Estimating the Global Distribution of Field Size using Crowdsourcing

Lesiv M, Laso Bayas JC, See L, Dürauer M, Dahlia D, Durando N, Hazarika R, Sahariah PK, et al. (2019). Estimating the Global Distribution of Field Size using Crowdsourcing. Global Change Biology 25 (1): 174-186. DOI:10.1111/gcb.14492.

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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. However, both have limitations, e.g. automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural 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 the most accurate global field size map to date 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 up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. 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, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture.

Item Type: Article
Uncontrolled Keywords: field size, crowdsourcing, visual interpretation, environmental changes, food security
Research Programs: Ecosystems Services and Management (ESM)
Depositing User: Luke Kirwan
Date Deposited: 22 Nov 2018 09:51
Last Modified: 17 Dec 2018 07:22
URI: http://pure.iiasa.ac.at/15558

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