Estimating the global distribution of field size using crowdsourcing

M Lesiv, JC Laso Bayas, L See, M Duerauer, D Dahlia, N Durando, R Hazarika, P.K. Sahariah, M Vakolyuk, V Blyshchyk, A Bilous, A Pérez-Hoyos, S Gengler, R. Prestele, S Bilous, I.H. Akhtar, K Singha, SB Choudhury, T Chetri, Ziga Malek & 13 others K Bungnamei, A Saikia, D Sahariah, W Narzary, O Danylo, T Sturn, M Karner, I. McCallum, D Schepaschenko, E. Moltchanova, D Fraisl, I Moorthy, Steffen Fritz

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

There is an 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, for example, 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 130 K 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 modeling, 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.
LanguageEnglish
Pages174-186
Number of pages13
JournalGlobal Change Biology
Volume25
Issue number1
Early online date22 Nov 2018
DOIs
Publication statusPublished - Jan 2019

Fingerprint

Farms
Remote sensing
smallholder
Satellite imagery
Nutrition
farm
Agriculture
remote sensing
Sustainable development
census
agricultural land
distribution
farm size
hunger
alternative agriculture
food production
food security
satellite imagery
nutrition
comparative study

Keywords

  • crowdsourcing
  • environmental changes
  • field size
  • food security
  • visual interpretation

Cite this

Lesiv, M., Laso Bayas, JC., See, L., Duerauer, M., Dahlia, D., Durando, N., ... Fritz, S. (2019). Estimating the global distribution of field size using crowdsourcing. Global Change Biology, 25(1), 174-186. https://doi.org/10.1111/gcb.14492
Lesiv, M ; Laso Bayas, JC ; See, L ; Duerauer, M ; Dahlia, D ; Durando, N ; Hazarika, R ; Sahariah, P.K. ; Vakolyuk, M ; Blyshchyk, V ; Bilous, A ; Pérez-Hoyos, A ; Gengler, S ; Prestele, R. ; Bilous, S ; Akhtar, I.H. ; Singha, K ; Choudhury, SB ; Chetri, T ; Malek, Ziga ; Bungnamei, K ; Saikia, A ; Sahariah, D ; Narzary, W ; Danylo, O ; Sturn, T ; Karner, M ; McCallum, I. ; Schepaschenko, D ; Moltchanova, E. ; Fraisl, D ; Moorthy, I ; Fritz, Steffen. / Estimating the global distribution of field size using crowdsourcing. In: Global Change Biology. 2019 ; Vol. 25, No. 1. pp. 174-186.
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abstract = "There is an 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, for example, 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 130 K 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 modeling, 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.",
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Lesiv, M, Laso Bayas, JC, See, L, Duerauer, M, Dahlia, D, Durando, N, Hazarika, R, Sahariah, PK, Vakolyuk, M, Blyshchyk, V, Bilous, A, Pérez-Hoyos, A, Gengler, S, Prestele, R, Bilous, S, Akhtar, IH, Singha, K, Choudhury, SB, Chetri, T, Malek, Z, Bungnamei, K, Saikia, A, Sahariah, D, Narzary, W, Danylo, O, Sturn, T, Karner, M, McCallum, I, Schepaschenko, D, Moltchanova, E, Fraisl, D, Moorthy, I & Fritz, S 2019, 'Estimating the global distribution of field size using crowdsourcing', Global Change Biology, vol. 25, no. 1, pp. 174-186. https://doi.org/10.1111/gcb.14492

Estimating the global distribution of field size using crowdsourcing. / Lesiv, M; Laso Bayas, JC; See, L; Duerauer, M; Dahlia, D; Durando, N; Hazarika, R; Sahariah, P.K.; Vakolyuk, M; Blyshchyk, V; Bilous, A; Pérez-Hoyos, A; Gengler, S; Prestele, R.; Bilous, S; Akhtar, I.H.; Singha, K; Choudhury, SB; Chetri, T; Malek, Ziga; Bungnamei, K; Saikia, A; Sahariah, D; Narzary, W; Danylo, O; Sturn, T; Karner, M; McCallum, I.; Schepaschenko, D; Moltchanova, E.; Fraisl, D; Moorthy, I; Fritz, Steffen.

In: Global Change Biology, Vol. 25, No. 1, 01.2019, p. 174-186.

Research output: Contribution to JournalArticleAcademicpeer-review

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AU - Lesiv, M

AU - Laso Bayas, JC

AU - See, L

AU - Duerauer, M

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AU - Durando, N

AU - Hazarika, R

AU - Sahariah, P.K.

AU - Vakolyuk, M

AU - Blyshchyk, V

AU - Bilous, A

AU - Pérez-Hoyos, A

AU - Gengler, S

AU - Prestele, R.

AU - Bilous, S

AU - Akhtar, I.H.

AU - Singha, K

AU - Choudhury, SB

AU - Chetri, T

AU - Malek, Ziga

AU - Bungnamei, K

AU - Saikia, A

AU - Sahariah, D

AU - Narzary, W

AU - Danylo, O

AU - Sturn, T

AU - Karner, M

AU - McCallum, I.

AU - Schepaschenko, D

AU - Moltchanova, E.

AU - Fraisl, D

AU - Moorthy, I

AU - Fritz, Steffen

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Y1 - 2019/1

N2 - There is an 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, for example, 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 130 K 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 modeling, 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.

AB - There is an 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, for example, 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 130 K 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 modeling, 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.

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Lesiv M, Laso Bayas JC, See L, Duerauer M, Dahlia D, Durando N et al. Estimating the global distribution of field size using crowdsourcing. Global Change Biology. 2019 Jan;25(1):174-186. https://doi.org/10.1111/gcb.14492