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The purpose of the present investigation was to examine the ability of the simple mechanistic model published by Jouven et al. (2006a) to simulate the effects of drought on the seasonal patterns of growth in managed grasslands. Data collected in Switzerland were used for reference. Different representations of the effects of water stress on transpiration and growth were tested, but otherwise all simulations were run using the standard model configuration. Results indicated that the model was able to correctly simulate the seasonality of growth across sites and years, as well as variations in soil water availability. Overall, the effects of drought on growth were realistically reproduced by the model. This was specifically the case with respect to the exceptional conditions characterizing the summer of 2003. On the other hand, there was a tendency for overestimating the impacts during conditions of moderate water stress. Replacing the original formulation of the water stress response with alternate schemes was not sufficient to bypass the problem. This suggests that it could be worth examining ways to represent drought avoidance or enhanced drought tolerance in the model. The failure to simulate a delayed start of growth in the spring of 2011 further indicate the necessity to address in a mechanistic way spring regrowth. Concerning future model developments, we argue that the inclusion of a root/soil compartment should be addressed with high priority to be able to account for the interactions between water and nutrient cycling. [ABSTRACT FROM AUTHOR]

As significant impacts of climate change are increasingly considered unavoidable, adaptation has become a policy priority. It is generally agreed that science is important for the adaptation process but specific guidance on how and to what degree science should contribute and be embedded in this process is still limited which is at odds with the high demand for science contributions to climate adaptation by international organizations, national governments and others. Here we present and analyze experiences from the tropical Andes based on a recent science-policy process on the national and supra-national government level. During this process a framework for the science contribution in climate adaptation has been developed; it consists of three stages, including (1) the framing and problem definition, (2) the scientific assessment of climate, impacts, vulnerabilities and risks, and (3) the evaluation of adaptation options and their implementation. A large amount of methods has been analyzed for stage (2), and a number of major climate adaptation projects in the region assessed for (3). Our study underlines the importance of joint problem framing among various scientific and non-scientific actors, definition of socio-environmental systems, time frames, and a more intense interaction of social and physical climate and impact sciences. Scientifically, the scarcity of environmental, social and economic data in regions like the Andes continue to represent a limitation to adaptation, and further investments into coordinated socio-environmental monitoring, data availability and sharing are essential. [ABSTRACT FROM AUTHOR]

Abstract: Process-based crop models are widely used in decision support systems or to assess impacts of climate change on agriculture at different spatial scales. They include crop and/or cultivar-specific parameters that need to be calibrated. However, the availability of reference data is often limited. An alternative is to use yield records from widely available Farm Accountancy Data Network (FADN). The goal of this study was therefore to propose and test a crop model calibration procedure that makes use of FADN data. To account for the lack of management information in the FADN databases, the concept of ‘Meteorologically Possible Yield’ (MPY) was adopted. This concept is particularly relevant in the context of climate impact studies as MPYs are by definition only driven by climate variability. As an example, the procedure was applied to calibrate the generic crop model CropSyst for a region located in north-eastern Switzerland. Validation using data from a long-term field trial with detailed information on fertilizer applications showed that the proposed procedure provides robust simulation results and is therefore suitable for climate impact studies in regions where detailed experimental data are scarce. In a case study application, the transferability of the local calibration to a site with drier conditions was tested and simulation results for this new site were compared to results obtained using local recalibration. Results showed that predicted yields can differ substantially and the differences can be strongly amplified when impacts of climate change are considered. This highlights the need for adjusting model calibrations to local site conditions and for considering parameter uncertainties in climate impact studies. [Copyright &y& Elsevier]

The Ozone DEposition Model (ODEM) was developed for national scale modeling of total and fractional ozone fluxes to grasslands and wheat using high-resolution gridded input data. The aim of this study was to improve the preliminary version 1.1 to enable season-long simulations by introducing a new parameterization of leaf area index (LAI), and the effect of soil moisture on soil surface resistance, and by refining the descriptions of friction velocity, u*, stability correction function, and all resistances depending on these parameters. Version 1.2 was tested by comparing simulation results with field measurements over grassland at Braunschweig, Germany, and wheat at Comun Nuovo, Lombardy, Italy. These comparisons showed that the simulation of ozone flux and u* for grassland was greatly improved by the modifications, yielding a root mean square error for model vs. simulation of 0.056 ppb m s−1, as compared to 0.106 ppb m s−1 obtained with the original model. Atmospheric, boundary layer, and in-canopy resistances, which all depend on u*, increased as a result of the combined modifications. For wheat, the agreement between simulated and measured ozone flux was good under wet conditions, but less during a phase characterized by drying soils. It is suggested that in-canopy processes, which are uncertain in the model, become more important under these conditions. The application of the model using 6-year ozone and meteorological data from a site in southern Switzerland (Cadenazzo) revealed that the monthly cumulative stomatal ozone flux for grassland remained more stable across the growing season than for wheat because of less variation in green LAI. Highest stomatal ozone uptake in wheat occurred during anthesis and declined later due to decreasing green LAI. In both grassland and wheat, cumulative seasonal ozone uptake was less variable between years than was the AOT40 index. The results demonstrate the improvements in ODEM 1.2, and they underline the importance of species-specific parameterizations of plant development. But it is also concluded that for high-resolution modeling of ozone fluxes the overall quality of model outputs may critically depend on the quality of an extensive set of site-specific biological, climatic, and edaphic input data. [Copyright &y& Elsevier]

The reduction of N fertilization in agriculture as part of the Farm to Fork (F2F) strategy plays a central role in the integrated nutrient management action plan of the European Commission. However, the implications of this strategy for mitigating N losses and possible side-effects on grassland yields under global change are largely unknow. We examined how a 20% reduction in N fertilization according to the F2F strategy is likely to impact yields, N 2 O emissions and N leaching of four intensively managed temperate grasslands in the Alpine region, two of them located in Switzerland, the other two in Germany. Following automatic data-driven calibration supported by inverse modeling and a cross-validation step, the process-based model DayCent was used for conducting the analysis. Global change scenarios under the representative concentration pathways (RCPs) 4.5 and 8.5 and a baseline scenario (current climate) were created for the time frame 2041–2060 with the help of the stochastic weather generator LARS-WG. Our results indicated that, under current conditions of climate and CO 2 levels (400 ppm), a 20% decrease in N fertilization would lead to a 5% drop in yields, but also in a 15% decline in N 2 O emissions and a 21% decline in N leaching (largely as NO 3 −). Under global change conditions (i.e. , climate change and higher atmospheric CO 2 levels), we found that increased yields, mainly induced by higher CO 2 levels, are likely to compensate for yield losses resulting from the reduction in N fertilization. In addition, we found that the effectiveness of the F2F strategy to mitigate N losses is likely to be preserved under global change, still with stronger effect on N leaching. The F2F-induced decline in N losses was stronger when the latter were expressed per unit of harvested dry matter, i.e. , up to 17% for N 2 O and up to 42% for N leaching. Although significant, these abatements in N losses are still below the 50% reduction level envisaged by the F2F strategy. Actions related to other axes of the strategy (e.g. , sustainable food consumption) will be necessary to further reduce N fertilization and, therefore, to reach this ambitious goal. Our results highlight the usefulness of models in accounting for interacting effects of global change and mitigation practices on multiple ecosystem services of grasslands. They allow quantification of the impact of new policies. [Display omitted] • Reducing N fertilization and losses plays a central role in the EU's Farm to Fork (F2F) strategy. • Possible side effects on yields of Alpine grasslands were quantified using a mechanistic model. • Yield losses due to reduced N fertilization are offset by positive effects of rising CO 2 levels. • Yield-scaled N losses as N 2 O and leaching tend to decrease in future scenarios with global change and the F2F strategy. • The impact of integrating all aspects of the F2F strategy should be further investigated. [ABSTRACT FROM AUTHOR]

Abstract: In order to analyze impacts of climate change on managed grassland systems and to project potential changes in farmers’ management practices in response to altered climatic conditions, we develop a modeling approach that integrates a process-based grassland model into an economic model. This economic model describes farmers’ decision making with respect to input use and accounts for production levels, production risks, fodder quality as determined by the grassland composition, and environmental protection. We apply the bio-economic model to an intensively managed grassland system with a geographic focus on the Swiss Plateau. Our results show an increase of future production risks in grassland production due to climate change. Projected changes in yield levels, grassland composition and optimal responses of risk-averse farmers are dependent on the assumptions concerning cross-compliance obligations, forage quality and particularly on the assumed effect of elevated CO2 concentrations: Grasslands yields will increase under future climatic conditions only if the benefits of rising atmospheric CO2 concentrations are taken into account. Without this potential benefit, climate change will lead to less intensive input use and lower grassland yields. [ABSTRACT FROM AUTHOR]

• Earlier onset of pre-alpine cut grassland growing season entails shifts to earlier cutting dates and more cuts per year. • Dynamic simulation of cutting dates can tackle these shifts if temperature and soil moisture are considered. • Climate change increase pre-alpine grassland yields only if growth is not limited by soil moisture and nitrogen. The productivity of permanent temperate cut grasslands is mainly driven by weather, soil characteristics, botanical composition and management. To adapt management to climate change, adjusting the cutting dates to reflect earlier onset of growth and expansion of the vegetation period is particularly important. Simulations of cut grassland productivity under climate change scenarios demands management settings to be dynamically derived from actual plant development rather than using static values derived from current management operations. This is even more important in the alpine region, where the predicted temperature increase is twice as high as compared to the global or Northern Hemispheric average. For this purpose, we developed a dynamic management module that provides timing of cutting and manuring events when running the biogeochemical model LandscapeDNDC. We derived the dynamic management rules from long-term harvest measurements and monitoring data collected at pre-alpine grassland sites located in S-Germany and belonging to the TERENO monitoring network. We applied the management module for simulations of two grassland sites covering the period 2011–2100 and driven by scenarios that reflect the two representative concentration pathways (RCP) 4.5 and 8.5 and evaluated yield developments of different management regimes. The management module was able to represent timing of current management operations in high agreement with several years of field observations (r² > 0.88). Even more, the shift of the first cutting dates scaled to a +1 °C temperature increase simulated with the climate change scenarios (−9.1 to −17.1 days) compared well to the shift recorded by the German Weather Service (DWD) in the study area from 1991−2016 (−9.4 to −14.0 days). In total, the shift in cutting dates and expansion of the growing season resulted in 1−2 additional cuts per year until 2100. Thereby, climate change increased yields of up to 6 % and 15 % in the RCP 4.5 and 8.5 scenarios with highest increases mainly found for dynamically adapted grassland management going along with increasing fertilization rates. In contrast, no or only minor yield increases were associated with simulations restricted to fertilization rates of 170 kg N ha−1 yr−1 as required by national legislations. Our study also shows that yields significantly decreased in drought years, when soil moisture is limiting plant growth but due to comparable high precipitation and water holding capacity of soils, this was observed mainly in the RCP 8.5 scenario in the last decades of the century. [ABSTRACT FROM AUTHOR]

• A model of the community dynamics of semi-natural grasslands is presented in detail. • The model was parameterized for species often observed in the Jura Mountains. • Management is a key driver of the simulated composition of the multi-species sward. • Initial community structure has a low impact at long-term on functional composition. Permanent grasslands host a high plant diversity, which sustains many ecosystem services. Thus, understanding how composition of the plant community responds to different management practices under given soil and climatic conditions is crucial for making best use of grasslands. modeling approaches may be used to explain the manifold interactions involved to sustain this diversity. We developed the dynamic, process-based ecological model DynaGraM to simulate the seasonal aboveground vegetation dynamics of semi-natural grasslands. The model allows specifying plant community by any number of species. The predictive power of the model was assessed by simulating the dynamics of a virtual mountain grassland in response to four typical management scenarios under constant climatic conditions over several decades. In our experiments, we modelled an assemblage of seven species representing contrasted plant functional types. We compared model outputs to compositions inferred from floristic records conducted in the French Jura Mountains. Irrespective of initial conditions, the simulated community converged to four distinct compositions that primarily reflected management. Overall, the results matched the functional composition inferred for each of the scenarios from the botanical records. Convergence in functional composition was reached in less than 15 years under grazing scenarios, but not less than 50 years under mowing scenarios. At quasi-equilibrium, the highest vegetation diversity was obtained for extensive grazing and the lowest for extensive mowing. Overall, this study introduces a novel and relatively simple approach to model competition and adaptation processes in plant community dynamics, thus providing a response to the key challenge of modeling multi-species grasslands. [ABSTRACT FROM AUTHOR]

The impact of extreme events (such as prolonged droughts, heat waves, cold shocks and frost) is poorly represented by most of the existing yield forecasting systems. Two new model-based approaches that account for the impact of extreme weather events on crop production are presented as a way to improve yield forecasts, both based on the Crop Growth Monitoring System (CGMS) of the European Commission. A first approach includes simple relations – consistent with the degree of complexity of the most generic crop simulators – to explicitly model the impact of these events on leaf development and yield formation. A second approach is a hybrid system which adds selected agro-climatic indicators (accounting for drought and cold/heat stress) to the previous one. The new proposed methods, together with the CGMS-standard approach and a system exclusively based on selected agro-climatic indicators, were evaluated in a comparative fashion for their forecasting reliability. The four systems were assessed for the main micro- and macro-thermal cereal crops grown in highly productive European countries. The workflow included the statistical post-processing of model outputs aggregated at national level with historical series (1995–2013) of official yields, followed by a cross-validation for forecasting events triggered at flowering, maturity and at an intermediate stage. With the system based on agro-climatic indicators, satisfactory performances were limited to microthermal crops grown in Mediterranean environments (i.e. crop production systems mainly driven by rainfall distribution). Compared to CGMS-standard system, the newly proposed approaches increased the forecasting reliability in 94% of the combinations crop × country × forecasting moment. In particular, the explicit simulation of the impact of extreme events explained a large part of the inter-annual variability (up to +44% for spring barley in Poland), while the addition of agro-climatic indicators to the workflow mostly added accuracy to an already satisfactory forecasting system. [ABSTRACT FROM AUTHOR]

Under the United Nations Framework Convention on Climate Change (UNFCCC), industrialized countries and countries with economies in transition (so called Annex 1 countries) are encouraged to move towards more sophisticated approaches for national greenhouse gas reporting. To develop a model-based approach for estimating nitrous oxide (N 2 O) emissions from agricultural soils, model calibration is one of the first important steps. Extensive multisite field observations are necessary for this purpose, as agricultural management in Western Europe is complex (e.g. , diverse crop rotations, different types of fertilizer and soil tillage). In the present study, we used ca. 24,000 daily N 2 O flux observations from six cropland sites, two in France and four in Switzerland, to conduct an automatic data-driven calibration of the biogeochemical model DayCent. This model is planned to be used for greenhouse gas reporting in the entire European Union as well as in Switzerland. After a site-specific calibration, a leave-one-out (LOO) cross-evaluation was conducted to assess the model's ability to predict N 2 O emissions for sites it was not calibrated for. Mean observed N 2 O fluxes for 54 interactions of crop cycles, field studies and treatments were used to evaluate the model. The LOO cross-evaluation resulted in a R 2 of 0.63 for the prediction of mean N 2 O fluxes per crop cycle, compared to an R 2 of 0.51 obtained with default parameterization. Our results showed that the improvement in N 2 O predictions was associated with the adjustment of only seven parameters controlling the N cycle in soil (e.g. , the maximum daily nitrification amount and the inflection point for the effect of water-filled pore space on denitrification) out of several hundred parameters. These parameters showed a wide range of values between sites, revealing an important challenge for calibration-based improvement of N 2 O simulations. Despite the remaining uncertainty, our model-based estimates of N 2 O emission per crop cycle (2.64 kg N ha-1) were clearly closer to measurements (2.67 kg N ha-1) than commonly used emission factor approaches (1.60–1.71 kg N ha-1). Based on extensive field observations, our results suggest that, after data-driven calibration of only few N cycle parameters, DayCent simulations are useful for reporting N 2 O emissions of complex cropland management. These model based-estimates were more accurate, because they consider key drivers that are disregarded by simpler approaches. Moving towards more complex methods of N 2 O reporting, is therefore expected to improve the accuracy and additionally allows to assess mitigation options. • Model estimates of N 2 O emissions can improve national greenhouse gas reporting. • Calibration based on field data is a critical first step for modeling N 2 O emissions. • N 2 O observations from six sites were used to test DayCent's performance. • The model's performance could be improved by calibrating only few N cycle parameters. • DayCent simulations were clearly more accurate than emission factor approaches. [ABSTRACT FROM AUTHOR]

Spring wheat is the principal crop in the steppe zone of Russia and Kazakhstan, but wheat productivity levels are currently low and susceptible to weather and climate anomalies. Water scarcity during the growing season represents a major stress factor and is expected to negatively affect wheat production in the future as well. In this paper we present a simple mechanistic model for assessing the impact of climate variability on spring wheat productivity in the steppe zone of Russia and Kazakhstan. The novel aspect of the model development is represented by the adoption of an adaptive approach for the formulation of growth partitioning. In spite of simplifying assumptions the model is shown to satisfactorily reproduce yield levels observed both at the local scale under controlled conditions as well as at the regional scale. The model is able to capture a significant percentage of the observed year-to-year variability of wheat yields. Results of the model application indicate that, for the steppe zone of Russia and Kazakhstan, seasonal water shortage is likely to cause yield deficits of 20–25%, with deficits of up to 40% in extreme years, and an increase in the coefficient of variation of yields. [Copyright &y& Elsevier]

Abstract: Climate change will alter the environmental conditions for crop growth and require adjustments in management practices at the field scale. In this paper, we analyzed the impacts of two different climate change scenarios on optimal field management practices in winterwheat and grain maize production with case studies from Switzerland. Management options included nitrogen fertilization (amount, timing and allocation) as well as irrigation. Optimal solutions that maximize the farmer’s utility were sought with the help of a bioeconomic modeling system that integrated the process-based crop growth model CropSyst into an economic decision model. The latter accounted not only for the crop specific average profit margins, but also for production risks, reflecting the utility (expressed as the certainty equivalent) of a risk-averse farmer’s management decisions at field scale. In view of the non-linearity and complexity of the problem, we used a genetic algorithm as optimization technique. For grain maize, our results showed that climate change will foster the use of irrigation, not only at sites prone to water limitation already under current climatic conditions, but more in general for climate change scenarios projecting a substantial decrease in summer precipitation. For winterwheat, irrigation was never identified as an optimal management option. For both crops and sites, climate change reduced the optimum nitrogen fertilization amount and decreased for winterwheat the number of fertilization applications. In all cases, the farmer’s certainty equivalent decreased between 7% and 25% under climate change, implying negative impacts on winterwheat and grain maize production even under the assumption of an adjustment of the optimum management practices. [Copyright &y& Elsevier]