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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Soil carbon and nitrogen stocks in sugarcane systems by Bayesian conditional autoregressive model - an unbiased prediction strategy

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Autor(es):
Abbruzzini, Thalita Fernanda ; Brandani, Carolina Braga ; Barrozo Toledo, Fernando Henrique Ribeiro ; Pellegrino Cerri, Carlos Eduardo
Número total de Autores: 4
Tipo de documento: Artigo Científico
Fonte: CARBON MANAGEMENT; v. 8, n. 2, p. 207-214, 2017.
Citações Web of Science: 0
Resumo

Spatially dependent data are predominant in soil science and prone to biased inferences from standard statistical analysis. Thus, the aims of this study were: to model the spatial dependency among soil sampling points using a Bayesian conditional autoregressive (CAR) prior; and to determine the effects of different sugarcane management systems on soil C and N stocks. Four sugarcane sites were evaluated: conventional burned (BSC); unburned (USC); and organic sugarcane for 4 years (O04) and 12 years (O12). A native vegetation forest (NVF) site was used as a reference. The CAR model prediction agreed with the observed results of both soil C and N stocks. The highest predicted soil C and N stocks at 0-30 cm depth were observed for O12 (57.3 and 4.8 Mg ha-1), and the lowest were for BSC (37.6 and 3.0 Mg ha(-1)). The Bayesian CAR model captured the spatial dependence among soil sampling points and allowed to compare soil C and N stocks of different sugarcane managements. Thus, Bayesian spatial modeling is a novel approach to evaluate soil management practices when performing ad hoc monitoring of soil carbon within contiguous areal units. (AU)

Processo FAPESP: 10/04017-7 - Emissões de gases do efeito estufa e estoques de c e n em solos sob cultivo orgânico e convencional de cana-de-açúcar
Beneficiário:Thalita Fernanda Abbruzzini
Linha de fomento: Bolsas no Brasil - Mestrado