TAPE MODELING OF EUCALYPTUS STEM IN CROP-LIVESTOCK-FORESTRY INTEGRATION SYSTEMS

Abstract

This paper aims to evaluate and compare the mixed effects modeling and artificial neural networks in order to estimate the taper of eucalyptus in integrated Crop-Livestock-Forestry (iCLF) systems. The data were collected in an experimental area of iCLF, implanted by the Brazilian Company of Farming Research – EMBRAPA Agrossilvipastoril, located in the municipality of Sinop, Mato Grosso State, Brazil. To reach the proposed aim, 165 trees with 51 months of age were scaled for the taper modeling with mixed effects models and artificial neural networks. The performance of these techniques was evaluated through precision measurements and graphical analysis. Mixed effects modeling and artificial neural networks are efficient and recommended in the estimative of taper of eucalyptus in integrated Crop-Livestock-Forestry system; however, despite both evaluated techniques present accurate results in predicting the taper of the sampled trees, the artificial neural network predicts values with greater precision than the modeling of mixed effects.

Plant phenotyping relevance

ユーカリ幹のテーパーという植物形態形質を推定するため、混合効果モデルと人工ニューラルネットワークを比較・評価しており、推定手法が研究の中心である。

Code and data availability

The article describes cubage measurements of 165 eucalyptus trees and MEM/ANN taper modeling, but provides no public dataset, code repository, or trained model deposit. The only URLs mentioned (R project, h2o CRAN package) are generic software references, not paper-specific assets.

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