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Communication dans un congrès

Prediction of in situ soil nitrogen mineralisation using artificial neural networks: a promising way to improve model accuracy

Abstract : The ability of artificial nural networ (ANN) to predict soil N mineralisation in fiels conditions using simple soil characteristics was tested on 74 unfertilised arable crop fields distributed all over France. In situ N mineralisation of soil mineral N and water contents. The ANN method used was the multilayer feed forward neuronal network trained by back propagation algorithm. A set of 56 sites was selected for ANN imput variables (clay content, log transformed CaCO3 content, organic N stock on 30 cm depht and soil pH) explained 62% of in situ N mineralisation rate of the training dataset without bias. However the prediction of N mineralisation for the validation dataset as biased with an error of ca 80 kg ha-1 for an average year. The use of cropping system information as input variables significantly improved both explpanation of dataset (R2=0.83, no bias) and rediction of independant dataset (prediction error of 30 kg ha-1). Thus ANN seemed well adapted to model N mineralisation using a limited number of easily measurable soil variables.
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https://hal.inrae.fr/hal-02756934
Déposant : Migration Prodinra <>
Soumis le : mercredi 3 juin 2020 - 23:34:43
Dernière modification le : vendredi 12 juin 2020 - 10:43:26

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  • HAL Id : hal-02756934, version 1
  • PRODINRA : 19383

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Matthieu Valé, Sovan Lek, Eric Justes. Prediction of in situ soil nitrogen mineralisation using artificial neural networks: a promising way to improve model accuracy. The 15th Nitrogen Workshop, May 2007, Lleida, Spain. ⟨hal-02756934⟩

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