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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.

International audience ; 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 ...

International audience ; 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.

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 (R2>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 (R2<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.

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 ...

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.

International audience ; 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 (R2>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 (R2<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.

International audience ; 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 (R2>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 (R2<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.

International audience ; This study assesses crop residues in the EU from major crops using empirical models to predict crop residues from yield statistics; furthermore it analyses the inter‐annual variability of those estimates over the period 1998‐2015, identifying its main drivers across Europe. The models were constructed based on an exhaustive collection of experimental data from scientific papers for the crops: wheat, barley, rye, oats, triticale, rice, maize, sorghum, rapeseed, sunflower, soybean, potato and sugarbeet. We discuss the assumptions on the relationship between yield and the harvest index, adopted by previous studies, to interpret the experimental data, quantify the uncertainties of these models, and establish the premises to implement them at regional scale –i.e NUTS level 3– within the EU. To cope this, we created a consolidated sub‐national statistical data along with an algorithm able to aggregate (figures are provided at country level) and disaggregate (production at 25 km grid is provided as supplementary material) estimates. The total lignocellulosic biomass production in the EU28 over the review period, according to our models, is 419 Mt, from which wheat is the major contributor (155 Mt). Our results show that maize and rapeseed are the two crops with the highest residue yield, respectively 8.9 and 8.6 t ha‐1. The spatial analysis revealed that these three crops, which, according to our results, are feedstocks highly suitable a priori for second generation biofuels in the EU and are unevenly distributed across Europe. Weather fluctuation was identified as the major driver in residue production from cereals, while, in the case of starch crops and oilseeds – which are predominant in northern Europe – corresponded to the marked production trend likely influenced by the agricultural policies and agro‐management over the review period. Additionally, our study highlights the limitation of such empirical models in quantifying lignocellulosic biomass in the EU.

International audience ; This study assesses crop residues in the EU from major crops using empirical models to predict crop residues from yield statistics; furthermore it analyses the inter‐annual variability of those estimates over the period 1998‐2015, identifying its main drivers across Europe. The models were constructed based on an exhaustive collection of experimental data from scientific papers for the crops: wheat, barley, rye, oats, triticale, rice, maize, sorghum, rapeseed, sunflower, soybean, potato and sugarbeet. We discuss the assumptions on the relationship between yield and the harvest index, adopted by previous studies, to interpret the experimental data, quantify the uncertainties of these models, and establish the premises to implement them at regional scale –i.e NUTS level 3– within the EU. To cope this, we created a consolidated sub‐national statistical data along with an algorithm able to aggregate (figures are provided at country level) and disaggregate (production at 25 km grid is provided as supplementary material) estimates. The total lignocellulosic biomass production in the EU28 over the review period, according to our models, is 419 Mt, from which wheat is the major contributor (155 Mt). Our results show that maize and rapeseed are the two crops with the highest residue yield, respectively 8.9 and 8.6 t ha‐1. The spatial analysis revealed that these three crops, which, according to our results, are feedstocks highly suitable a priori for second generation biofuels in the EU and are unevenly distributed across Europe. Weather fluctuation was identified as the major driver in residue production from cereals, while, in the case of starch crops and oilseeds – which are predominant in northern Europe – corresponded to the marked production trend likely influenced by the agricultural policies and agro‐management over the review period. Additionally, our study highlights the limitation of such empirical models in quantifying lignocellulosic biomass in the EU.

This study assesses crop residues in the EU from major crops using empirical models to predict crop residues from yield statistics; furthermore it analyses the inter‐annual variability of those estimates over the period 1998‐2015, identifying its main drivers across Europe. The models were constructed based on an exhaustive collection of experimental data from scientific papers for the crops: wheat, barley, rye, oats, triticale, rice, maize, sorghum, rapeseed, sunflower, soybean, potato and sugarbeet. We discuss the assumptions on the relationship between yield and the harvest index, adopted by previous studies, to interpret the experimental data, quantify the uncertainties of these models, and establish the premises to implement them at regional scale –i.e NUTS level 3– within the EU. To cope this, we created a consolidated sub‐national statistical data along with an algorithm able to aggregate (figures are provided at country level) and disaggregate (production at 25 km grid is provided as supplementary material) estimates. The total lignocellulosic biomass production in the EU28 over the review period, according to our models, is 419 Mt, from which wheat is the major contributor (155 Mt). Our results show that maize and rapeseed are the two crops with the highest residue yield, respectively 8.9 and 8.6 t ha‐1. The spatial analysis revealed that these three crops, which, according to our results, are feedstocks highly suitable a priori for second generation biofuels in the EU and are unevenly distributed across Europe. Weather fluctuation was identified as the major driver in residue production from cereals, while, in the case of starch crops and oilseeds – which are predominant in northern Europe – corresponded to the marked production trend likely influenced by the agricultural policies and agro‐management over the review period. Additionally, our study highlights the limitation of such empirical models in quantifying lignocellulosic biomass in the EU.

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.