Abstract
ABSTRACT Eucalyptus plantation has expanded considerably in Brazil, especially in regions where soils have low fertility, such as in Brazilian Cerrados. To achieve greater productivity, it is essential to know the needs of the soil and the right moment to correct it. Mathematical and computational models have been used as a promising alternative to help in this decision-making process. The aim of this study was to model the influence of climate and physico-chemical attributes in the development of Eucalyptus urograndis in Entisol quartzipsamment soil using the decision tree induction technique. To do so, we used 30 attributes, 29 of them are predictive and one is the target-attribute or response variable regarding the height of the eucalyptus. We defined four approaches to select these features: no selection, Correlation-based Feature Selection (CFS), Chi-square test (χ2) and Wrapper. To classify the data, we used the decision tree induction technique available in the Weka software 3.6. This data mining technique allowed us to create a classification model for the initial development of eucalyptus. From this model, one can predict new cases in different production classes, in which the individual wood volume (IWV) and the diameter at breast height (DBH) are crucial features to predict the growth of Eucalyptus urograndis, in addition to the presence of chemical soil components such as: magnesium (Mg+2), phosphorus (P), aluminum (Al+3), potassium (K+), potential acidity (H + Al), hydrogen potential (pH), and physical attributes such as soil resistance to penetration and related to climate, such as minimum temperature.
Highlights
The plantation of Eucalyptus spp in Brazil has increased in recent years because of the following reasons: quick growth, diversification in the use of wood and the ease of adapting to different soil and climate conditions
We present some metrics such as accuracy, Kappa coefficient, classes precision, and the number of rules generated in each tree for each method of attribute selection
From the 29 predictive attributes in the dataset, the Wrapper method selected just five of them: diameter at breast height (DBH), individual wood volume (IWV), organic matter (OM), Potential acidity (H+Al) and sum of bases (SB) at a depth from 0.20 to 0.40 m, and potential acidity at a depth from 0.00 to 0.10 m with an accuracy of 84.67% and Kappa of 0.77, indicating that the use of these five attributes are enough to achieve a very good classification
Summary
The plantation of Eucalyptus spp in Brazil has increased in recent years because of the following reasons: quick growth, diversification in the use of wood and the ease of adapting to different soil and climate conditions. This fact makes commercial plantations of eucalyptus in Brazil quite variable and productive, which contributes to the recognition of the country as the one with the best technologies in eucalyptus plantation currently, reaching around 60 m3.ha-1 average productivity in rotations of seven years (SFB, 2016). The reason is that those systems can make the sector increase even more, since it allows the evaluation of large amounts of information on soils and plants, which can lead to new findings and most appropriate strategies to increase productivity and environmental protection
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