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

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]

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]

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]

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]

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]

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]