Importância relativa das variáveis preditoras no processo de modelagem da produtividade florestal

Modeling forest growth and production is a major challenge for forest managers due to the large number of variables involved and the importance of the generated estimates for decision making in the forestry enterprise. Several statistical and artificial intelligence methods can be used to verify the importance of variables and their selection for the forest modeling process. This study demonstrates the use of the perturbation method in defining the relative importance of predictor variables (silvicultural, climatic and management) in predicting the productivity of eucalyptus stands at the end of the rotation. Data from 320 eucalyptus plantations located in the north of the State of Minas Gerais, aged over seven years, were used. Precipitation distributed at different ages and soil clay content were the most important variables for the prediction of volume at cutting age.A modelagem do crescimento e produção florestal é um grande desafio para os gestores florestais em função da grande quantidade de variáveis envolvidas e da importância das estimativas geradas para a tomada de decisão no empreendimento florestal. Diversos métodos estatísticos e de inteligência artificial podem ser utilizados visando a verificação da importância das variáveis e seleção das mesmas para o processo de modelagem florestal. Neste estudo é demostrado o uso do método de perturbação em modelos de Redes Neurais Artificiais na definição da importância relativa de variáveis preditoras (silviculturais, climáticas e de manejo) da produtividade de povoamentos de eucalipto ao final da rotação (produção florestal). Foram utilizados dados de 320 talhões de plantios de eucalipto localizados no norte do Estado de Minas Gerais, com idade superior a sete anos. A precipitação distribuída em diversas idades e o teor de argila do solo foram as variáveis de maior importância para a predição do volume na idade de corte

Tree Growth and Nutrient Dynamics in Pine Plantations in Southern Brazil

<div><p>ABSTRACT For the development of nutrient budget models to recommend lime and fertilizers for agricultural and forestry crops, curves of plant growth and nutrient accumulation are required. Information about how nutrients are partitioned between the different plant organs is also necessary, but still scarce for pine in Brazil. This study evaluated the growth, biomass partitioning, and nutrient dynamics in pine forests in southern Brazil. To this end, we assessed unthinned 2, 4, 6, and 8-year-old stands of Pinus taeda L. Three plots of 20 × 30 m per stand were delimited, in which three trees of different classes of diameter at breast height (DBH) were chosen. These trees were measured, felled, and the weight of their fresh components (leaves, branches, bark, and wood) was evaluated. Samples of each tree compartment and from the forest litter were taken to determine dry weight and nutrient content. From trees of the mean DBH class, the roots were also collected and the dry weight and nutrient contents determined. The same sampling procedure was carried out with soil for physical and chemical characterization. Regression models were adjusted to estimate growth, nutrient uptake, and nutrient use efficiency of pine trees, based on data collected in this and previous studies. The equations developed in this research can be used in nutrient budget models as well as in other simulation models, to establish recommendations of lime and fertilizers for Pinus taeda stands in southern Brazil.</p></div