Predicting Carbon Stocks Following Reforestation of Pastures: A Sampling Scenario‐Based Approach for Testing the Utility of Field‐Measured and Remotely Derived Variables

Reforestation of agricultural lands is an important means of restoring land and sequestering carbon (C). At large scales, the labour and costs of direct measurement of ecosystem responses can be prohibitive, making the development of models valuable. Here, we develop a new sampling scenario‐based mo...
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Autor*in:	Cavagnaro, Timothy R [verfasserIn] Cunningham, Shaun C

Format:	Artikel
Sprache:	Englisch

Erschienen:	2017

Rechteinformationen:	Nutzungsrecht: Copyright © 2016 John Wiley & Sons, Ltd.

Schlagwörter:	reforestation soil C Bayesian GLM carbon (C) sequestration leaf litter soil ecology

Übergeordnetes Werk:	Enthalten in: Land degradation & development - Chichester, Sussex : Wiley, 1996, 28(2017), 3, Seite 1122-1133
Übergeordnetes Werk:	volume:28 ; year:2017 ; number:3 ; pages:1122-1133

Links:	Volltext Link aufrufen Link aufrufen

DOI / URN:	10.1002/ldr.2585

Katalog-ID:	OLC1993158979

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spelling	10.1002/ldr.2585 doi PQ20170501 (DE-627)OLC1993158979 (DE-599)GBVOLC1993158979 (PRQ)p955-ddf2d064c642f14d347e7c0e3c6a4bc85648119bfbf4517751ac3c280dd504223 (KEY)0175141720170000028000301122predictingcarbonstocksfollowingreforestationofpast DE-627 ger DE-627 rakwb eng 630 ZDB Cavagnaro, Timothy R verfasserin aut Predicting Carbon Stocks Following Reforestation of Pastures: A Sampling Scenario‐Based Approach for Testing the Utility of Field‐Measured and Remotely Derived Variables 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Reforestation of agricultural lands is an important means of restoring land and sequestering carbon (C). At large scales, the labour and costs of direct measurement of ecosystem responses can be prohibitive, making the development of models valuable. Here, we develop a new sampling scenario‐based modelling approach coupled with Bayesian model averaging to build predictive models for absolute values in mixed‐species woody plantings and differences from their adjacent pasture, for litter stocks, soil C stocks and soil C:N ratios. Modelling scenarios of increasing data availability and effort were tested. These included variables that could be derived without a site visit (e.g. location, climate and management) that were sampled in the adjacent pasture (e.g. soil C and nutrients) or were sampled in the environmental planting (e.g. vegetation, litter properties, soil C and nutrients). The predictive power of models varied considerably among C variables (litter stocks, soil C stocks and soil C:N ratios in tree plantings and their differences to their adjacent pastures) and the model scenarios used. The use of a sampling scenario‐based approach to building predictive models shows promise for monitoring changes in tree plantings, following reforestation. The approach could also be readily adapted to other contexts where sampling effort for predictor variables in models is a major potential limitation to model utilization. This study demonstrates the benefit of exploring scenarios of data availability during modelling and will be especially valuable where the sampling effort differs greatly among variables. Copyright © 2016 John Wiley & Sons, Ltd. Nutzungsrecht: Copyright © 2016 John Wiley & Sons, Ltd. reforestation soil C Bayesian GLM carbon (C) sequestration leaf litter soil ecology Cunningham, Shaun C oth Enthalten in Land degradation & development Chichester, Sussex : Wiley, 1996 28(2017), 3, Seite 1122-1133 (DE-627)211582530 (DE-600)1319202-4 (DE-576)9211582539 1085-3278 nnns volume:28 year:2017 number:3 pages:1122-1133 http://dx.doi.org/10.1002/ldr.2585 Volltext http://onlinelibrary.wiley.com/doi/10.1002/ldr.2585/abstract http://search.proquest.com/docview/1889683843 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OPC-GGO AR 28 2017 3 1122-1133
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topic_title	630 ZDB Predicting Carbon Stocks Following Reforestation of Pastures: A Sampling Scenario‐Based Approach for Testing the Utility of Field‐Measured and Remotely Derived Variables reforestation soil C Bayesian GLM carbon (C) sequestration leaf litter soil ecology
topic	ddc 630 misc reforestation misc soil C misc Bayesian GLM misc carbon (C) sequestration misc leaf litter misc soil ecology
topic_unstemmed	ddc 630 misc reforestation misc soil C misc Bayesian GLM misc carbon (C) sequestration misc leaf litter misc soil ecology
topic_browse	ddc 630 misc reforestation misc soil C misc Bayesian GLM misc carbon (C) sequestration misc leaf litter misc soil ecology
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title	Predicting Carbon Stocks Following Reforestation of Pastures: A Sampling Scenario‐Based Approach for Testing the Utility of Field‐Measured and Remotely Derived Variables
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title_full	Predicting Carbon Stocks Following Reforestation of Pastures: A Sampling Scenario‐Based Approach for Testing the Utility of Field‐Measured and Remotely Derived Variables
author_sort	Cavagnaro, Timothy R
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title_sort	predicting carbon stocks following reforestation of pastures: a sampling scenario‐based approach for testing the utility of field‐measured and remotely derived variables
title_auth	Predicting Carbon Stocks Following Reforestation of Pastures: A Sampling Scenario‐Based Approach for Testing the Utility of Field‐Measured and Remotely Derived Variables
abstract	Reforestation of agricultural lands is an important means of restoring land and sequestering carbon (C). At large scales, the labour and costs of direct measurement of ecosystem responses can be prohibitive, making the development of models valuable. Here, we develop a new sampling scenario‐based modelling approach coupled with Bayesian model averaging to build predictive models for absolute values in mixed‐species woody plantings and differences from their adjacent pasture, for litter stocks, soil C stocks and soil C:N ratios. Modelling scenarios of increasing data availability and effort were tested. These included variables that could be derived without a site visit (e.g. location, climate and management) that were sampled in the adjacent pasture (e.g. soil C and nutrients) or were sampled in the environmental planting (e.g. vegetation, litter properties, soil C and nutrients). The predictive power of models varied considerably among C variables (litter stocks, soil C stocks and soil C:N ratios in tree plantings and their differences to their adjacent pastures) and the model scenarios used. The use of a sampling scenario‐based approach to building predictive models shows promise for monitoring changes in tree plantings, following reforestation. The approach could also be readily adapted to other contexts where sampling effort for predictor variables in models is a major potential limitation to model utilization. This study demonstrates the benefit of exploring scenarios of data availability during modelling and will be especially valuable where the sampling effort differs greatly among variables. Copyright © 2016 John Wiley & Sons, Ltd.
abstractGer	Reforestation of agricultural lands is an important means of restoring land and sequestering carbon (C). At large scales, the labour and costs of direct measurement of ecosystem responses can be prohibitive, making the development of models valuable. Here, we develop a new sampling scenario‐based modelling approach coupled with Bayesian model averaging to build predictive models for absolute values in mixed‐species woody plantings and differences from their adjacent pasture, for litter stocks, soil C stocks and soil C:N ratios. Modelling scenarios of increasing data availability and effort were tested. These included variables that could be derived without a site visit (e.g. location, climate and management) that were sampled in the adjacent pasture (e.g. soil C and nutrients) or were sampled in the environmental planting (e.g. vegetation, litter properties, soil C and nutrients). The predictive power of models varied considerably among C variables (litter stocks, soil C stocks and soil C:N ratios in tree plantings and their differences to their adjacent pastures) and the model scenarios used. The use of a sampling scenario‐based approach to building predictive models shows promise for monitoring changes in tree plantings, following reforestation. The approach could also be readily adapted to other contexts where sampling effort for predictor variables in models is a major potential limitation to model utilization. This study demonstrates the benefit of exploring scenarios of data availability during modelling and will be especially valuable where the sampling effort differs greatly among variables. Copyright © 2016 John Wiley & Sons, Ltd.
abstract_unstemmed	Reforestation of agricultural lands is an important means of restoring land and sequestering carbon (C). At large scales, the labour and costs of direct measurement of ecosystem responses can be prohibitive, making the development of models valuable. Here, we develop a new sampling scenario‐based modelling approach coupled with Bayesian model averaging to build predictive models for absolute values in mixed‐species woody plantings and differences from their adjacent pasture, for litter stocks, soil C stocks and soil C:N ratios. Modelling scenarios of increasing data availability and effort were tested. These included variables that could be derived without a site visit (e.g. location, climate and management) that were sampled in the adjacent pasture (e.g. soil C and nutrients) or were sampled in the environmental planting (e.g. vegetation, litter properties, soil C and nutrients). The predictive power of models varied considerably among C variables (litter stocks, soil C stocks and soil C:N ratios in tree plantings and their differences to their adjacent pastures) and the model scenarios used. The use of a sampling scenario‐based approach to building predictive models shows promise for monitoring changes in tree plantings, following reforestation. The approach could also be readily adapted to other contexts where sampling effort for predictor variables in models is a major potential limitation to model utilization. This study demonstrates the benefit of exploring scenarios of data availability during modelling and will be especially valuable where the sampling effort differs greatly among variables. Copyright © 2016 John Wiley & Sons, Ltd.
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title_short	Predicting Carbon Stocks Following Reforestation of Pastures: A Sampling Scenario‐Based Approach for Testing the Utility of Field‐Measured and Remotely Derived Variables
url	http://dx.doi.org/10.1002/ldr.2585 http://onlinelibrary.wiley.com/doi/10.1002/ldr.2585/abstract http://search.proquest.com/docview/1889683843
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up_date	2024-07-03T13:37:28.583Z
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