Estimating the Global Distribution of Field Size using Crowdsourcing

Lesiv M, Bayas JCL, See L, Duerauer M, Dahlia D, Durando N, Hazarika R, Sahariah PK, et al. (2018). Estimating the Global Distribution of Field Size using Crowdsourcing.

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Project: Stimulating Innovation for Global Monitoring of Agriculture and its Impact on the Environment in support of GEOGLAM (SIGMA, FP7 603719), Harnessing the power of crowdsourcing to improve land cover and land-use information (CROWDLAND, FP7 617754),

Abstract

There is increasing evidence that smallholder farms contribute substantially to food production globally yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used but both have limitations, e.g. limited geographical coverage by remote sensing or coarse spatial resolution when using census data. This paper demonstrates another approach to quantifying and mapping field size globally using crowdsourcing. A campaign was run in June 2017 where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo-Wiki application. During the campaign, participants collected field size data for 130K unique locations around the globe. Using this sample, we have produced an improved global field size map (over the previous version) and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental and national levels. The results show that smallholder farms occupy no more than 40% of agricultural areas, which means that, potentially, there are much more smallholder farms in comparison with the current global estimate of 12%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modelling, comparative studies of agricultural dynamics across different contexts and contribute to SDG 2, among many others.

Item Type: Dataset
Uncontrolled Keywords: field size, crowdsourcing, visual interpretation, environmental changes, food security
Research Programs: Ecosystems Services and Management (ESM)
Depositing User: Luke Kirwan
Date Deposited: 12 Oct 2018 09:54
Last Modified: 12 Oct 2018 09:54
URI: http://pure.iiasa.ac.at/15526

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