Abstract

The objective of this work was to optimize a neural network (NN) for modelling potato tuber growth and its in-field variations in eastern Canada In addition to climatic inputs, the cumulative and maximal leaf area index (LAI) were incorporated to account for in-field scale variability Soil and genetic parameters were assumed to be integrated in LAI as suggested by earlier work. Each input and combination of inputs was evaluated from the changes they induced in MAE (mean absolute error) and RMSE (root mean square error). Results using data from several replicated on-farm experiments between 2005 and 2008 suggest that a NN model using cumulative solar radiation, cumulative rainfall and cumulative LAI can adequately model site-specific tuber growth. The MAE of the retained model was 209 kg DM ha(-1), which represents less than 4% of the mean final tuber yield for the 3 years of the study Non-linear effects of explicative variables on tuber yield were attested by comparing the results of the NN simulations to those of a multiple linear regression (MLR). The failure of MLR to simulate temporal discontinuities in tuber growth supports the use of a non-linear approach such as a NN to model tuber growth (C) 2010 Elsevier B V All rights reserved.

Keywords

Crop growth model; Neural network; Leaf area index; Solar radiation; SUBSTOR

Published in

Computers and Electronics in Agriculture
2010, Volume: 73, number: 2, pages: 126-132
Publisher: ELSEVIER SCI LTD

SLU Authors

• Bolinder, Martin

  • Department of Soil and Environment, Swedish University of Agricultural Sciences
    
  • Laval University

UKÄ Subject classification

Agricultural Science
Computer Science


Publication identifier

DOI: https://doi.org/10.1016/j.compag.2010.05.011

Permanent link to this page (URI)

https://res.slu.se/id/publ/60002