Knowledge Center Catalog

Soil carbon and nitrogen stocks in sugarcane systems by Bayesian conditional autoregressive model – an unbiased prediction strategy (Record no. 58647)

MARC details
000 -LEADER
fixed length control field 02146nab a22002897a 4500
001 - CONTROL NUMBER
control field 58647
003 - CONTROL NUMBER IDENTIFIER
control field MX-TxCIM
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20191211165914.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 150722s2017 uk |||p|op||| 00| 0 eng d
024 8# - OTHER STANDARD IDENTIFIER
Standard number or code https://doi.org/10.1080/17583004.2017.1309204
040 ## - CATALOGING SOURCE
Original cataloging agency MX-TxCIM
100 1# - MAIN ENTRY--PERSONAL NAME
9 (RLIN) 4827
Personal name Abbruzzini, T.F.
245 10 - TITLE STATEMENT
Title Soil carbon and nitrogen stocks in sugarcane systems by Bayesian conditional autoregressive model – an unbiased prediction strategy
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc. United Kingdom :
Name of publisher, distributor, etc. Taylor and Francis
Date of publication, distribution, etc. 2017.
500 ## - GENERAL NOTE
General note Peer review
520 ## - SUMMARY, ETC.
Summary, etc. 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.
546 ## - LANGUAGE NOTE
Language note Text in English
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 4013
Topical term or geographic name as entry element Bayesian theory
Source of heading or term AGROVOC
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 4828
Topical term or geographic name as entry element Soil
Source of heading or term AGROVOC
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 2601
Topical term or geographic name as entry element Carbon
Source of heading or term AGROVOC
700 1# - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 4829
Personal name Braga Brandani, C.
700 1# - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 1999
Personal name Toledo, F.H.
Field link and sequence number I1706676
Miscellaneous information Genetic Resources Program
700 1# - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 4830
Personal name Pellegrino Cerri, C.E.
773 0# - HOST ITEM ENTRY
Title Carbon Management
Related parts v. 8, no. 2, p. 207-214
856 4# - ELECTRONIC LOCATION AND ACCESS
Link text Access only for CIMMYT Staff
Uniform Resource Identifier http://libcatalog.cimmyt.org/Download/cis/58647.pdf
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Article
Suppress in OPAC No
Holdings
Date last seen Total Checkouts Price effective from Koha item type Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Withdrawn status Home library Current library Date acquired
05/16/2017   05/16/2017 Article Not Lost Dewey Decimal Classification     CIMMYT Staff Publications Collection   CIMMYT Knowledge Center: John Woolston Library CIMMYT Knowledge Center: John Woolston Library 05/16/2017

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