A Categorical Modelling Approach to Site and Growth of Eucalyptus Stands in Brazilian Eastern Amazon

Ernandes Macedo Da Cunha Neto,Emmanoella Costa Guaraná Araujo,Hudson Franklin Pessoa Veras,Allan Libanio Pelissari,Thiago Cardoso Silva,Ana Paula Dalla Corte,Carlos Roberto Sanquetta

Revista de Gestão Social e Ambiental（2023）

Cited 0|Views12

Abstract

Theoretical framework: Site index cannot be generalized to different eucalyptus clonal stands, since each clone has a distinct growth and yield pattern, in which categorical variables may add site-specific effects to assess model's interregional variability. Objective: This study aimed to assess the statistical performance of site index, as well as growth and yield models in different configurations adding categorical variables. Method: The study was carried out in eucalyptus stands in Eastern Brazilian Amazon with three clones of different ages and a different number of trees. Traditional Schumacher’s site model was fitted with the addition of categorical clone variable. Beck-Della Bianca’s model was fitted by ordinary least squares (OLS) and two-stage least squares (2SLS), adding dominant height as site variable and including clone variable. Results and discussion: Schumacher’s clone model presented lower standard estimate error (9.50%) and higher adjusted coefficient of determination (0.61), correcting the lack of normality and homoscedasticity. 2SLS was more accurate than OLS for Beck-Della Bianca’s model. This model validation resulted in root-mean-squared error of 2.82% and bias of 0.03%. Research implications: Additive and multiplicative effects on site index resulted in polymorphism. Clone variable provided more parsimonious and accurate models to estimate site index and forest growth and yield, in which 2SLS was recommended for forest prognosis.

Translated text

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper