36 resultados para forest plantations


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There are strong uncertainties regarding LAI dynamics in forest ecosystems in response to climate change. While empirical growth & yield models (G&YMs) provide good estimations of tree growth at the stand level on a yearly to decennial scale, process-based models (PBMs) use LAI dynamics as a key variable for enabling the accurate prediction of tree growth over short time scales. Bridging the gap between PBMs and G&YMs could improve the prediction of forest growth and, therefore, carbon, water and nutrient fluxes by combining modeling approaches at the stand level.Our study aimed to estimate monthly changes of leaf area in response to climate variations from sparse measurements of foliage area and biomass. A leaf population probabilistic model (SLCD) was designed to simulate foliage renewal. The leaf population was distributed in monthly cohorts, and the total population size was limited depending on forest age and productivity. Foliage dynamics were driven by a foliation function and the probabilities ruling leaf aging or fall. Their formulation depends on the forest environment.The model was applied to three tree species growing under contrasting climates and soil types. In tropical Brazilian evergreen broadleaf eucalypt plantations, the phenology was described using 8 parameters. A multi-objective evolutionary algorithm method (MOEA) was used to fit the model parameters on litterfall and LAI data over an entire stand rotation. Field measurements from a second eucalypt stand were used to validate the model. Seasonal LAI changes were accurately rendered for both sites (R-2 = 0.898 adjustment, R-2 = 0.698 validation). Litterfall production was correctly simulated (R-2 = 0.562, R-2 = 0.4018 validation) and may be improved by using additional validation data in future work. In two French temperate deciduous forests (beech and oak), we adapted phenological sub-modules of the CASTANEA model to simulate canopy dynamics, and SLCD was validated using LAI measurements. The phenological patterns were simulated with good accuracy in the two cases studied. However, IA/max was not accurately simulated in the beech forest, and further improvement is required.Our probabilistic approach is expected to contribute to improving predictions of LAI dynamics. The model formalism is general and suitable to broadleaf forests for a large range of ecological conditions. (C) 2014 Elsevier B.V. All rights reserved.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Genus Atta includes some of the most important Formicidae leaf cutter ants which cause extensive damage to the eucalyptus plantations. Atta sexdens rubropilosa Forel, one of the chief pests in Brazilian reforestation, can restrict and reduce forest productivity by its intense and constant leaf-cutting activities on plants at all stages. Therefore, the demand for new products to control A. sexdens rubropilosa indicates the study of the utilization of the dry powder formulation of diatomaceous earth (DE) against this pest in the eucalyptus cultivars. The study was conducted using 120 colonies of A. sexdens rubropilosa in Eucalyptus grandis Hill ex. Maiden x Eucalyptus urophylla Blake (Myrtaceae) (urograndis) stand. The randomized block experimental design was used with six treatments (1, 10, 25, and 50 g/m2 of DE, 6.0 g/m2 sulfluramid bait per square meter of loose soil, and the control) with five replications, each with four colonies of this ant. Diatomaceous earth was applied to the active A. sexdens rubropilosa ant holes, and the sulfluramid bait was applied in bulk in a localized manner. The control efficacy of A. sexdens rubropilosa with DE was low, showing values similar to that of the control, and, for this reason, it cannot be used to control this ant. The bait with sulfluramid showed higher efficacy than those of the other treatments.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)