Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 509021
Title Scaling point/plot measurements of greenhouse gas fluxes, balances and intensities to whole-farms and landscapes
Author(s) Rosenstock, T.S.; Rufino, Mariana; Chirinda, N.; Bussel, L.G.J. van; Reidsma, P.; Butterbach-Bahl, K.
Source In: Methods for Measuring Greenhouse Gas Balances and Evaluating Mitigation Options in Smallholder Agriculture / Rosenstock, T.S., Rufino, M.C., Butterbach-Bahl, K., Wollenberg, E., Richards, M., Springer - ISBN 9783319297927 - p. 175 - 188.
DOI http://dx.doi.org/10.1007/978-3-319-29794-1_9
Department(s) Laboratory of Geo-information Science and Remote Sensing
Plant Production Systems
PE&RC
Environmental Systems Analysis Group
Publication type Peer reviewed book chapter
Publication year 2015
Abstract Measurements of nutrient stocks and greenhouse gas (GHG) fluxes are typically collected at very local scales (<1 to 30 m2) and then extrapolated to estimate impacts at larger spatial extents (farms, landscapes, or even countries). Translating point measurements to higher levels of aggregation is called scaling. Scaling fundamentally involves conversion of data through integration or interpolation and/or simplifying or nesting models. Model and data manipulation techniques to scale estimates are referred to as scaling methods. In this chapter, we first discuss the necessity and underlying premise of scaling and scaling methods. Almost all cases of agricultural GHG emissions and carbon (C) stock change research relies on disaggregated data, either spatially or by farming activity, as a fundamental input of scaling. Therefore, we then assess the utility of using empirical and process-based models with disaggregated data, specifically concentrating on the opportunities and challenges for their application to diverse smallholder farming systems in tropical regions. We describe key advancements needed to improve the confidence in results from these scaling methods in the future.
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