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Jiang, Y., Zebarth, B.J., Somers, G.H., MacLeod, J.A. and Savard, M.M. (2012) Nitrate Leaching from Potato Production in Eastern Canada. In: He, Z., Larkin, R.P. and Honeycutt, C.W., Eds., Sustainable Potato Production: Global Case Studies, Springer, The Netherlands, 233-250.
http://dx.doi.org/10.1007/978-94-007-4104-1_13

has been cited by the following article:

• TITLE: Comparison of Machine Learning Regression Methods to Simulate NO3 Flux in Soil Solution under Potato Crops

  AUTHORS: J. G. Fortin, A. Morais, F. Anctil, L. E. Parent

  KEYWORDS: Machine Learning Regression; Nitrate Leaching; Suction Lysimeter; Potato Cropping System

  JOURNAL NAME: Applied Mathematics, Vol.5 No.5, March 24, 2014

  ABSTRACT: Nitrate (NO3) leaching is a major issue in sandy soils intensively cropped to potato. Modelling could test the effect of management practices on nitrate leaching, particularly with regard to optimal N application rates. The NO3 concentration in the soil solution is well known for its local heterogeneity and hence represents a major challenge for modeling. The objective of this 2-year-study was to evaluate machine learning regression methods to simulate seasonal NO3 concentration dynamics in suction lysimeters in potato plots receiving different N application rates. Four machine learning function approximation methods were compared: multiple linear regressions, multivariate adaptive regression splines, multiple-layer perceptrons, and least squares support vector machines. Input candidates were chosen for known relationships with NO3 concentration. The best regression model was obtained with a 6-inputs least squares support vector machine combining cumulative rainfall, cumulative temperature, day of the year, N fertilisation rate, soil texture, and depth.