Modelling the productivity of naturalised pasture in the North Island,New Zealand: a decision tree approach |
| |
| Institution: | 1. Grup de Mineralogia Aplicada i Geoquímica de Fluids, Departament de Mineralogia, Petrologia i Geologia Aplicada, SIMGEO UB-CSIC, Facultat de Ciències de la Terra, Universitat de Barcelona (UB), C/ Martí i Franquès, s/n, 08028 Barcelona, Spain;2. Centre for Hydrogeology and Geothermics, University of Neuchâtel, Rue Emile-Argand 11, 2000 Neuchâtel, Switzerland;3. Hydrogeology Group (GHS), Department of Civil and Environmental Engineering, Universitat Politècnica de Catalunya (UPC), c/Jordi Girona 1-3, 08034 Barcelona, Spain;4. Department of Chemical and Geological Sciences, University of Cagliari, Via Trentino 51, 09127 Cagliari, Italy;5. Desertification Research Center-NRD, University of Sassari, Viale Italia 39, 07100 Sassari, Italy;1. Department of Psychology, North Dakota State University, Minard 232, Fargo, ND 58108, USA;2. Neuropsychiatric Research Institute, 120 8th Street S., Fargo, ND 58102, USA;3. Eating Disorders Clinical and Research Program, Massachusetts General Hospital, 2 Longfellow Place, Suite 200, Boston, MA 02114, USA;4. Department of Psychiatry, Harvard Medical School, 401 Park Drive, Boston, MA 02215, USA;5. Department of Counseling and Applied Educational Psychology, Northeastern University, 360 Huntington Avenue, Boston, MA 02115, USA |
| |
| Abstract: | Decision tree, one of the data mining methods, has been widely used as a modelling approach and has shown better predictive ability than traditional approaches (e.g. regression). However, very little is known from the literature about how the decision tree performs in predicting pasture productivity. In this study, decision tree models were developed to investigate and predict the annual and seasonal productivity of naturalised hill-pasture in the North Island, New Zealand, and were compared with regression models with respect to model fit, validation and predictive accuracy. The results indicated that the decision tree models for annual and seasonal pasture productivity all had a smaller average squared error (ASE) and a higher percentage of correctly predicted cases than the corresponding regression models. The decision tree model for annual pasture productivity had an ASE which was only half of that of the regression model, and correctly predicted 90% of the cases in the model validation which was 10.8 percentage points higher than that of the regression model. Furthermore, the decision tree models for annual and seasonal pasture productivity also clearly revealed the relative importance of environmental and management variables in influencing pasture productivity, and the interaction among these variables. Spring rainfall was the most significant factor influencing annual pasture productivity, while hill slope was the most significant factor influencing spring and winter pasture productivity, and annual P fertiliser input and autumn rainfall were the most significant factors influencing summer and autumn pasture productivity. One limitation of using the decision tree to predict pasture productivity was that it did not generate a continuous prediction, and thus could not detect the influence of small changes in environmental and management variables on pasture productivity. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
