LINEAR MIXED MODEL TO DESCRIBE THE BASAL AREA INCREMENT FOR INDIVUDUAL CEDRO (<i>Cedrela odorata </i>L.)TREES IN OCCIDENTAL AMAZON, BRAZIL

• Main Authors: Thiago Augusto da Cunha, César Augusto Guimarães Finger, Paulo Renato Schneider
• Format: Article
• Language: Portuguese
• Published: Universidade Federal de Santa Maria 2013-08-01
• Series: Ciência Florestal
• Subjects: modelo misto; mínimos quadrados generalizados; morfometria da árvore; índice de competição
• Online Access: http://cascavel.ufsm.br/revistas/ojs-2.2.2/index.php/cienciaflorestal/article/view/10557

<p><a href="http://dx.doi.org/10.5902/1980509810557"><span>http://dx.doi.org/10.5902/1980509810557</span></a></p><p>Reliable growth data from trees are important to establish a rational forest management. Characteristics from trees, like the size, crown architecture and competition indices have been used to mathematically describe the increment efficiently when associated with them. However, the precise role of these effects in the growth-modeling destined to tropical trees needs to be further studied. Here we reconstructed the basal area increment (BAI) of individual <em>Cedrelaodorata</em> trees, sampled at Amazon forest, to develop a growth-model using potential-predictors like: (1) classical tree size; (2) morphometric data; (3) competition and (4) social position including liana loads. Despite the large variation in tree size and growth, we observed that these kinds of predictor variables described well the BAI in level of individual tree. The fitted mixed model achieve a high efficiency (R²=92.7 %) and predicted 3-years BAI over bark for trees of <em>Cedrelaodorata</em> ranging from 10 to 110 cm at diameter at breast height. Tree height, steam slenderness and crown formal demonstrated high influence in the BAI growth model and explaining most of the growth variance (Partial R²=87.2%). Competition variables had negative influence on the BAI, however, explained about 7% of the total variation. The introduction of a random parameter on the regressions model (mixed modelprocedure) has demonstrated a better significance approach to the data observed and showed more realistic predictions than the fixed model.</p>