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1.
Agric Syst ; 168: 213-223, 2019 Jan.
Article in English | MEDLINE | ID: mdl-30774184

ABSTRACT

Each month the JRC issues the MARS Bulletin detailing the agro-meteorological and expert analysis underpinning the assessment of European crops' status and yield forecasts. In this context a resume is provided to give an overview on the geographical distribution of eventual crop damages. The MARS Bulletin provides such information in a set of synthetic maps (Areas of Concern), produced in each Bulletin, depicting extreme weather events and their impact on crops that have occurred in Europe during the analysis period. The present article describes the mix of quantitative and qualitative datasets and methodologies that drive the delineation of the Areas of Concern (AOC) maps and evaluates their capability to resemble crop production losses. The quantitative analysis is based on the Mars Crop Yield Forecasting System (MCYFS) indicators coming from meteorological models, crop growth models and remote sensing data. Indicators are considered in absolute and relative terms and in their relation with standard statistical metrics. The outcome of the quantitative analysis is a set of potential Areas of Concern. Experts' judgment is thus necessary to discriminate potential results through a qualitative analysis focused on: past occurred events and climatologic conditions; agro-management practices; regional agricultural systems peculiarity and their historical resilience and resistance to adverse conditions. In this article the experts' judgment of the team of current MARS analysts, as used in the AOC analysis, is translated into a warning index. Such index condenses the specific contribution to the final production of each development stage and the adverse agrometeorological events occurred, as depicted into the AOC maps. The warning index is computed at country scale for the past five agricultural seasons, from season 2011-2012 to season 2015-2016. Two crops are considered, winter wheat and grain maize as proxy for winter and summer crop groups. The warning indexes calculated are then compared to the national production in a qualitative way. To support the analysis few study cases are presented. The findings of this article highlight that the events depicted in the AOC maps are informative about production losses and specific knowledge is needed to full understand the information carried.

2.
Sci Rep ; 8(1): 15420, 2018 10 18.
Article in English | MEDLINE | ID: mdl-30337571

ABSTRACT

Here we assess the quality and in-season development of European wheat (Triticum spp.) yield forecasts during low, medium, and high-yielding years. 440 forecasts were evaluated for 75 wheat forecast years from 1993-2013 for 25 European Union (EU) Member States. By July, years with median yields were accurately forecast with errors below ~2%. Yield forecasts in years with low yields were overestimated by ~10%, while yield forecasts in high-yielding years were underestimated by ~8%. Four-fifths of the lowest yields had a drought or hot driver, a third a wet driver, while a quarter had both. Forecast accuracy of high-yielding years improved gradually during the season, and drought-driven yield reductions were anticipated with lead times of ~2 months. Single, contrasting successive in-season, as well as spatially distant dry and wet extreme synoptic weather systems affected multiple-countries in 2003, '06, '07, '11 and 12', leading to wheat losses up to 8.1 Mt (>40% of total EU loss). In these years, June forecasts (~ 1-month lead-time) underestimated these impacts by 10.4 to 78.4%. To cope with increasingly unprecedented impacts, near-real-time information fusion needs to underpin operational crop yield forecasting to benefit from improved crop modelling, more detailed and frequent earth observations, and faster computation.


Subject(s)
Crops, Agricultural , European Union , Forecasting/methods , Seasons , Triticum/growth & development , Crops, Agricultural/economics , Crops, Agricultural/growth & development , Droughts , European Union/organization & administration , Extreme Heat , Humans , Models, Statistical , Predictive Value of Tests , Rain , Weather
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