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.

In the present study an exploratory analysis of the relationship between regional fAPAR time-series and yield inter-annual variability in the period 1999-2012 has been conducted, evaluating its reliability for operational crop yield forecasting activities. Overall, the results indicated strong correlation between fAPAR and official yields in water-limited yield agro-climatic conditions for all the three crops studied. Where crops are subjected to frequent water stress, e.g. the Mediterranean basin, the Black Sea area, most of the yield inter-annual variability is explained by leaf area formation along the growing season, and can be well captured by regional fAPAR time-series. By contrast, in the regions of high yields, where generally fAPAR inter-annual variability is low as consequence of rainfall regimes close to potential evapotranspiration (or intensive irrigation in the specific case of grain maize) the correspondence between fAPAR and yield anomalies is weak. Yield variations are explained by other factors than leaf area formation (e.g. excess of rain during harvest), and therefore extreme yields in those regions are not necessarily corresponding to large fAPAR anomalies. The use of remote sensing in operational crop yield forecasting at regional scale therefore requires the consideration of additional, independent indicators: weather parameters, crop models, etc. EO biophysical products provide, indeed, a synoptic view of the vegetation status and leaf area formation along the growing season. Nevertheless, the results of this study indicates that their interpretation may be sometimes misleading when used alone to explain extreme yields.JRC.H.4-Monitoring Agricultural Resource

In most landscapes, accurate crop yield forecasting depends on a quantitative understanding of the relation between past weather, management and crop yield variability. We evaluated and improved the regression-based crop yield forecasting methodology currently employed in the MARS-Crop Yield Forecasting System (M-CYFS) for maize in Hungary. We quantified the effect of: 1) different statistical trends; 2) different crop growth simulation model outputs providing weekly predictors; 3) yield prediction lead times; and 4) spatial aggregation, on the forecast accuracy as evaluated against statistical yield from 1993-2012. The LOESS (locally weighted scatterplot smoothing) trend provided the lowest root mean square error (RMSE) in describing the yield time-series compared to the quadratic and linear trend. Using the WOFOST crop model-based predictors to explain the yield residuals derived with each of the three trends, the lowest RMSEs were obtained with the Water Limited Leaf Area Index (WLLAI) and Water Limited Above Ground Biomass (WLB) predictors in combination with the LOESS trend. The LOESS trend was used to evaluate the effect of spatial aggregating subnational yield forecasts. During the first half of the crop cycle there are only marginal differences between the NUTS0 (national), NUTS1 (supra-regional), NUTS2 (regional), and NUTS3 (sub-regional) level. However, the NUTS0 forecast had a slightly lower accuracy from the start of flowering and onwards, indicating the possible benefit of maintaining spatial detail when aggregating data. The RMSE of the forecasts started to decrease in week 24 and 25. Even though the relative soil moisture decreased earliest, the best performing yield forecasts were associated with lead times of about 5-8 weeks before harvest and were obtained with the WLLAI and WLB as predictors. The best forecasts were associated with the critical phenological phases of flowering and grain-filling respectively occurring between week 27 to 30 and week 31 to 35. The best performing national forecast was based on NUTS1 level forecasts with an r2 and a RMSE of respectively 0.8565 and 425.9 kg·ha-1 using WLLAI as predictor. Finally, we compared the regression-based forecasts with operational forecasts performed by the Ministry of Agriculture of Hungary and the JRC-MARS forecasts from 2007 to 2012.JRC.H.4-Monitoring Agricultural Resource

India is the second-most populous country and the second-most important producer of rice of the world. Most Indian rice production depends on monsoon timing and dynamics. In 2002, the lowest monsoon precipitation of the last 130+ years was observed. It coincided with the worst rice production anomaly recorded by FAOSTAT from 1961 to 2014. In that year, freshwater limitation was blamed as responsible for the yield losses in the southeastern coastal regions. Given the important implication for local food security and international market stability, we here investigate the specific mechanisms behind the effects of this extreme meteorological drought on rice yield at the national and regional levels. To this purpose, we integrate output from the hydrological model, surface, and satellite observations for the different rice cropping cycles into state-of-the-art and novel climate indicators. In particular, we adopt the standardized precipitation evapotranspiration index (SPEI) as an indicator of drought due to the local surface water balance anomalies (i.e., precipitation and evapotranspiration). We propose a new indicator of the renewable surface freshwater availability due to non-local sources, i.e., the standardized river discharge index (SDI) based on the anomalies of modelled river discharge data. We compare these indicators to the soil moisture observations retrieved from satellites. We link all diagnostics to the recorded yields at the national and regional level, quantifying the long-term correlations and the best match of the 2002 anomaly. Our findings highlight the need for integrating non-local surface freshwater dynamics with local rainfall variability to determine the soil moisture conditions in rice fields for yields assessment, modeling, and forecasting.