Data on agricultural field size has been collected via Geo-Wiki campaigns, where this type of data can provide some indication of agricultural practices, and it helps us to determine what types of satellite data are needed for agricultural monitoring in different parts of the world.

In the first set of Geo-Wiki campaigns ran during 2011 and 2012, volunteers were asked to identify the size of agricultural fields from very small to large when the pixel to be identified contained cropland. Examples of very small, small, medium and large fields were provided as example images to help the volunteers in classifying the field size. From the data collected, the first global map of field size was published by Fritz et al. (2015). At the time, this map formed a key input for allocating agricultural production to different farms sizes at the country level in a global study looking at the relevance of farm size and agricultural diversity to commodity and nutrient production (Herrero et al., 2017). The results showed that both small and large farms are important for food security and nutrient supply. In low- and middle-income countries, small farms are crucial for providing food and nutrients. Small and medium farms produce up to 77% of all commodities and nutrients considered in the study, which is particularly true in areas such as Sub-Saharan Africa, Southeast Asia, South Asia, and China. The majority of global micronutrients and protein are also produced in more diverse agricultural landscapes; hence, as farm sizes increase (e.g., with agricultural intensification), the diversity in production must be maintained to ensure diverse nutrient production. The role of larger farms is also crucial as they contribute to the global trade balance, which can help to alleviate food shortfalls in some countries.

The field size map used by Herrero et al. (2017) was based on a relatively small sample of around 13,000 locations globally. Thus in 2017, a second Geo-Wiki campaign was run, supported by the EU’s FP7 SIGMA project (No. 603719) and the ERC CrowdLand project (No. 617754), which was focused entirely on the collection of field size data. The aim was to improve and update the original global field size map so that it can be used for the types of policy-relevant exercises undertaken by Herrero et al. (2017). Under the direction of Dr Myroslava Lesiv, the Geo-Wiki interface was adapted to provide more user-friendly tools to estimate field size, shown in Figure 1.

SIGMA field size © IIASA
Figure 1: Screenshot of the Geo-Wiki interface showing: a) the area measuring tool; b) the actual field sizes delineated and measured using a); c) the cumulative work done by a participant; d) the main classification area, gridded; e) the button to switch between different background imagery, i.e. Google or Bing; f) buttons to select the field size categories: very large, large, medium, small, very small, or no fields; g) possible reasons to skip the current location; h) a button to display location in Google Earth; k) examples of field size estimation for training; l) a button to ask experts for help. Source of imagery: Google Maps.
Figure 2: The spatial distribution of dominant field size © IIASA
Figure 2: The spatial distribution of dominant field size

Both gamification and incentives such as Amazon vouchers and co-authorship were offered to promote participation where expert control points were employed to control the quality. The campaign ran in June 2017, and volunteers helped to collect field size data at 130K unique locations around the globe, shown in Figure 2.

A global field size map was then produced form the data collected. From this, the percentage of different field sizes was estimated, ranging from very small to very large, in agricultural areas at global, continental and national levels. The results were significant because they showed that smallholder farms occupy up to 40% of agricultural areas globally, which is much larger than current global estimates, which range from 12 to 24%. This approach clearly demonstrated the difference between estimating global field size from statistics versus a bottom-up approach to estimate field size using crowdsourcing.  

This second, larger field size data set is described in detail in a paper published in Global Change Biology (Lesiv et al. 2018) along with the global area estimation of different field sizes; the data set and a pre-print version of the paper can be accessed through IIASA’s PURE publication repository. Other data on field size can be obtained from data sets collected as part of the early Geo-Wiki campaigns.


Fritz, S., See, L. McCallum, I., You, L., Bun, A., Albrecht, F., Schill, C., Perger, C., Duerauer, M., Havlik, P., Mosnier, A., Thornton, P., Wood-Sichra, U., Herrero, M., Becker-Reshef, I., Justice, C., Hansen, M., Gong, P., Abdel Aziz, S., Cipriani, A., Cumani, R., Cecchi, G., Conchedda, G., Ferreira, S., Gomez, A., Haffani, M., Kayitakire, F., Malanding, J., Mueller, R., Newby, T., Nonguierma, A., Olusegun, A., Ortner, S., Ram, R., Rocha, J., Schepaschenko, D., Schepaschenko, M., Terekhov, A., Tiangwa, A., Vancutsem, C., Vintrou, E., Wenbin, W., van der Velde, M., Dunwoody, A., Kraxner, F. and Obersteiner, M. (2015): Mapping global cropland and field size. Global Change Biology, 21(5), 1980-1992, https://doi.org/10.1111/gcb.12838

Herrero, M., Thornton, P.K., Power, B., Bogard, J.R., Remans, R., Fritz, S., Gerber, J.S., Nelson, G., See, L., Waha, K., Watson, R.A., West, P.C., Samberg, L.H., van de Steeg, J., Stephenson, E., van Wijk, M., Havlík, P. (2017): Farming and the geography of nutrient production for human use: a transdisciplinary analysis. The Lancet Planetary Health, 1(1), e33–e42, https://doi.org/10.1016/S2542-5196(17)30007-4

Lesiv, M., Laso Bayas, J.C., See, L., Duerauer, M., Dahlia, D., Durando, N., Hazarika, R., Sahariah, P.K., Vakolyuk, M., Blyshchyk, V., Bilous, A., Perez-Hoyos, A., Gengler, S., Prestele, R., Bilous, S., Akhtar, I.H., Singha, S., Choudhury, S.B., Chetri, T., Malek, Z. et al. (2018): Estimating the global distribution of field size using crowdsourcing. Global Change Biology, 25(1), 174-186, https://doi.org/10.1111/gcb.14492

Data Set available in IIASA PURE:

https://pure.iiasa.ac.at/id/eprint/15526/

Publications

Lesiv, M. , Laso Bayas, J.C. , See, L. , Dürauer, M., Dahlia, D., Durando, N., Hazarika, R., Sahariah, P.K., Vakolyuk, M., Blyshchyk, V., Bilous, A., Perez-Hoyos, A., Gengler, S., Prestele, R., Bilous, S., Akhtar, I.H., Singha, K., Choudhury, S.B., 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 25 (1) 174-186. 10.1111/gcb.14492.