Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
Methods for the quantification of GHG emissions at the landscape level for developing countries in smallholder contexts
Environmental Research Letters, Volume 8, No. 1, Article 015019, Year 2013
Notification
URL copied to clipboard!
Description
Landscape scale quantification enables farmers to pool resources and expertise. However, the problem remains of how to quantify these gains. This article considers current greenhouse gas (GHG) quantification methods that can be used in a landscape scale analysis in terms of relevance to areas dominated by smallholders in developing countries. In landscape scale carbon accounting frameworks, measurements are an essential element. Sampling strategies need careful design to account for all pools/fluxes and to ensure judicious use of resources. Models can be used to scale-up measurements and fill data gaps. In recent years a number of accessible models and calculators have been developed which can be used at the landscape scale in developing country areas. Some are based on the Intergovernmental Panel on Climate Change (IPCC) method and others on dynamic ecosystem models. They have been developed for a range of different purposes and therefore vary in terms of accuracy and usability. Landscape scale assessments of GHGs require a combination of ground sampling, use of data from census, remote sensing (RS) or other sources and modelling. Fitting of all of these aspects together needs to be performed carefully to minimize uncertainties and maximize the use of scarce resources. This is especially true in heterogeneous landscapes dominated by smallholders in developing countries. © 2013 IOP Publishing Ltd.
Authors & Co-Authors
Milne, Eleanor
United States, Fort Collins
Colorado State University
United Kingdom, Leicester
University of Leicester
Neufeldt, Henry
Kenya, Nairobi
World Agroforestry Centre
Rosenstock, Todd S.
Kenya, Nairobi
World Agroforestry Centre
Smalligan, Michael J.
United States, East Lansing
Michigan State University
Cerri, Carlos Eduardo Pellegrino
Brazil, Sao Paulo
Universidade de São Paulo
Malin, Daniella
United States, Hartland
Sustainable Food Lab
Easter, Mark J.
United States, Fort Collins
Colorado State University
Bernoux, Martial
France, Marseille
Ird Institut de Recherche Pour le Developpement
Ogle, Stephen M.
United States, Fort Collins
Colorado State University
Casarim, Felipe M.
United States, Arlington
Winrock International
Pearson, Timothy R.H.
United States, Arlington
Winrock International
Bird, David Neil
Austria, Graz
Joanneum Research Forschungsgesellschaft Mbh
Steglich, Evelyn M.
United States, Temple
Blackland Research and Extension Center
Ostwald, Madelene
Sweden, Linkoping
Linköpings Universitet
Denef, Karolien
United States, Fort Collins
Colorado State University
Paustian, Keith H.
United States, Fort Collins
Colorado State University
Statistics
Citations: 42
Authors: 16
Affiliations: 11
Identifiers
Doi:
10.1088/1748-9326/8/1/015019
e-ISSN:
17489326
Research Areas
Environmental