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The draining and fertilization of peatlands for agriculture is globally an important source of the greenhouse gas nitrous oxide (N2O). Hitherto, the contribution of major sources to the N2O emission—that is, fertilization and nitrogen (N) release from peat decomposition—has not yet been deciphered. This hampers the development of smart mitigation strategies, considering that rewetting to halt peat decomposition and reducing N fertilization are promising N2O emission‐reduction strategies. Here, we used machine learning techniques and global N2O observational data to generalize the distribution of N2O emissions from agriculturally managed peatlands, to distinguish the sources of N2O emissions, and to compare mitigation options. N2O emissions from agriculturally managed croplands were 401.0 (344.5–470.9) kt N year−1, with 121.6 (88.6–163.3) kt N year−1 contributed by fertilizer N. On grasslands, 64.0 (54.6–74.7) kt N2O‐N year−1 were emitted, with 4.6 (3.7–5.7) kt N2O‐N year−1 stemming from fertilizer N. The fertilizer‐induced N2O emission factor ranged from 1.5% to 3.2%. Reducing the current fertilizer input by 20% could achieve a 10% N2O emission reduction for croplands but only 3% for grasslands. Rewetting 1.9 Mha cropland and 0.26 Mha grassland would achieve the same N2O emission reductions. Our results suggest that N2O mitigation strategies for managed peatlands should be considered separately across land‐use types and climatic zones. For croplands, particularly in the tropics, relevant N2O mitigation potentials are achievable through both fertilizer N reduction and peatland rewetting. For grasslands, management schemes to halt peat degradation (e.g. rewetting) should be considered preferentially for mitigating N2O and contributing to meeting climate goals. [ABSTRACT FROM AUTHOR]

Climate change and globalization affect the suitable conditions for agricultural crops and insect pests, threatening future food security. It remains unknown whether shifts in species' climatic suitability will be linear or rather non‐linear, with crop exposure to pests suddenly increasing when a critical temperature threshold is crossed. Moreover, uncertainty of forecasts can arise because of the modelling approach based either on species distribution data or on physiological measurements. Here, we compared the predictions of two modelling approaches (physiological models and species distribution models) for forecasting the potential distribution of agricultural insect pests in Europe. Despite conceptual differences, we found good agreement overall between the two approaches. We further identified a potential regime change in pest pressure along a temperature gradient. With both modelling approaches, we found an inflection point in the number of pest species with suitable climatic conditions around a minimum temperature of the coldest month of −3°C. Our results could help decision‐makers anticipate the onset of rising pest pressure and provide support for intensifying surveillance measures, particularly in regions where temperatures are already beyond the inflection point. [ABSTRACT FROM AUTHOR]

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]

We present the integration of early 21st century climate projections for Europe based on simulations carried out within the EU-FP6 ENSEMBLES project with the LARS-WG stochastic weather generator. The aim was to upgrade ELPIS, a repository of local-scale climate scenarios for use in impact studies and risk assessments that already included global projections from the CMIP3 ensemble and regional scenarios for Japan. To obtain a more reliable simulation of daily rainfall and extremes, changes in wet and dry series derived from daily ENSEMBLES outputs were taken into account. Kernel average smoothers were used to reduce noise arising from sampling artefacts. Examples of risk analyses based on 25-km climate projections from the ENSEMBLES ensemble of regional climate models illustrate the possibilities offered by the updated version of ELPIS. The results stress the importance of tailored information for local-scale impact assessments at the European level. [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 effectiveness of hedging drought risks with weather derivatives was investigated for rain-fed grain maize production in Switzerland under current (198 1-2003) and projected future (2070-2100) climatic conditions. Depending on location, hedging reduced the value-at-risk (VaR) measure to a variable degree, although with a considerable basis risk, but hedging may provide a valid risk transfer since loading of 90% to 240% of the fair premium can be paid to obtain a hedged situation with improved outcomes relative to the reference. However, the fair premium of a specific contract may vary by a factor of two to four over the 70-year period considered, which represents a substantial uncertainty for both the farmer and the institution writing the contract. [ABSTRACT FROM AUTHOR]

Abstract: There are clear indications that in the future the Alpine region will increasingly suffer from droughts. In this paper, a simple method is devised for determining probability of drought occurrence and exceedance of severity thresholds. A reconstruction of drought occurrence during the past 100 yr indicates that the method is able to detect major events and also to correctly gauge their relative severity. The procedure is used in conjunction with climate simulations for the European region valid for 2071–2100 to study the impact of climate change on the likelihood and severity of droughts. The climate scenario used for the analysis refers to a SRES A2 emission pathway and specifies in particular a decrease in the frequency of wet days of about 20% with respect to the growing season of summer crops (April to September). Under these conditions the frequency of droughts is shown to increase from about 15% to more than 50%. In addition, the results indicate an overall shift in the distribution toward higher severity scores. The average severity increases by a factor of two, but also at the upper end of the spectrum severity increases by more than 20%. It is argued that this will affect the perception of extreme droughts, i.e. of those events rarer than the 10th percentile of the distribution. If this scenario comes true, by the end of the 21st century droughts comparable in severity to the 2003 event would represent the norm rather than the exception. [Copyright &y& Elsevier]

Summary: Climate change is expected to have a profound influence on soil moisture. According to that the present study aimed at the investigation of future soil water evolution by considering the relationship between soil moisture changes resulting from climate change projections, soil, and terrain characteristics, with special emphasis on slope and groundwater depth. The distributed hydrological model WaSiM-ETH was used to simulate root zone soil water content at a 1km grid resolution in the 1700km2 Thur river basin (NE Switzerland) during the 6-month period from April to September. The model was driven by baseline climate data from national data records for 1981–2000, or by scenario data for 2081–2100. The latter were based on grid point projections of two global climate models (CSIRO [B2] and HadCM3 [A2]). The datasets were completed with observations and simulations for the exceptionally warm and dry summer of 2003. The results suggested that a warmer climate with less summertime precipitation may significantly lower the seasonal mean soil water content in many parts of this basin, leading to frequent water stress conditions. Strongest effects occurred with HadCM3, and to a similar extent with 2003 weather data. The magnitude of soil moisture changes were related to land use, soil texture, and slope. In relative terms, reductions in soil water were largest for sloping soils with low water storage capacity, and also larger for forests than for cropland and grasslands. In absolute (volumetric) units, most pronounced reductions were indicated for flat areas with good water supply (mostly dominated by cropland). Here, the rooting zone was often connected to the groundwater, and capillary rise counteracted soil water depletion under current climate during the first half of the season, but was disconnected much earlier under climate change conditions. In steeper areas, groundwater had no influence and thus soil water content changed mainly in response to decreased precipitation and increased evapotranspiration. It is concluded that soil water contents generally decline in this pre-alpine river basin with climate change, but that the degree of soil water depletion varies with climate scenario, land use, soil texture, and topographic conditions. Thus, realistic soil water projections require reliable predictions of summertime climate, and the use of a full representation of biophysical processes that control evapotranspiration, including vertical and lateral subsurface water flows. [Copyright &y& Elsevier]

During a special observing period (SOP) of the Mesoscale Alpine Programme (MAP), boundary layer processes in highly complex topography were investigated in the Riviera Valley in southern Switzerland. The main focus was on the turbulence structure and turbulent exchange processes near the valley surfaces and free troposphere. Due to the anticipated spatial inhomogeneity, a number of different turbulence probes were deployed on a cross section through the valley. Together with a suite of more conventional instrumentation, to observe mean meteorological structure in the valley, this effort yielded a highly valuable dataset. The latter is presently being exploited to yield insight into the turbulence structure in very complex terrain, and its relation to flow regimes and associated mean flow characteristics. Specific questions, such as a detailed investigation of turbulent exchange processes over complex topography and the validity of surface exchange parameterizations in numerical models for such surfaces, the closure of the surface energy balance, or the definition and meaning of the “boundary layer height,” are investigated using the MAP-Riviera dataset. In the present paper, we provide details on sites and their characteristics, on measurements and observational strategies, and on efforts to guarantee comparability between different instrumentation at different sites, and we include an overview of the available instrumentation. On the basis of preliminary data and first results, the main research goals of the project are outlined. [ABSTRACT FROM AUTHOR]

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]

In this study, we investigated the consequences of climate change on bioclimatic indices in vineyards along the edge of Lake Neuchatel in Switzerland. Like in other vineyards all around the world, the typicity of wines and the phenology of vines have changed, particularly since the 1970s. Trends in the growing season average temperature and in Huglin's heliothermal index show that the climate in the Neuchatel vineyards changed from very cool or cool to temperate during the last decades. Trends in the cool night index and in the prior to harvest cool night index both indicate that in the near future this wine region will frequently experience temperate instead of cool nights during the weeks leading up to harvest. Our results highlight the need for adaptation strategies, such as an upward elevational shift for Pinot Noir, as climatic conditions will become too warm at its current location in the next decades. They also show that conditions in this region are already favorable for more thermophilic varieties such as Merlot. In the context of global warming, this kind of analysis should be conducted throughout winegrowing regions in order to develop efficient adaptation strategies at the microclimatic scale. [ABSTRACT FROM AUTHOR]

Global changes pose both risks and opportunities to agriculture and forestry, and biological forecasts can inform future management strategies. Here, we investigate potential land-use opportunities arising from climate change for these sectors in Europe, and risks associated with the introduction and establishment of novel insect pests. Adopting a metaweb approach including all interaction links between 126 crops and forest tree species and 89 black-listed insect pest species, we show that the metawebs shift toward increased numbers of links and overlap of suitable area under climate change. Decomposing the metaweb across regions shows large saturation in southern Europe, while many novel interactions are expected for northern Europe. In light of the rising consumer awareness about human health and environmental impacts of food and wood production, the challenge will be to effectively exploit new opportunities to create diverse local agriculture and forestry while controlling pest species and reducing risks from pesticide use. Marc Grünig et al. report a study of land-use opportunities and risks of introducing novel insect pests in Europe that may arise from global climate change. Using a metaweb approach, they find that there is a predicted general increase in risk of pests to managed plant species under climate change due to an increase in land with suitable climate for both pests and plants. [ABSTRACT FROM AUTHOR]

Accumulated growing degree-days (aGDD) are widely used to predict phenological stages of plants and insects. It has been shown in the past that the best predictive performance is obtained when aGDD are computed from hourly temperature data. As the latter are not always available, models of diurnal temperature changes are often employed to retrieve the required information from data of daily minimum and maximum temperatures. In this study, we examine the performance of a well-known model of hourly temperature variations in the context of a spatial assessment of aGDD. Specifically, we examine whether a generic calibration of such a temperature model is sufficient to infer in a reliable way spatial patterns of key phenological stages across the complex territory of Switzerland. Temperature data of a relatively small number of meteorological stations is used to obtain a generic model parameterization, which is first compared with site-specific calibrations. We show that, at the local scale, the predictive skill of the generic model does not significantly differ from that of the site-specific models. We then show that for aGDD up to 800 °C d (on a base temperature of 10 °C), phenological dates predicted with aGDD obtained from estimated hourly temperature data are within ± 3 days of dates estimated on the basis of observed hourly temperatures. This suggests the generic calibration of hourly temperature models is indeed a valid approach for pre-processing temperature data in regional studies of insect and plant phenology. [ABSTRACT FROM AUTHOR]

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]

Aim To quantify the relative importance of propagule pressure, climate-matching and host availability for the invasion of agricultural pest arthropods in Europe and to forecast newly emerging pest species and European areas with the highest risk of arthropod invasion under current climate and a future climate scenario ( A1 F1). Location Europe. Methods We quantified propagule pressure, climate-matching and host availability by aggregating large global databases for trade, European arthropod interceptions, Koeppen-Geiger world climate classification (including the A1 F1 climate change scenario until 2100) and host plant distributions for 118 quarantine arthropod species. Results As expected, all the three factors, propagule pressure, climate suitability and host availability, significantly explained quarantine arthropod invasions in Europe, but the propagule pressure only had a positive effect on invasion success when considered together with climate suitability and host availability. Climate change according to the A1 F1 scenario generally increased the climate suitability of north-eastern European countries and reduced the climate suitability of central European countries for pest arthropod invasions. Main conclusions To our knowledge, this is the first demonstration that propagule pressure interacts with other factors to drive invasions and is not alone sufficient to explain arthropod establishment patterns. European countries with more suitable climate and large agricultural areas of suitable host plants for pest arthropods should thus be more vigilant about introduction pathways. Moreover, efforts to reduce the propagule pressure, such as preventing pests from entering pathways and strengthening border controls, will become more important in north-eastern Europe in the future as the climate becomes more favourable to arthropod invasions. [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]

In the semi-arid Peruvian Andes, the growing season is mostly determined by the timing of the onset and retreat of the wet season, to which annual crop yields are highly sensitive. Recently, local farmers in the Rio Santa basin (RSB) reported more erratic rainy season onsets and further challenges related to changes in rainfall characteristics. Previous studies based on local rain gauges, however, did not find any significant long-term rainfall changes, potentially linked to the scarce data basis and inherent difficulties in capturing the highly variable rainfall distribution typical for complex mountain terrain. To date, there remains considerable uncertainty in the RSB regarding changes in plant-available water over the last decades. In this study, we exploit satellite-derived information of high-resolution vegetation greenness as an integrated proxy to derive variability and trends of plant water availability. By combining MODIS Aqua and Terra vegetation indices (VIs), datasets of precipitation (both for 2000–2020) and soil moisture (since 2015), we explore recent spatio-temporal changes in the vegetation growing season. We find the Normalized Difference Vegetation Index (NDVI) to be coupled to soil moisture on a sub-seasonal basis, while NDVI and rainfall only coincide on interannual timescales. Over 20 years, we find significant greening in the RSB, particularly pronounced during the dry season (austral winter), indicating an overall increase in plant-available water over the past 2 decades. The start of the growing season (SOS) exhibits high interannual variability of up to 2 months compared to the end of the growing season (EOS), which varies by up to 1 month, therefore dominating the variability of the growing season length (LOS). The EOS becomes significantly delayed over the analysis period, matching the observed dry-season greening. While both in situ and gridded rainfall datasets show incoherent changes in annual rainfall for the region, Climate Hazards InfraRed Precipitation with Station data (CHIRPS) rainfall suggests significant positive dry-season trends for 2 months coinciding with the most pronounced greening. As the greening signal is strongly seasonal and reaches high altitudes on unglaciated valley slopes, we cannot link this signal to water storage changes on timescales beyond one rainy season, making interannual rainfall variability the most likely driver. Exploring El Niño–Southern Oscillation (ENSO) control on greening, we find an overall increased LOS linked to an earlier SOS in El Niño years, which however cannot explain the observed greening and delayed EOS. While our study could not corroborate anecdotal evidence of recent changes, we confirm that the SOS is highly variable and conclude that rainfed farming in the RSB would profit from future efforts being directed towards improving medium-range forecasts of the rainy season onset. [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]

In the semi-arid Peruvian Andes, the growing season is mostly determined by the timing of the onset and retreat of the wet season, to which annual crop yields are highly sensitive. Recently, local farmers in the Rio Santa basin (RSB) reported decreasing predictability of the onset of the rainy season and further challenges related to changes in rainfall characteristics. Previous studies based on time series of local rain gauges however, did not find any significant changes in either the timing or intensity of the wet season. Both in-situ and satellite rainfall data for the region lack the necessary spatial resolution to capture the highly variable rainfall distribution typical for complex terrain, and are often questionable in terms of quality and temporal consistency. To date, there remains considerable uncertainty in the RSB regarding hydrological changes over the last decades. In this study, we overcome this limitation by exploiting satellite-derived information on vegetation greenness to reveal a robust and highly resolved picture of recent changes in rainfall and vegetation phenology across the region: As the semi-arid climate causes water availability (i.e. precipitation) to be the key limiting factor for plant growth, patterns of precipitation occurrence and the seasonality of vegetation indices (VIs) are tightly coupled. Therefore, VIs can serve as an integrated proxy of rainfall. By combining MODIS Aqua and Terra VIs for 2000-2020 and several datasets of precipitation, we explore recent spatio-temporal changes in vegetation and water availability. Furthermore, we examine their links to El Niño Southern Oscillation (ENSO). While different rainfall datasets tend to be incoherent in the period of observation, we find significant greening over the majority of the RSB domain in VI data, particularly pronounced during the dry season (Austral winter). This indicates an overall increase of plant available water over time. The rainy season onset and consequently the start of the growing season (SOS) exhibits high inter-annual variability and dominates the growing season length (LOS). The end of the growing season (EOS) is significantly delayed in the analysis which matches the observed dry-season greening. By partitioning the results into periods of three stages of ENSO (neutral, Niño, Niña), we find an earlier SOS and an overall increased season length in years associated with El Niño. However, the appearance of Niño/Niña events during the analysed period cannot explain the observed greening and delayed EOS. While our study could not corroborate anecdotal evidence for recent changes in the SOS, we confirm that the SOS is highly variable and conclude that rainfed farming in the RSB would profit from future efforts being directed towards improving medium-range forecasts of the rainy season onset. [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]

We document the release of webXTREME , a new online tool for the evaluation of indices of climatic extremes (extreme temperatures and aridity) having impact on agricultural production. The tool is globally available and can be operated with either observed weather data or time series representing future climatic conditions. It is thus suitable for risk evaluation under climate change. webXTREME was implemented using Shiny , an open-source programming framework for creating web applications on the basis of the R Statistical Language. [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]

Yield forecasts are generally based on a combination of expert knowledge, survey data, statistical analyses and model simulations. These forecasts, when public, influence crop prices and can be used to estimate end-of-season stocks. Thus, the skills and limitations of such products are important because they inform trade policies. In Europe, yield forecasts are made available to stakeholders throughout the growing season via the monthly MARS (Monitoring Agricultural ResourceS) Bulletin. The MARS Crop Yield Forecasting System relies on an in-depth analysis of past climate, short-term weather forecasts and crop growth simulations. In this paper, we focus on the occurrence of abnormally low yields and evaluate how accurately agro-climatic indicators and model outputs anticipate their occurrences for two crop species in two European countries of contrasted agroclimatic conditions. Importantly, the indicators considered here encompass a large range of complexity levels and several are used to inform the yield forecasts presented in the MARS Bulletin. Each indicator is independently used to predict the onset occurrence of an abnormal yield loss (henceforth named extreme) in France and in Spain for both winter wheat and non-irrigated grain maize for a period covering the 1976–2013 growing seasons. Indicators are ranked based on a score quantifying their ability to accurately separate extreme from non-extreme yield loss events. We provide and in-depth analysis of the robustness of our ranking to alternative definitions of extreme yield loss. No single indicator performs systematically well (e.g., whatever the country or crop species) but several show acceptable scores (e.g., averaged temperatures, vapor pressure deficit, precipitation, potential yield). We find no obvious relationship between the level of complexity of indicators and their accuracy. Single climate variables such as temperature or precipitation often perform as well as a crop model integrating temperatures and precipitation effects on crop growth. Namely, monthly averaged maximum temperatures and precipitation rank highest for both crop species in France. Drought indices perform well in Spain for wheat and for maize. We argue that our transparent framework can be useful to evaluate and improve crop-monitoring systems worldwide. [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]

Monthly weather forecasts (MOFCs) were shown to have skill in extratropical continental regions for lead times up to 3 weeks, in particular for temperature and if weekly averaged. This skill could be exploited in practical applications for implementations exhibiting some degree of memory or inertia toward meteorological drivers, potentially even for longer lead times. Many agricultural applications fall into these categories because of the temperature-dependent development of biological organisms, allowing simulations that are based on temperature sums. Most such agricultural models require local weather information at daily or even hourly temporal resolution, however, preventing direct use of the spatially and temporally aggregated information of MOFCs, which may furthermore be subject to significant biases. By the example of forecasting the timing of life-phase occurrences of the codling moth (Cydia pomonella), which is a major insect pest in apple orchards worldwide, the authors investigate the application of downscaled weekly temperature anomalies of MOFCs for use in an impact model requiring hourly input. The downscaling and postprocessing included the use of a daily weather generator and a resampling procedure for creating hourly weather series and the application of a recalibration technique to correct for the original underconfidence of the forecast occurrences of codling moth life phases. Results show a clear skill improvement of up to 3 days in root-mean-square error over the full forecast range when incorporating MOFCs as compared with deterministic benchmark forecasts using climatological information for predicting the timing of codling moth life phases. [ABSTRACT FROM AUTHOR]

Global warming will lead to earlier beginnings and prolongation of growing seasons in temperate regions and will have pronounced effects on phenology and life-history adaptation in many species. These changes were not easy to simulate for actual phenologies because of the rudimentary temporal (season) and spatial (regional) resolution of climate model projections. We investigate the effect of climate change on the regional incidence of a pest insect with nearly worldwide distribution and very high potential for adaptation to season length and temperature - the Codling Moth, Cydia pomonella. Seasonal and regional climate change signals were downscaled to the hourly temporal scale of a pest phenology model and the spatial scale of pest habitats using a stochastic weather generator operating at daily scale in combination with a resampling approach for simulation of hourly weather data. Under future conditions of increased temperatures (2045-2074), the present risk of below 20% for a pronounced second generation (peak larval emergence) in Switzerland will increase to 70-100%. The risk of an additional third generation will increase from presently 0-2% to 100%. We identified a significant two-week shift to earlier dates in phenological stages, such as overwintering adult flight. The relative extent (magnitude) of first generation pupae and all later stages will significantly increase. The presence of first generation pupae and later stages will be prolonged. A significant decrease in the length of overlap of first and second generation larval emergence was identified. Such shifts in phenology may induce changes in life-history traits regulating the life cycle. An accordingly lifehistory adaptation in photoperiodic diapause induction to shorter day-length is expected and would thereby even more increase the risk of an additional generation. With respect to Codling Moth management, the shifts in phenology and voltinism projected here will require adaptations of plant protection strategies to maintain their sustainability. [ABSTRACT FROM AUTHOR]

Abstract: We aimed to characterise the coincidence of yield variations with weather variables for major field crops using long-term datasets and reveal whether there are commonalities across the European agricultural regions. Long-term national and/or regional yield datasets were used from 14 European countries (total of 25 regions). Crops studied were spring and winter barley and wheat, winter oilseed rape, potato and sugar beet. Relative yield deviations were determined for all crops. Meteorological data on monthly means for temperature variables, solar radiation, accumulated precipitation and evapotranspiration were provided for the relevant agricultural regions of each country for 1975–2008. Harmful effects of high precipitation during grain-filling in grain and seed crops and at flowering in oilseed rape were recorded. In potato reduced precipitation at tuber formation was associated with yield penalties. Elevated temperatures had harmful effects for cereals and rapeseed yields. [ABSTRACT FROM AUTHOR]

Climate plays a fundamental role in agriculture because of to its influence on production. All processes are regulated by specific climatic requirements. Furthermore, European agriculture, based on highly developed farming techniques, is mainly oriented to high quality food production that is more susceptible to meteorological hazards. These hazards can modify environment–genotype interactions, which can affect the quality of production. The COST 734 Action (Impacts of Climate Change and Variability on European Agriculture), launched in 2006, is composed of 28 signature countries and is funded by the European Commission. The main objective of the Action is the evaluation of possible impacts arising from climate change and variability on agriculture and the assessment of critical thresholds for various European areas. The Action will concentrate on four different tasks: agroclimatic indices and simulation models, including review and assessment of tools used to relate climate and agricultural processes; evaluation of the current trends of agroclimatic indices and model outputs, including remote sensing; developing and assessing future regional and local scenarios of agroclimatic conditions; and risk assessment and foreseen impacts on agriculture. The work will be carried out by respective Working Groups. This paper presents the results of the analysis of the first phase of inventory activity. Specific questionnaires were disseminated among COST 734 countries to collect information on climate change analysis, studies, and impact at the European level. The results were discussed with respect to their spatial distribution in Europe and to identify possible common long- and short-term strategies for adaptation. [ABSTRACT FROM AUTHOR]

This work presents a new field goniospectrometer developed at the Institute for Atmospheric and Climate Science (IAC) of the Swiss Federal Institute of Technology (ETH; Switzerland). The goniospectrometer was built to study the hemispherical directional reflectance factor (HDRF) of snow, but can also be applied to other surfaces with moderate surface roughness. The IAC ETH goniospectrometer measures HDRFs with high spatial resolution. The goniometer is exclusively built of straight parts, thus ensuring a high pointing accuracy. The two robotic arms are controlled automatically with step motors, whereby the step size can be defined by the user. With the default grid size of 15° in zenith and azimuth, the time needed to collect one complete HDRF dataset is 11 min, corresponding to a change of less than 4° in solar zenith and azimuth angles. The spectrometer comprises two probes. The first probe is equipped with a 3° foreoptic and is used for taking a spectrum of the reflected radiance; the second is placed on a tripod, has a 2π foreoptic, and simultaneously records a spectrum of the incoming irradiance. Both probes measure in the spectral range from 350 to 1050 nm, with a resolution of approximately 3 nm at around 700 nm. The performance of the new goniospectrometer was tested at the Greenland Environmental Observatory Summit Station (72°35′N, 34°30′W, 3203 m ASL) during the summer of 2004. [ABSTRACT FROM AUTHOR]

A simulation of the 1991 summer has been performed over south Greenland with a coupled atmosphere–snow regional climate model (RCM) forced by the ECMWF re-analysis. The simulation is evaluated with in-situ coastal and ice-sheet atmospheric and glaciological observations. Modelled air temperature, specific humidity, wind speed and radiative fluxes are in good agreement with the available observations, although uncertainties in the radiative transfer scheme need further investigation to improve the model’s performance. In the sub-surface snow-ice model, surface albedo is calculated from the simulated snow grain shape and size, snow depth, meltwater accumulation, cloudiness and ice albedo. The use of snow metamorphism processes allows a realistic modelling of the temporal variations in the surface albedo during both melting periods and accumulation events. Concerning the surface albedo, the main finding is that an accurate albedo simulation during the melting season strongly depends on a proper initialization of the surface conditions which mainly result from winter accumulation processes. Furthermore, in a sensitivity experiment with a constant 0.8 albedo over the whole ice sheet, the average amount of melt decreased by more than 60%, which highlights the importance of a correctly simulated surface albedo. The use of this coupled atmosphere–snow RCM offers new perspectives in the study of the Greenland surface mass balance due to the represented feedback between the surface climate and the surface albedo, which is the most sensitive parameter in energy-balance-based ablation calculations. [ABSTRACT FROM AUTHOR]

Pre-alpine grasslands provide important economic value via fodder used for milk and meat production. Grassland soils also support environmental key functions such as carbon and nitrogen storage, water retention, erosion control and biodiversity. At present, these functions are jeopardized by climate change, which is likely to be accelerated in coming decades. Beside climate change, management decisions (most important cutting and manuring frequency/amount) have a high impact on grassland yields and soil C and N dynamics. In pre-alpine regions climate change is increasing the length of the growing season mainly due to an earlier start in spring (Chang et al. 2017). Together with later autumn senescence, higher temperatures, higher CO2 concentrations and changed soil water and nutrient availability this can lead to significant changes in biomass development throughout the year. Under real field conditions, the farmer cuts the grass on demand and influenced by biomass development as well as climate and soil conditions. With an earlier start of the growing season farmers likely do the first cut earlier, which will also influence timing of further cutting and manuring events throughout the year. Similarly, drought will likely cause a delay in cutting. Thus, instead of using fixed management dates (for cutting and manuring) derived from current climate conditions, within long term climate change scenarios (e.g. RCP4.5, RCP 8.5), it is necessary to dynamically fit the timing of management practices depending on plant development as affected by predicted climate conditions.A new management module for the process-based biogeochemical model LandscapeDNDC (Kraus et al., 2014; Haas et al., 2013; Kiese et al., 2011), was designed and implemented which fulfills this topic by imitating an intensive managing farmer. Depending on simulated aboveground biomass development and detailed rule-sets, timing of grassland management (cutting and manuring) are set dynamically over the time of climate change scenario studies.The new management module was applied on two different pre-alpine grassland sites for which comprehensive data on management and yields were available. At first, the module was calibrated and successfully validated for the time period of measurements; i.e. 2012-2016. It was then applied on 90 years of climate change scenario simulations with the representative concentration pathways (RCP) 4.5 and 8.5 and comparing grassland C and N dynamics with a scenario run based on static management i.e. fixed dates.Results show that climate change affects grassland growth of especially during the start of the growing season with significant earlier first cutting dates particularly for RCP 8.5. Overall, the carbon and nitrogen stocks decrease with climate change highlighting a risk on soil fertility and thus productivity of montane grassland soils. [ABSTRACT FROM AUTHOR]