Search

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]

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]

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]

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

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

The draining and fertilization of peatlands for agriculture is globally an important source of the greenhouse gas nitrous oxide (N 2 O). Hitherto, the contribution of major sources to the N 2 O 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 N 2 O emission-reduction strategies. Here, we used machine learning techniques and global N 2 O observational data to generalize the distribution of N 2 O emissions from agriculturally managed peatlands, to distinguish the sources of N 2 O emissions, and to compare mitigation options. N 2 O 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 N 2 O-N year -1 were emitted, with 4.6 (3.7-5.7) kt N 2 O-N year -1 stemming from fertilizer N. The fertilizer-induced N 2 O emission factor ranged from 1.5% to 3.2%. Reducing the current fertilizer input by 20% could achieve a 10% N 2 O emission reduction for croplands but only 3% for grasslands. Rewetting 1.9 Mha cropland and 0.26 Mha grassland would achieve the same N 2 O emission reductions. Our results suggest that N 2 O mitigation strategies for managed peatlands should be considered separately across land-use types and climatic zones. For croplands, particularly in the tropics, relevant N 2 O 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 N 2 O and contributing to meeting climate goals., (Global Change Biology© 2024 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

Urine patches from grazing cattle are hotspots of nitrous oxide (N 2 O) emissions. The default IPCC emission factor for urine patches (EF urine ) 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 N 2 O emissions and corresponding EF urine 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 EF urine value (p > 0.05). EF urine 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. R 2  = 0.60) or average WFPS 30 days after urine application using a linear model (Adj. R 2  = 0.45). The derived precipitation model was used to simulate EF urine weekly over the last 20 years showing no significant differences between the seasons of a year. The resulting overall average EF urine was 0.67%. More field studies are needed across sites/regions differing in climate and soil properties to implement a country-specific EF 3 for Switzerland and to improve the quantification of N 2 O emissions at the national scales., Competing Interests: Conflict of interestThe authors have no competing interests to declare that are relevant to the content of this article., (© The Author(s) 2023.)

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.

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.