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Predicting of biomass in Brazilian tropical dry forest: a statistical evaluation of generic equations

Overview of attention for article published in Anais da Academia Brasileira de Ciências, September 2017
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Title
Predicting of biomass in Brazilian tropical dry forest: a statistical evaluation of generic equations
Published in
Anais da Academia Brasileira de Ciências, September 2017
DOI 10.1590/0001-3765201720170047
Pubmed ID
Authors

Robson B DE Lima, Francisco T Alves, Cinthia P DE Oliveira, José A A DA Silva, Rinaldo L C Ferreira

Abstract

Dry tropical forests are a key component in the global carbon cycle and their biomass estimates depend almost exclusively of fitted equations for multi-species or individual species data. Therefore, a systematic evaluation of statistical models through validation of estimates of aboveground biomass stocks is justifiable. In this study was analyzed the capacity of generic and specific equations obtained from different locations in Mexico and Brazil, to estimate aboveground biomass at multi-species levels and for four different species. Generic equations developed in Mexico and Brazil performed better in estimating tree biomass for multi-species data. For Poincianella bracteosa and Mimosa ophthalmocentra, only the Sampaio and Silva (2005) generic equation was the most recommended. These equations indicate lower tendency and lower bias, and biomass estimates for these equations are similar. For the species Mimosa tenuiflora, Aspidosperma pyrifolium and for the genus Croton the specific regional equations are more recommended, although the generic equation of Sampaio and Silva (2005) is not discarded for biomass estimates. Models considering gender, families, successional groups, climatic variables and wood specific gravity should be adjusted, tested and the resulting equations should be validated at both local and regional levels as well as on the scales of tropics with dry forest dominance.

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Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 24%
Student > Bachelor 5 15%
Researcher 5 15%
Student > Ph. D. Student 4 12%
Student > Doctoral Student 3 9%
Other 4 12%
Unknown 5 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 29%
Environmental Science 9 26%
Engineering 2 6%
Biochemistry, Genetics and Molecular Biology 1 3%
Immunology and Microbiology 1 3%
Other 3 9%
Unknown 8 24%