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Assessing the potential of an algorithm based on mean climatic data to predict wheat yield

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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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Acknowledgments

The authors thank the Service Public de Wallonie, Direction Générale Opérationnelle de l’Agriculture, des Ressources naturelles et de l’Environnement (SPW-DGO3) for its financial support for the project entitled ‘Suivi en temps réel de l’environnement d’une parcelle agricole par un réseau de microcapteurs en vue d’optimiser l’apport en engrais azotés’. The authors thank also the OptimiSTICS team that allowed them to use the Matlab running code of the STICS model. They are very grateful to CRA-w, especially the Department ‘Agriculture et milieu naturel’, for the Ernage station climate database and to staff in the ULg- GxABT Geopedological Unit for their soil analyses. Finally, they wish to thank the two anonymous reviewers for their helpful comments.

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Authors and Affiliations

  1. Department of Environmental Sciences and Technologies, ULg Gembloux Agro-Bio Tech, 2 Passage Des Déportés, 5030, Gembloux, Belgium

    Benjamin Dumont, Vincent Leemans & Marie-France Destain

  2. Department of Agronomical Sciences, ULg Gembloux Agro-Bio Tech, 5030, Gembloux, Belgium

    Salvador Ferrandis & Bernard Bodson

  3. ULg Gembloux Agro-Bio Tech – Walloon Agricultural Research Centre (CRA-W), 5030, Gembloux, Belgium

    Jean-Pierre Destain

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  1. Benjamin Dumont
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Correspondence to Benjamin Dumont.

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Dumont, B., Leemans, V., Ferrandis, S. et al. Assessing the potential of an algorithm based on mean climatic data to predict wheat yield. Precision Agric 15, 255–272 (2014). https://doi.org/10.1007/s11119-014-9346-9

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