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Hail remains a major threat to agriculture in Switzerland and beyond and assessments of current and future hail risk are of paramount importance for decision-making in the insurance industry and the agricultural sector. However, relating observational information on hail with crop-specific damages is challenging. Here, we build and systematically assess a model to predict hail damage footprints for field crops (wheat, maize, barley, rapeseed) and grapevine from the operational radar product Maximum Expected Severe Hail Size (MESHS) at different spatial resolutions. To this end, we combine the radar information with detailed geospatial information on agricultural land use and geo-referenced damage data from a crop insurer for 12 recent hail events in Switzerland. We find that for field crops, model skill gradually increases when the spatial resolution is reduced from 1 km down to 8 km. For even lower resolutions, the skill is diminished again. On the contrary, for grapevine, a lower model resolution tends to reduce skill, which is attributed to the different spatial distribution of field crops and grapevine in the landscape. It is shown that identifying a suitable MESHS thresholds to model damage footprints always involves trade-offs. For the lowest possible MESHS threshold (20 mm) the model predicts damage about two times too often (high frequency bias and number of false alarms) but also has a high probability of detection (80 %). The frequency bias decreases for larger thresholds and reaches an optimal value close to 1 for MESHS thresholds of 30–40 mm. However, this comes at the cost of a substantially lower probability of detection (around 50 %) while overall model skill remains largely unchanged. We argue that, ultimately, the best threshold selection therefore depends on the user need and the costs of a false alarm or a missed event. Finally, the frequency of false alarms can be substantially reduced when only areas with high cropland density are considered. Results from this simple, open-source model show that modelling of hail damage footprints to crops from single-polarization radar in Switzerland is skillful and is best done at 8 km resolution for field crops and 1 km for grapevine. They further allow different users of such models to identify the suitable threshold for their application, taking into account associated trade-offs. [ABSTRACT FROM AUTHOR]

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]

Mountain areas harbor large climatic and geographic gradients and form numerous habitats that promote high overall biodiversity. Compared to macroorganisms, knowledge about drivers of biodiversity and distribution of soil bacteria in mountain regions is still scarce but a prerequisite for conservation of bacterial functions in soils. An important question is, whether soil bacterial communities with similar structures share environmental preferences. Using metabarcoding of the 16S rRNA gene marker, we assessed soil bacterial communities at 255 sites of a regular grid covering the mountainous landscape of Switzerland, which is characterized by close location of biogeographic regions that harbor different land-use types. Distribution of bacterial communities was mainly shaped by environmental selection, as revealed by 47.9% variance explained by environmental factors, with pH (29%) being most important. Little additional variance was explained by biogeographic regions (2.8%) and land-use types (3.3%). Cluster analysis of bacterial community structures revealed six bacterial community types (BCTs), which were associated to several biogeographic regions and land-use types but overall differed mainly in their preference for soil pH. BCT I and II occurred at neutral pH, showed distinct preferences for biogeographic regions mainly differing in elevation and nutrient availability. BCT III and IV differed only in their preferred soil pH. BCT VI occurred in most acidic soils (pH 3.6) and almost exclusively at forest sites. BCT V occurred in soils with a mean pH of 4 and differed from BCT VI in preference for lower values of organic C, total nitrogen and their ratio. Indicator species and bipartite network analyses revealed 3,998 OTUs associating to different levels of environmental factors and BCTs. Taxonomic classification revealed opposing associations of taxa deriving from the same phyla. The results revealed that pH, land-use type, biogeographic region, and nutrient availability were the main factors shaping bacterial communities across Switzerland. Indicator species and bipartite network analyses revealed environmental preferences of bacterial taxa. Combining information of environmental factors and BCTs yielded increased resolution of the factors shaping soil bacterial communities and provided an improved biodiversity framework. OTUs exclusively associated to BCTs provide a novel resource to identify unassessed environmental drivers. [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 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]

Besides its primary role in producing food and fiber, agriculture also has relevant effects on several other functions, such as management of renewable natural resources. Climate change (CC) may lead to new trade-offs between agricultural functions or aggravate existing ones, but suitable agricultural management may maintain or even improve the ability of agroecosystems to supply these functions. Hence, it is necessary to identify relevant drivers (e.g., cropping practices, local conditions) and their interactions, and how they affect agricultural functions in a changing climate. The goal of this study was to use a modeling framework to analyze the sensitivity of indicators of three important agricultural functions, namely crop yield (food and fiber production function), soil erosion (soil conservation function), and nutrient leaching (clean water provision function), to a wide range of agricultural practices for current and future climate conditions. In a two-step approach, cropping practices that explain high proportions of variance of the different indicators were first identified by an analysis of variance-based sensitivity analysis. Then, most suitable combinations of practices to achieve best performance with respect to each indicator were extracted, and trade-offs were analyzed. The procedure was applied to a region in western Switzerland, considering two different soil types to test the importance of local environmental constraints. Results show that the sensitivity of crop yield and soil erosion due to management is high, while nutrient leaching mostly depends on soil type. We found that the influence of most agricultural practices does not change significantly with CC; only irrigation becomes more relevant as a consequence of decreasing summer rainfall. Trade-offs were identified when focusing on best performances of each indicator separately, and these were amplified under CC. For adaptation to CC in the selected study region, conservation soil management and the use of cropped grasslands appear to be the most suitable options to avoid trade-offs. [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]

In this paper, we assess climate change impacts on an intensively managed grassland system at the Swiss Plateau using the process-based grassland model PROGRASS. Taking the CO fertilization into account, we find increasing yield levels (in the range of 10-24%) and sharp increases in production risks for an illustrative climate change scenario that suggests a marked increase in temperature and decrease in summer rainfall. Climate change-induced increases in the coefficients of variation of grassland yields are in the range of 21 and 50%. This finding underpins that additional risk management strategies are needed to cope with climate-change impacts on grassland production. The outputs from the grassland model are evaluated economically using certainty equivalents, i.e., accounting for mean quasi rents and production risks. To identify potential risk management strategies under current and future climatic conditions, we consider adjustments of production intensity and farm-level yield insurance. The impact of climate change on production intensities is found to be ambiguous: farmers' will increase intensity under unconstrained production conditions, but will decrease production intensity in the presence of a cross-compliance scheme. Our results also show that the considered insurance scheme is a powerful tool to manage climate risks in grassland production under current and future conditions because it can reduce the coefficients of variation of quasi rents by up to 50%. However, we find that direct payments tend to reduce farmers' incentives to use such insurance scheme. [ABSTRACT FROM AUTHOR]

Mechanisms controlling transient responses to elevated CO2 concentration and climate change in an unfertilized grassland on the Swiss Plateau were examined in light of simulations with PROGRASS, a process-based model of grass-clover interactions. Daily weather for a series of transient climate scenarios spanning the 21st century were developed for the study site with the help of the LARS-WG weather generator. Changes in the length of dry and wet spells, temperature, precipitation and solar radiation defining the scenarios were obtained from regional climate simulations carried out in the framework of the PRUDENCE project. Compared to 1961-1990, the latter indicated that for 2071-2100 there would be a noticeable increase in temperature (roughly 3°C in winter and 5°C in summer), a significant drop in summer precipitation (of the order of -30%) and a nearly 2-told increase in the length of dry spells. Assuming that clover is less drought-tolerant than grass, we examined whether clover abundance would decrease as a consequence of increasing heat and water stress, or be promoted on account of CO2 stimulation. Results indicated that: (1) at our site, climate change alone did not curtail biological N2 fixation to the point as to alter the composition of the sward but nevertheless entailed a decrease in grassland productivity; (2) increasing CO2 clearly promoted clover growth (via feedbacks on nitrogen acquisition) and grassland productivity; and (3) even with CO2 stimulation, the additional N input from symbiotic N2 fixation was not sufficient to substantially improve the mineral N status of the system and promote grass competitiveness. [ABSTRACT FROM AUTHOR]

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]

A simple model of yield was used along with climate scenarios to assess the impact of climate change on grain maize productivity and associated economic risk in Switzerland. In a first application, changes in the precipitation regime alone were shown to affect the distribution of yield considerably, with shifts not only in the mean but also in the standard deviation and the skewness. Production risk was found to respond more markedly to changes in the long-term mean than in the inter-annual variability of seasonal precipitation amounts. In a further application, yield projections were generated with respect to a full climate scenario, with the emission pathway as specified in the IPCC A2 scenario. Anticipation of the sowing date was found to reduce the negative impact of climate change on yield stability, but was not sufficient to ensure average productivity levels comparable to those observed at present. We argued that this was caused by the reduction in the duration of the growing season, which had a stronger impact than suggested by previous studies. Assuming no change in price relations, the results also revealed a strong increase in production risk with climate change, with more than a doubling in the probability of yield falling short of a critical threshold as compared to today’s situation. [ABSTRACT FROM AUTHOR]

Climate change is expected to affect both the average level and the variability of crop yields. In this modelling study, we quantified mean and inter-annual variability of grain yield for maize Zea mays L., winter wheat Triticum spp. L. and winter canola Brassica napus L. for climatic conditions corresponding to current and doubled atmospheric CO2 concentrations. Climate scenarios with and without taking into account changes in the inter-annual variability of climate were developed from the output of a regional climate model for the time window 2071 to 2100. Climate change effects on the mean yield of maize and canola were consistently negative, but a positive impact was simulated for mean yield of winter wheat for elevated CO2 concentration. The coefficient of yield variation increased in the scenarios for maize and canola, but decreased for wheat. Higher thermal time requirements increased mean yield and reduced yield variability for all crops. Shifts in the sowing dates had a beneficial impact on the yield of maize, but not on the yield of canola and wheat. It is concluded that in the Alpine region, the potential effect of climate change is crop-specific. However, the introduction of new cultivars may provide means by which to maintain or even increase current productivity levels for most of the crops. [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]

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 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]

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]

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]