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

Author(s)

Abbruzzini, Thalita Fernanda ; Brandani, Carolina Braga ; Barrozo Toledo, Fernando Henrique Ribeiro ; Pellegrino Cerri, Carlos Eduardo

• Document type: Journal article
• Source: CARBON MANAGEMENT; v. 8, n. 2, p. 207-214, 2017.
• Web of Science Citations: 0

Abstract

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)

• FAPESP's process: 10/04017-7 - Greenhouse gas emissions and soil C and N under organic and conventional sugarcane cultivation
• Grantee: Thalita Fernanda Abbruzzini
• Support Opportunities: Scholarships in Brazil - Master