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

Abstract: Impact response surfaces (IRSs) of spring and winter wheat yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect in Europe. The sensitivity of modelled yield to systematic increments of changes in temperature (-2 to +9°C) and precipitation (-50 to +50%) was tested by modifying values of 1981–2010 baseline weather.In spite of large differences in simulated yield responses to both baseline and changed climate between models, sites, crops and years, several common messages emerged. Ensemble average yields decline with higher temperatures (3–7% per 1°C) and decreased precipitation  (3–9% per 10% decrease), but benefit from increased precipitation (0-8% per 10% increase). Yields are more sensitive to temperature than precipitation changes at the Finnish site while sensitivities are mixed at the German and Spanish sites. Precipitation effects diminish under higher temperature changes. Inter-model variability is highest for baseline climate at the Spanish site, but relatively insensitive to changed climate. Modelled responses diverge most at the Finnish and German sites for winter wheat under temperature change. The IRS pattern of yield reliability tracks average yield levels. Inter-annual yield variability is more sensitive to precipitation than temperature, except at the Spanish site for spring wheat.Optimal temperatures for present-day cultivars are close to the baseline under Finnish conditions but below the baseline at the German and Spanish sites. This suggests that adoption of later maturing cultivars with higher temperature requirements might already be advantageous, and increasingly so under future warming. No Label