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Dumont, B.; Basso, B.; Leemans, V.; Destain, J.P.; Bodson, B.; Destain, M.-F. |
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Title |
Yield variability linked to climate uncertainty and nitrogen fertilisation |
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2013 |
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CropM |
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9th ECPA - European Conference on Precision Agriculture, Lleida, Spain, 2013-07-07 to 2013-07-11 |
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MA @ admin @ |
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2411 |
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Dumont, B.; Ferrandis, V., S.; Leemans, V.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
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Title |
Vers un système de prédiction du rendement en temps réel |
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2012 |
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CropM |
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IXeme Seminaire STICS, Orléans, Sainte Montaine (France)., 2012-10-16 to 2012-10-16 |
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MA @ admin @ |
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2405 |
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Dumont, B.; Leemans, V.; Ferrandis Vallterra, S.; Vancutsem, F.; Seutin, B.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
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Title |
Assessing the potential of an algorithm based on mean climatic data to predict wheat yield |
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Conference Article |
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2012 |
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CropM |
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11th International Conference on Precision Agriculture. Indianapolis (USA)., 2012-07-15 to 2012-07-18 |
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MA @ admin @ |
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2404 |
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Dumont, B.; Leemans, V.; Ferrandis Vallterra, S.; Vancutsem, F.; Seutin, B.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
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Title |
A first step towards a real-time predictive yield support system |
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2012 |
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CIGR-AgEng 2012, International Conference on Agricultural Engineering, Valencia (Spain)., 2012-07-07 to 2012-07-12 |
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Call Number |
MA @ admin @ |
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2403 |
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Author |
Dumont, B.; Leemans, V.; Ferrandis, S.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
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Title |
Assessing the potential of an algorithm based on mean climatic data to predict wheat yield |
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Journal Article |
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Year |
2014 |
Publication |
Precision Agriculture |
Abbreviated Journal |
Precision Agric. |
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15 |
Issue |
3 |
Pages |
255-272 |
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Keywords |
stics model; yield prediction; real-time; proxy-sensing; stochastic weather generator; crop yield; mediterranean environment; simulation-model; variability; nitrogen; ensembles; forecasts; demeter; europe |
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Abstract |
The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3-4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days. |
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English |
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1385-2256 1573-1618 |
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CropM |
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MA @ admin @ |
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4621 |
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