more importance for sustainable farming in the context of climate change challenges while monocropping is expected to become increasingly problematic. This is, among others, because of increasing plant protection challenges due to warmer climate which is expected to result in severe droughts, heavy rainfall and waterlogging in northern latitudes more frequently. Such changes require improved soil structure and water retention, also aided by crop rotations, to avoid yield losses. Our objective is to build and apply a dynamic optimization model of farm level crop rotation on many field parcels over 30-40 years. The model takes into account various adaptation management methods such as fungicide treatment, soil improvements such as liming, and nitrogen fertilization, simultaneously with dynamic crop rotation choices. However, these management options come along with costs. Using the model, outcomes of crop growth simulation modeling can be included into economic analysis. Simulated new cultivars, suited for a longer growing season, can be defined as alternatives to current cultivars, both having specific nutrient and other input requirements such as water, labor or pesticides. The model is used in evaluating the value of future cultivars and other management practices in climate and socio-economic scenarios. The first results show that expected market prices have major impacts on the management choices, the resulting yield levels, production and income over time. No Label

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