Shifts in Apple Phenology under Climate Change in Switzerland and Implications for Exposure to Abiotic and Biotic Risks

It is expected that the development of plants and insects will be accelerated by global warming, resulting in an earlier occurrence of phenological stages in the future as compared to today. For plants, this could lead to a higher exposure to climatic shocks (late frosts in spring, and critical high temperatures in summer) and changes in the incidence of insect pests. Assessing the implications of such shifts in phenology is important to be able to devise, where necessary, means for reducing biotic and abiotic risks in plant production. In this contribution we present an analysis of the potential impacts of climate change on the risk of late frost and damages to fruits caused by critically high temperatures in apple orchards across Switzerland. We further discuss the possible effects of climate change on the appearance of the codling moth (Cydia pomonella L.), the key apple pest in many areas of the world. To conduct the analysis, we run carefully calibrated phenological models for different apple cultivars and the codling moth, feeding them with updated, transient climate change scenarios covering 1980-2100 developed for Switzerland on a 2 km x 2 km spatial-resolution grid. The climate scenarios represent three different emissions pathways allowing for consideration of a wide range of future climates. The results are discussed in a broader context by comparison with findings from other countries

Climate Change, Weather Insurance Design and Hedging Effectiveness

The insurance industry has so far relied on historical data to develop and price weather insurance contracts. In light of climate change, we examine the effects of this practice in terms of the hedging effectiveness and profitability of insurance contracts. We use simulated crop and weather data for today’s and future climatic conditions to derive optimal weather insurance contracts. We assess the hedging effectiveness and profits of adjusted contracts that are designed with data that accounts for the changing distribution of weather and yields due to climate change. We find that, with climate change, the benefits from hedging with adjusted contracts almost triple and expected profits increase by about 240%. Furthermore, we investigate the effect on risk reduction (for the insured) and profits (for the insurer) from hedging future weather risks with non-adjusted contracts, which are based on historical weather and yield data. When offering non-adjusted insurance contracts, we find that insurers either face substantial losses, or generate profits that are significantly smaller than profits from offering adjusted insurance products. Non-adjusted insurance contracts that create profits in excess of the profits from adjusted contracts cause at the same time negative hedging benefits for the insured. We observe that non-adjusted contracts exist that create simultaneously positive profits and hedging benefits, however at a much larger uncertainty compared to the corresponding adjusted contracts.weather insurance design, climate change, non-stationarity, hedging effectiveness

Quantification of climate change impacts on agricultural pests

Temperature is the dominant abiotic factor determining development rates, preproduction and migration of many insects. Climate change will therefore alter population abundance, geographical distribution and seasonal phenology of important agricultural pests. Scenarios concerning possible impacts of climate change on pests are necessary to identify adapted plant protection strategies and sustainable plant management options. Model-based studies are a valuable method for estimating the impacts of climate change on insect pests. Such projections are not easy to develop, because impact models often require a high temporal and spatial resolution of future climate data and long-term field observations are necessary for model calibration and validation

Risk management strategies to cope with climate change in grassland production: an illustrative case study for the Swiss plateau

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 CO2 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 schem

Modelling the impacts of seasonal drought on herbage growth under climate change

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Generic calibration of a simple model of diurnal temperature variations for spatial analysis of accumulated degree-days

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

Assessing Climate Change Impacts on Managed Grassland Production Using a Bio-Economic Modelling Approach

We develop a bio-economic model that combines the process based grassland simulation model PROGRASS with an economic decision model, which accounts for income risks and yield quality, to derive optimal nitrogen application rates in a grass-clover system in Switzerland. The model is applied to current as well as to future climate conditions. Though nitrogen increases yields, it also leads to a higher variance and more negative skewness of yields, i.e. is risk increasing. Accounting for farmers’ risk aversion thus reduces optimal nitrogen use. We find climate change, ceteris paribus, to lead to higher grassland yields but also to increase the variability of yields substantially. Optimal adaptation responses to climate change were found to be sensitive to the consideration of yield quality and the level of farmer’s risk aversion

Adaptation options under climate change for multifunctional agriculture: a simulation study for western Switzerland

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

Adapting agricultural land management to climate change: a regional multi-objective optimization approach

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 cropland

Widespread greening suggests increased dry-season plant water availability in the Rio Santa valley, Peruvian Andes

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