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Hail represents a major threat to agriculture in Switzerland, 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 damage is challenging. Here, we build and systematically assess an open-source 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. In contrast, for grapevine, decreasing model resolution below 1 km 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 twice as often as observed (high frequency bias and false alarm ratio), but it 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, as measured by the Heidke skill score (HSS), remains largely unchanged (0.41–0.44). We argue that, ultimately, the best threshold therefore depends on the relative costs of a false alarm versus a missed event. Finally, the frequency of false alarms is substantially reduced and skill is improved (HSS = 0.54) 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 skilful and is best done at 8 km resolution for field crops and 1 km for grapevine. [ABSTRACT FROM AUTHOR]

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]

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]

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 tradeoffs. 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]

Urine patches from grazing cattle are hotspots of nitrous oxide (N2O) emissions. The default IPCC emission factor for urine patches (EFurine) is 0.77% for wet climates and 0.32% for dry climates. However, literature reports a considerable range of cattle urine EF values and urine characteristics used in experimental studies, revealing contrary results on the effects of urine patch characteristics and seasonal pattern. Therefore, we examined N2O emissions and corresponding EFurine values in relation to urine patch characteristics (urine N concentration, urine volume, patch area, urine composition) and environmental drivers (precipitation, water filled pore space, soil temperature). Ten artificial urine application experiments were performed from July 2020 to June 2022 on a pasture located in Eastern Switzerland. Urine N concentration, patch area, volume and urine N composition showed no significant effects on the EFurine value (p > 0.05). EFurine varied, however, strongly over time (0.17–2.05%). A large part of the variation could be predicted either by cumulative precipitation 20 days after urine application using a second order polynomial model (Adj. R2 = 0.60) or average WFPS 30 days after urine application using a linear model (Adj. R2 = 0.45). The derived precipitation model was used to simulate EFurine weekly over the last 20 years showing no significant differences between the seasons of a year. The resulting overall average EFurine was 0.67%. More field studies are needed across sites/regions differing in climate and soil properties to implement a country-specific EF3 for Switzerland and to improve the quantification of N2O emissions at the national scales. [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]

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]

A major challenge of agriculture is to improve the sustainability of food production systems in order to provide enough food for a growing human population. Pests and pathogens cause vast yield losses, while crop protection practices raise environmental and human health concerns. Decision support systems provide detailed information on optimal timing and necessity of crop protection interventions, but are often based on phenology models that are time‐, cost‐, and labor‐intensive in development. Here, we aim to develop a data‐driven approach for pest damage forecasting, relying on big data and deep learning algorithms. We present a framework for the development of deep neural networks for pest and pathogen damage classification and show their potential for predicting the phenology of damages. As a case study, we investigate the phenology of the pear leaf blister moth (Leucoptera malifoliella, Costa). We employ a set of 52,322 pictures taken during a period of 19 weeks and establish deep neural networks to categorize the images into six main damage classes. Classification tools achieved good performance scores overall, with differences between the classes indicating that the performance of deep neural networks depends on the similarity to other damages and the number of training images. The reconstructed damage phenology of the pear leaf blister moth matches mine counts in the field. We further develop statistical models to reconstruct the phenology of damages with meteorological data and find good agreement with degree‐day models. Hence, our study indicates a yet underexploited potential for data‐driven approaches to enhance the versatility and cost efficiency of plant pest and disease forecasting. [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]

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]

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]

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]

Purpose Although the importance of climate change is generally acknowledged, its impacts are often not taken into account explicitly when planning development projects. This being due to limited resources, among others, this paper aims to propose a simple and low-cost approach to assess the viability of human activities under climate change. Design/methodology/approach Many human activities are feasible only within a narrow range of climatic conditions. Comparing such "climate corridors" with future climate projections provides an intuitive yet quantitative means for assessing needs for, and the viability of, adaptation activities under climate change. Findings The approach was tested within development projects in Pakistan, Peru and Tajikistan. The approach was shown to work well for forestry and agriculture, indicating positive/negative prospects for wheat in two districts in Pakistan, temperature constraints for maize in Peru and widening elevation ranges for walnut trees in Tajikistan. Practical implications Climate corridor analyses feed into the preparation of Local Adaptation Plans of Action in Pakistan. Originality/value The simplicity and robustness of climate corridor analysis allow for efficient analysis and communication of climate change impacts. It works when data availability is limited, but it can as well accommodate a wide range of complexities. It has proven to be an effective vehicle for mainstreaming climate change into adaptation planning. [ABSTRACT FROM AUTHOR]

Large variability in N2O emissions from managed grasslands may occur because most emissions originate in surface litter or near-surface soil where variability in soil water content (θ) and temperature (Ts) is greatest. To determine whether temporal variability in θ and Ts of surface litter and near-surface soil could explain this in N2O emissions, a simulation experiment was conducted with ecosys, a comprehensive mathematical model of terrestrial ecosystems in which processes governing N2O emissions were represented at high temporal and spatial resolution. Model performance was verified by comparing N2O emissions, CO2 and energy exchange, and θ and Ts modelled by ecosys with those measured by automated chambers, eddy covariance (EC) and soil sensors on an hourly timescale during several emission events from 2004 to 2009 in an intensively managed pasture at Oensingen, Switzerland. Both modelled and measured events were induced by precipitation following harvesting and subsequent fertilizing or manuring. These events were brief (2-5 days) with maximum N2O effluxes that varied from < 1 mgNm-2h-1 in early spring and autumn to > 3 mgNm-2h-1 in summer. Only very small emissions were modelled or measured outside these events. In the model, emissions were generated almost entirely in surface litter or near-surface (0-2 cm) soil, at rates driven by N availability with fertilization vs. N uptake with grassland regrowth and by O2 supply controlled by litter and soil wetting relative to O2 demand from microbial respiration. In the model, NOx availability relative to O2 limitation governed both the reduction of more oxidized electron acceptors to N2O and the reduction of N2O to N2, so that the magnitude of N2O emissions was not simply related to surface and near-surface θ and Ts. Modelled N2O emissions were found to be sensitive to defoliation intensity and timing which controlled plant N uptake and soil θ and Ts prior to and during emission events. Reducing leaf area index (LAI) remaining after defoliation to half that under current practice and delaying harvesting by 5 days raised modelled N2O emissions by as much as 80% during subsequent events and by an average of 43% annually. Modelled N2O emissions were also found to be sensitive to surface soil properties. Increasing near-surface bulk density by 10% raised N2O emissions by as much as 100% during emission events and by an average of 23% annually. Relatively small spatial variation in management practices and soil surface properties could therefore cause the large spatial variation in N2O emissions commonly found in field studies. The global warming potential from annual N2O emissions in this intensively managed grassland largely offset those from net C uptake in both modelled and field experiments. However, model results indicated that this offset could be adversely affected by suboptimal land management and soil properties. [ABSTRACT FROM AUTHOR]

Smallholder agriculture in the Central Andes of Peru is based to large extent on rainfed cropping systems, is exposed to climatic risks and is expected to respond sensitively to increasing temperatures and shifts in the precipitation regime. Here, we examine the potential implications of early twenty-first century climate change scenarios for the cultivation of potato, maize, wheat, barley and broad bean, five annual crops that account for 50 % of the cultivated area in the Department of Cusco and Apurímac and provide the dietary backbone for a large share of the local population. The scenarios disclose a regionally coherent increase in temperature of the order of 1 °C but overall only moderate changes in growing season precipitation by 2030. A simple crop model is used to assess the effects of these changes on crop phenology and development. The results show earlier harvest dates, shorter cropping seasons and, in a few cases, a slightly higher risk of planting failure in the near future. This suggests that a better understanding of changes in the precipitation regime at the onset of the cropping season is required to evaluate short-term needs and possibilities for adaptation. However, as the scenarios are highly uncertain, these conclusions should be verified. [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]

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]

In several regions of the world, climate change is expected to have severe impacts on agricultural systems. Changes in land management are one way to adapt to future climatic conditions, including land-use changes and local adjustments of agricultural practices. In previous studies, options for adaptation have mostly been explored by testing alternative scenarios. Systematic explorations of land management possibilities using optimization approaches were so far mainly restricted to studies of land and resource management under constant climatic conditions. In this study, we bridge this gap and exploit the benefits of multi-objective regional optimization for identifying optimum land management adaptations to climate change. We design a multi-objective optimization routine that integrates a generic crop model and considers two climate scenarios for 2050 in a meso-scale catchment on the Swiss Central Plateau with already limited water resources. The results indicate that adaptation will be necessary in the study area to cope with a decrease in productivity by 0–10 %, an increase in soil loss by 25–35 %, and an increase in N-leaching by 30–45 %. Adaptation options identified here exhibit conflicts between productivity and environmental goals, but compromises are possible. Necessary management changes include (i) adjustments of crop shares, i.e. increasing the proportion of early harvested winter cereals at the expense of irrigated spring crops, (ii) widespread use of reduced tillage, (iii) allocation of irrigated areas to soils with low water-retention capacity at lower elevations, and (iv) conversion of some pre-alpine grasslands to croplands. [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]

This paper presents a simple approach for estimating the spatial and temporal variability of seasonal net irrigation water requirement (IWR) at the catchment scale, based on gridded land use, soil and daily weather data at 500 × 500 m resolution. In this approach, IWR is expressed as a bounded, linear function of the atmospheric water budget, whereby the latter is defined as the difference between seasonal precipitation and reference evapotranspiration. To account for the effects of soil and crop properties on the soil water balance, the coefficients of the linear relation are expressed as a function of the soil water holding capacity and the so-called crop coefficient. The 12 parameters defining the relation were estimated with good coefficients of determination from a systematic analysis of simulations performed at daily time step with a FAO-type point-scale model for five climatically contrasted sites around the River Rhone and for combinations of six crop and ten soil types. The simple scheme was found to reproduce well results obtained with the daily model at six additional verification sites. We applied the simple scheme to the assessment of irrigation requirements in the whole Swiss Rhone catchment. The results suggest seasonal requirements of 32 × 106 m³ per year on average over 1981-2009, half of which at altitudes above 1500 m. They also disclose a positive trend in the intensity of extreme events over the study period, with an estimated total IWR of 55 × 106 m³ in 2009, and indicate a 45% increase in water demand of grasslands during the 2003 European heat wave in the driest area of the studied catchment. In view of its simplicity, the approach can be extended to other applications, including assessments of the impacts of climate and land-use change. [ABSTRACT FROM AUTHOR]

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]

Insurers have relied on historical data to design weather insurance contracts. In light of climate change, we examine the effects of this practice on the hedging effectiveness and profitability of insurance contracts. Using synthetic crop and weather data for today's and future climatic conditions we derive adjusted weather insurance contracts that account for shifts in the distribution of weather and yields. In our scenario, hedging benefits from adjusted contracts almost triple and expected profits increase by about 240 per cent. We further investigate the effect on risk reduction (for the insured) and profits (for the insurer) from hedging future weather risk with non-adjusted contracts based on historical data. In this case, insurers generate profits that are significantly smaller than for adjusted contracts, or even face substantial losses. Moreover, non-adjusted contracts that create higher profits than the adjusted counterparts cause negative hedging benefits for the insured. [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]

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]

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]

The Mesoscale Alpine Programme’s Riviera project investigated the turbulence structure and related exchange processes in an Alpine valley by combining a detailed experimental campaign with high-resolution numerical modelling. The present contribution reviews published material on the Riviera Valley’s boundary layer structure and discusses new material on the near-surface turbulence structure. The general conclusion of the project is that despite the large spatial variability of turbulence characteristics and the crucial influence of topography at all scales, the physical processes can accurately be understood and modelled. Nevertheless, many of the “text book characteristics” like the interaction between the valley and slope wind systems or the erosion of the nocturnal valley inversion need reconsideration, at least for small non-ideal valleys like the Riviera Valley. The project has identified new areas of research such as post-processing methods for turbulence variables in complex terrain and new approaches for the surface energy balance when advection is non-negligible. The exchange of moisture and heat between the valley atmosphere and the free troposphere is dominated by local “secondary” circulations due to the curvature of the valley axis. Because many curved valleys exist, and operational models still have rather poor resolution, parameterization of these processes may be required. [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]

We improved a process-oriented biogeochemical model of carbon and nitrogen cycling in grasslands and tested it against in situ measurements of biomass and CO2 and CH4 fluxes at five European grassland sites. The new version of the model (PASIM) calculates the growth and senescence of aboveground vegetation biomass accounting for sporadic removals when the grassland is cut and for continuous removals when it is grazed. Limitations induced by high leaf area index (LAI), soil water deficits and aging of leaves are also included. We added to this a simple empirical formulation to account for the detrimental impact on vegetation of trampling and excreta by grazing animals. Finally, a more realistic methane emission module than is currently used was introduced on the basis of the quality of the animals' diet. Evaluation of this improved version of PASIM is performed at (1) Laqueuille, France, on grassland continuously grazed by cattle with two plots of intensive and extensive grazing intensities, (2) Oensingen, Switzerland, on cut grassland with two fertilized and nonfertilized plots, and (3) Carlow, Ireland, on grassland that is both cut and grazed by cattle during the growing season. In addition, we compared the modeled animal CH4 emissions with in situ measurements on cattle for two grazing intensities at the grazed grassland site of Laqueuille. Altogether, when all improvements to the PASIM model are included, we found that the new parameterizations resulted into a better fit to the observed seasonal cycle of biomass and of measured CO2 and CH4 fluxes. However, the large uncertainties in measurements of biomass and LAI make simulation of biomass dynamics difficult to make. Also simulations for cut grassland are better than for grazed swards. This work paves the way for simulating greenhouse gas fluxes over grasslands in a spatially explicit manner, in order to quantify and understand the past, present and future role of grasslands in the greenhouse gas budget of the European continent. [ABSTRACT FROM AUTHOR]

We apply a simulation model in order to quantify, the patterns of carbon and nitrogen cycling within European grasslands. We map the fluxes of CO2, N2O and CH4 exchanged with the atmosphere as controlled by climate and management conditions. We distinguish between grazing and cutting practice. Because geo-referenced management information for grasslands does not exist at the scale of Europe, we develop a new and robust set of rules defining some management variables. We then perform realistic simulations in term of N fertilization using a national level data set. The model results at European scale are compared with agricultural statistics (yield, animal stocks), which shows that our very simple management calculation is reasonably realistic. We also compare the simulated seasonal cycle of grassland phenology as calculated by PASIM with remote sensing observations from the EOS-TERRA-MODIS satellite, which shows a good general agreement. Emission factors for soil N2O and grazing animals CH4 emissions are diagnosed from the model runs and shown to be comparable to those of previous experimental surveys. We investigate impact of N fertilization on NPP and C storage potential, N2O emissions by soils and CH4 emissions by ruminants. We conclude that, for different time horizon, CH4 and N2O sources are lower than the potential sink of CO2, on a carbon equivalent basis. This result is independent of fertilization intensity but assumes that the current soil C stocks are below the long-term equilibrium values. [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]

The interannual variability of summer mean soil moisture conditions in Switzerland during the 20th century was studied using a stochastic soil moisture model. The model was validated against high-quality observations of the water balance at a representative location. The summer mean degree of saturation was found to fluctuate around the point of stomatal closure at low elevations to the north of the Alps, clearly above this threshold at higher elevations and to the south of the Alps, and systematically below this limit in the inner-Alpine domain. In addition, the results indicated that the largest interannual variability occurs at low elevations. A linearization of the model revealed that interannual variations of summer mean soil moisture are driven in order of importance by variations in the rate of storm arrivals, joint variations in the rate of storm arrivals and maximum evapotranspiration, and variations in mean rainfall depth. This was explained in terms of the sensitivities of seasonal mean soil moisture with respect to the driving parameters. [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]