TY  - JA
AU  - Abbruzzini,T.F.
AU  - Braga Brandani,C.
AU  - Toledo,F.H.
AU  - Pellegrino Cerri,C.E.
TI  - Soil carbon and nitrogen stocks in sugarcane systems by Bayesian conditional autoregressive model – an unbiased prediction strategy
PY  - 2017///
CY  - United Kingdom : 
PB  - Taylor and Francis
KW  - Bayesian theory
KW  - AGROVOC
KW  - Soil
KW  - Carbon
N1  - Peer review
N2  - 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
UR  - http://libcatalog.cimmyt.org/Download/cis/58647.pdf
DO  - https://doi.org/10.1080/17583004.2017.1309204
T2  - Carbon Management
ER  -