[en] 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.
Disciplines :
Agriculture & agronomy Computer science
Author, co-author :
Dumont, Benjamin ; Université de Liège - ULiège > Sciences et technologie de l'environnement > Agriculture de précision
Leemans, Vincent ; Université de Liège - ULiège > Sciences et technologie de l'environnement > Agriculture de précision
Ferrandis, Salvador
Bodson, Bernard ; Université de Liège - ULiège > Sciences agronomiques > Phytotechnie des régions tempérées
Destain, Jean-Pierre ; Université de Liège - ULiège > Sciences agronomiques > Phytotechnie des régions tempérées
Destain, Marie-France ; Université de Liège - ULiège > Sciences et technologie de l'environnement > Agriculture de précision
Language :
English
Title :
Assessing the potential of an algorithm based on mean climatic data to predict wheat yield
Publication date :
2014
Journal title :
Precision Agriculture
ISSN :
1385-2256
eISSN :
1573-1618
Publisher :
Springer, Secaucus, United States - New Jersey
Volume :
15(3)
Pages :
255-272
Peer reviewed :
Peer Reviewed verified by ORBi
Name of the research project :
Suivi en temps réel de l’environnement d’une parcelle agricole par un réseau de micro-capteurs en vue d’optimiser l’apport en engrais azotés
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