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Temperature and precipitation are the most important factors responsible for agricultural productivity variations. In 2018 spring/summer growing season, Europe experienced concurrent anomalies of both. Drought conditions in central and northern Europe caused yield reductions up to 50% for the main crops, yet wet conditions in southern Europe saw yield gains up to 34%, both with respect to the previous 5‐year mean. Based on the analysis of documentary and natural proxy‐based seasonal paleoclimate reconstructions for the past half millennium, we show that the 2018 combination of climatic anomalies in Europe was unique. The water seesaw, a marked dipole of negative water anomalies in central Europe and positive ones in southern Europe, distinguished 2018 from the five previous similar droughts since 1976. Model simulations reproduce the 2018 European water seesaw in only 4 years out of 875 years in historical runs and projections. Future projections under the RCP8.5 scenario show that 2018‐like temperature and rainfall conditions, favorable to crop growth, will occur less frequent in southern Europe. In contrast, in central Europe high‐end emission scenario climate projections show that droughts as intense as 2018 could become a common occurrence as early as 2043. While integrated European and global agricultural markets limited agro‐economic shocks caused by 2018's extremes, there is an urgent need for adaptation strategies for European agriculture to consider futures without the benefits of any water seesaw. Key Points: Unique concurrent spring and summer climatic anomalies affected Europe in 20182018‐like droughts could become a common occurrence as early as 2043Climate change adaptation strategies for agriculture in Europe cannot count on recurrent water seesaws [ABSTRACT FROM AUTHOR]

Abstract This paper presents an evaluation framework followed to identify cost-efficient alternatives to extend the MARS Crop Yield Forecasting System (MCYFS), run by the European Commission Joint Research Centre since 1992, to other main producing areas of the world: Eastern European Neighbourhood, Asia, Australia, South America and North America. These new systems would follow the principles and components of the MCYFS Europe: a meteorological data infrastructure, a remote sensing data infrastructure, a crop modelling platform, statistical tools, a team of analysts and a crop area estimation component. The framework designed evaluates the performance of the possible MCYFS-like system realizations against six defined objectives and their costs. Possible monitoring systems are based on a combination of different technical solutions for each of the MCYFS components, and are evaluated through an automatic algorithm that calculates the expected system performance –relying on a priori expert judgement–, the costs, and possible risks to construct some technical solutions, to finally identify the cost-efficient ones. A baseline system, achieving the minimum required performance, was identified as the most efficient starting point for the MCYFS extension in all the geographical areas. Such system would be built upon: (i) near real-time reanalysis meteorological products; (ii) remote sensing data from low-resolution (~1 km) platforms with a long-term product archive; (iii) crop models based on crop-specific model calibration from experimental data published in scientific literature; (iv) statistical methods based on trend and regression analysis applied to national level; (v) a team of analysts with specific technical profiles (on meteorology, remote sensing, and agronomy); and (vi) digital classification of very high resolution imagery supported by non-expensive ground surveys for area estimation. In countries where accessibility to local data and resources is high the baseline system can be upgraded enhancing some of the components: sub-national statistical analysis with additional statistical methods like multiple regression or scenario analysis; recruitment of experts on local agricultural conditions in the team of analysts; local calibration of crop models with experimental data; and exploiting high and low resolution biophysical products from remote sensing for crop monitoring. Highlights • We present a framework to identify cost-efficient solutions for a crop yield forecasting system in the main producing areas. • An automatic algorithm evaluates the expected performance, costs, and possible risks to construct some technical solutions. • The analysis identified a baseline system common to all the geographical areas reaching the minimum required performance. • A roadmap to upgrade the baseline system indicates those solutions improving the system performance at lower costs. [ABSTRACT FROM AUTHOR]

Long timeseries of Earth observation data for the characterization of agricultural crops across large scales are of high interest to crop modelers, scientists, and decision makers in the fields of agricultural and environmental policy as well as crop monitoring and food security. They are particularly important for regression-based crop monitoring systems that rely on historic information. The major challenge lies in identifying pixels from satellite imagery that represent pure enough crop signals. Here, we present a data-driven semi-automatic approach to identify pure pixels of two crop groups (i.e., winter and spring crops and summer crops) based on a MODIS–NDVI timeseries. We applied this method to the European Union at a 250 m spatial resolution. Pre-processed and smoothed, daily normalized difference vegetation index (NDVI) data (2001–2017) were used to first extract the phenological data. To account for regional characteristics (varying climate, agro-management, etc.), these data were clustered by administrative units and by year using a Gaussian mixture model. The number of clusters was pre-defined using data from regional agricultural acreage statistics. After automatic labelling, clusters were filtered based on agronomic knowledge and phenological information extracted from the same timeseries. The resulting pure pixels were validated with two different datasets, one based on high-resolution Sentinel-2 data (5 sites, 2 years) and one based on a regional crop map (1 site, 7 years). For the winter and spring crop class, pixel purity amounted to 93% using the first validation dataset and to 73% using the second one, averaged over the different years. For summer crops, the respective values were 61% (91% without one critical validation site) and 72%. The phenological analyses revealed a clear trend towards an earlier NDVI peak (approximately −0.28 days/year) for winter and spring crops across Europe. We expect that this dataset will be useful for various applications, from crop model calibration to operational crop monitoring and yield forecasting. [ABSTRACT FROM AUTHOR]

Integrative and collaborative process adopted to design the EU Bioeconomy Monitoring System • An EU-wide internationally coherent system to monitor the bioeconomy is described. • The system will provide information on the sustainability of the bioeconomy. • A multi-dimensional and comprehensive framework is required. • EU and international experts have contributed to the design of the system. • The system will be published through the EC's Knowledge Centre for Bioeconomy. The EU Bioeconomy Strategy, updated in 2018, in its Action Plan pledges an EU-wide, internationally coherent monitoring system to track economic, environmental and social progress towards a sustainable bioeconomy. This paper presents the approach taken by the European Commission's (EC) Joint Research Centre (JRC) to develop such a system. To accomplish this, we capitalise on (1) the experiences of existing indicator frameworks; (2) stakeholder knowledge and expectations; and (3) national experiences and expertise. This approach is taken to ensure coherence with other bioeconomy-related European monitoring frameworks, the usefulness for decision-making and consistency with national and international initiatives to monitor the bioeconomy. We develop a conceptual framework, based on the definition of a sustainable bioeconomy as stated in the Strategy, for a holistic analysis of the trends in the bioeconomy sectors, following the three pillars of sustainability (economy, society and environment). From this conceptual framework, we derive an implementation framework that aims to highlight the synergies and trade-offs across the five objectives of the Bioeconomy Strategy in a coherent way. The EU Bioeconomy Monitoring System will be publicly available on the web platform of the EC Knowledge Centre for Bioeconomy. [ABSTRACT FROM AUTHOR]

Abstract The MARS-Crop Yield Forecasting System (M-CYFS) is used since 1993 to forecast the yields of all major crops in the European Union (EU) based on gridded runs of the WOFOST crop model. Using 28 years of observation, from 1988 to 2015, we quantified the variability in crop yield reported by all 28 EU Member States (MS) that can be explained by each individual WOFOST crop model based predictors and a few simple meteorological variables. A linear regression is used as a screening tool to quantify the relationship between each predictor and the yield residuals from the trend throughout the crop cycle for 168 country/crop combinations, assuming the yield residuals from the trend depend on the inter-annual climate variability. The results are plotted and analyzed at different level: every 10 days for each country crop/combination and each predictor; synthetized every 10 days for each country/crop combination keeping the predictor showing the best relationship with the yield residuals; finally, the best predictor found for each MS during the entire growing season is used to evaluate the ability of the model to estimate yield variability of each crop at European scale. While 61% of the grain maize (Zea mays L.) yield variability can be anticipated 80 days before harvest with the simulated water limited biomass for countries where rainfed maize prevails, 41% of the soft wheat (Triticum aestivum L.) yield variability can be reproduced a month before harvest, the best estimates being obtained where wheat is predominantly exposed to water stress. For the other crops analyzed, the results are also found to be reliable for crops predominantly exposed to water stress and becoming unreliable in agricultural systems exposed to an oceanic climate with a high level of inputs. The agro-meteorological processes related to an excess of water (nitrogen losses, diseases, anoxia, harvest conditions) would need to be disentangled and better integrated into the crop modeling system to improve the predictors. The monthly cumulated meteorological predictors are performing only slightly worse than the crop model predictors and help to characterize the main processes responsible for the yield variability. Nevertheless, the predictive capacity of the meteorological predictors is spatially and temporally incoherent and differs according to the crop phenology. In comparison, the M-CYFS crop model predictors are more consistent since the predictors summarize the succession of agro-meteorological conditions determining the yield throughout the entire growing season. Highlights • Crop model and meteorological variables are compared to yield variability. • Crop model simulations allow to anticipate yields of crops exposed to drought. • Impact of warm temperatures on yield during grain filling is reflected by WOFOST. • Impacts of water excess on crop growth are not reproduced by the crop model. • The simulated biomass of winter cereals at anthesis is poorly related to the yields. [ABSTRACT FROM AUTHOR]

This study addresses the role of satellite Earth Observation (EO) indicators within an operational crop yield forecasting system for the European Union (EU) and neighbouring countries, by exploring the correlation between official yield statistics and indicators derived from fAPAR time-series at sub-national level for the period 1999–2012, and by identifying possible differences across agro-climatic conditions in Europe. A significant correlation between fAPAR and official yields ( R 2 > 0.6) was found in water-limited yield agro-climatic conditions (e.g. the Black Sea region and the Mediterranean basin) for all three crops studied. In regions where crops experience frequent water stress, most of the yield inter-annual variability is explained by substantial changes in leaf area from one year to another, and can be well captured by regional fAPAR time-series. By contrast, in regions characterized by high yields (e.g. northern Europe) – where water constraints are generally not frequent and, therefore, fAPAR inter-annual variability is low – the correlation between fAPAR and yield is weaker ( R 2 < 0.5) as yield variations tend to be explained by multiple factors other than green leaf area. These results confirm the reliability of EO time-series for operational crop yield forecasting at regional level, but also suggest that additional meteorological variables (temperature, precipitation, evapotranspiration) need to be taken into account to interpret EO products meaningfully. Moreover, specific issues related to the spatial resolution of the EO-products, and the absence of dynamic crop masks, currently impede access to crop-specific time-series in the fragmented agricultural landscapes of Europe, and restrict the use of 1-km biophysical products to major crops. [ABSTRACT FROM AUTHOR]

Europe experienced exceptional climate conditions in spring and summer 2018. The extreme drought in central and northern Europe heavily affected key socio-economic sectors. While, anomalous wet conditions in southern Europe favoured sectors such as agriculture triggering crop yield gains that partially offset the losses in central and northern Europe. Paleoclimate reconstructions show the uniqueness of the 2018 concurrent climate anomalies in central Europe, although five similar drought events were observed in the last decades. Future climate projections, under the high-end emission scenario RCP8.5, show that drought events similar to the one of 2018 could become the norm in central Europe, while favourable conditions in southern Europe will become extremely rare. [ABSTRACT FROM AUTHOR]