Mapping Economic Returns to Agriculture for Informing Environmental Policy in the Murray–Darling Basin, Australia

Abstract

We integrate information from several disparate data sources including agricultural statistics and remote sensing to quantify and map the distribution and dynamics of agricultural returns to land and water resources from

Carbon farming via assisted natural regeneration as a cost-effective mechanism for restoring biodiversity in agricultural landscapes

“…Two main sources of uncertainty are inherent in spatial estimates of agricultural profitability: mapping uncertainty, and estimation uncertainty (Bryan et al, 2009). Mapping uncertainty emerges due to inaccuracies in the underlying land use data layer, as well as the use of NDVI mapping as a proxy for agricultural yield.…”

“…The map is a grid of profitability at full equity (PFE, $ ha À1 ) at a 1 km 2 resolution across the Australian continent, using data on production, revenues and costs for 23 irrigated and rain-fed agricultural commodities, combined with data on land use (2005/2006) and yield estimates. PFE is a measure of profit which is calculated as the difference between revenue from the sale of agricultural commodities and all fixed and variable costs (Bryan et al, 2009;Marinoni et al, 2012). The system developed by Marinoni and colleagues will enable the production of a more current map of agricultural profitability once the latest land use data set for Australia (2010/2011) is finalized.…”

Land use mapping error introduces strongly-localised, scale-dependent uncertainty into land use and ecosystem services modelling

“…Agricultural yields were derived from census data by SLA, and apportioned to cells in the land use map (Bryan et al, 2009b). To compliment this we used the Agricultural Production Systems Simulator (APSIM) to quantify crop yields (Bryan et al, 2014a(Bryan et al, , 2014bZhao et al, 2013) and crop residue availability for biofuel production (Zhao et al, 2015).…”

“…Change in land use and land cover is regarded as the single most important element of global change affecting ecosystems (Foley et al, 2005;Vitousek, 1994). Land use maps are fundamental data for mapping and modelling ecosystem services and their value (Crossman et al, 2013a(Crossman et al, , 2013bDe Groot et al, 2010;Liu et al, 2010) at all scales including local (Butler et al, 2013;Raudsepp-Hearne et al, 2010), regional (Ausseil et al, 2013;Bryan and Crossman, 2013;Bryan et al, 2009b), national (Bateman et al, 2013;Bryan et al, 2014a;Lawler et al, 2014), and global (Costanza et al, 1997;Verburg et al, 2011Verburg et al, , 2013. Global and continental scale land use maps are commonly generated by the classification of remotely-sensed imagery (Gong et al, 2013;Hansen et al, 2000;Schneider et al, 2010).…”

Use of Remote Sensing to Support Forest and Wetlands Policies in the USA

“…Remote sensing is used at regional scales to improve land use policy and decision-making, especially for agricultural and urban areas [3,4]. For example, data from AVHRR (Advanced Very High Resolution Radiometer), MODIS (Moderate Resolution Imaging Spectroradiometer) and MISR (Multiangle Imaging Spectroradiometer) satellite sensors have been used to monitor compliance with a biomass burning reduction policy implemented in 2006 in Acre, Brazil [5].…”