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  • 1
    ISSN: 1573-5036
    Keywords: cations ; fire ; nitrogen ; nutrients ; phosphorus ; slash-and-burn ; soil ; tropical forests
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Abstract The most commonly observed change in soil following slash-and-burn clearing of tropical forest is a short-term increase in nutrient availability. Studies of shifting cultivation commonly cite the incorporation of nutrient-rich ash from consumed aboveground biomass into soil as the reason for this change. The effects of soil heating on nutrient availability have been examined only rarely in field studies of slash-and-burn, and soil heating as a mechanism of nutrient release is most often assumed to be of minor importance in the field. Few budgets for above and belowground nutrient flux have been developed in the tropics, and a survey of results from field and laboratory studies indicates that soils are sufficiently heated during most slash-and-burn events, particularly in dry and monsoonal climates, to cause significant, even substantial release of nutrients from non-plant-available into plant-available forms in soil. Conversely, large aboveground losses of nutrients during and after burning often result in low quantities of nutrients that are released to soil. Assessing the biophysical sustainability of an agricultural practice requires detailed information about nutrient flux and loss incurred during management. To this end, current conceptual models of shifting cultivation should be revised to more accurately describe these fluxes and losses.
    Type of Medium: Electronic Resource
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  • 2
    ISSN: 1573-5036
    Keywords: fire ; nitrogen ; phosphorus ; soil nutrient heterogeneity ; tree effects ; tropical dry forest
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Abstract Individual trees are known to influence soil chemical properties, creating spatial patterns that vary with distance from the stem. The influence of trees on soil chemical properties is commonly viewed as the agronomic basis for low-input agroforestry and shifting cultivation practices, and as an important source of spatial heterogeneity in forest soils. Few studies, however, have examined the persistence of the effects of trees on soil after the pathways responsible for the effects are removed. Here, we present evidence from a Mexican dry forest indicating that stem-related patterns of soil nutrients do persist following slash-and-burn removal of trees and two years of cropping. Pre-disturbance concentrations of resin extractable phosphorus (P), bicarbonate extractable P, NaOH extractable P, total P, total nitrogen (N) and carbon (C), KCl extractable nitrate (NO3 -), and net N mineralization and nitrification rates were higher in stem than dripline soils under two canopy dominant species of large-stemmed trees with contrasting morphologies and phenologies (Caesalpinia eriostachys Benth. and Forchhammeria pallida Liebm.). These stem effects persisted through slash burning and a first growing season for labile inorganic and organic P, NaOH inorganic P, and plant-available P, and through a second growing season for labile organic P, NaOH organic P, and plant-available P. While stem effects for extractable NO3 -, net nitrification rates, total N and C disappeared after felling and slash burning, these stem effects returned after the first growing season. These results support the view that tree-influenced patterns of soil nutrients do persist after tree death, and that trees contribute to the long-term spatial heterogeneity of forest soils.
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Statistics and computing 7 (1997), S. 1-10 
    ISSN: 1573-1375
    Keywords: Bayes factor ; eigenvalue decomposition ; Gaussian mixture ; Gibbs sampler
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract A new approach to cluster analysis has been introduced based on parsimonious geometric modelling of the within-group covariance matrices in a mixture of multivariate normal distributions, using hierarchical agglomeration and iterative relocation. It works well and is widely used via the MCLUST software available in S-PLUS and StatLib. However, it has several limitations: there is no assessment of the uncertainty about the classification, the partition can be suboptimal, parameter estimates are biased, the shape matrix has to be specified by the user, prior group probabilities are assumed to be equal, the method for choosing the number of groups is based on a crude approximation, and no formal way of choosing between the various possible models is included. Here, we propose a new approach which overcomes all these difficulties. It consists of exact Bayesian inference via Gibbs sampling, and the calculation of Bayes factors (for choosing the model and the number of groups) from the output using the Laplace–Metropolis estimator. It works well in several real and simulated examples.
    Type of Medium: Electronic Resource
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Statistics and computing 8 (1998), S. 145-158 
    ISSN: 1573-1375
    Keywords: Bounded likelihood ; identifiability ; Gibbs sampler ; non-informative prior ; normal distribution ; Poisson distribution ; prior feedback ; proper posterior distribution ; simulated annealing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract This paper synthesizes a global approach to both Bayesian and likelihood treatments of the estimation of the parameters of a hidden Markov model in the cases of normal and Poisson distributions. The first step of this global method is to construct a non-informative prior based on a reparameterization of the model; this prior is to be considered as a penalizing and bounding factor from a likelihood point of view. The second step takes advantage of the special structure of the posterior distribution to build up a simple Gibbs algorithm. The maximum likelihood estimator is then obtained by an iterative procedure replicating the original sample until the corresponding Bayes posterior expectation stabilizes on a local maximum of the original likelihood function.
    Type of Medium: Electronic Resource
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