Estimating the global distribution of field size using crowdsourcing

Area of Science:

• Agricultural Science
• Geospatial Analysis
• Crowdsourcing Methodologies

Background:

• Accurate data on agricultural field sizes is lacking globally, hindering food production assessments.
• Existing methods like remote sensing and census data have limitations in scale and spatial resolution.
• Smallholder farms play a substantial role in global food security, yet their exact contribution is underquantified.

Purpose of the Study:

• To develop a novel, crowdsourced approach for quantifying and mapping global agricultural field sizes.
• To create the most accurate global map of agricultural field sizes to date.
• To estimate the prevalence of different farm sizes at global, continental, and national levels.

Main Methods:

• Utilized a crowdsourcing campaign via the Geo-Wiki application in June 2017.
• Collected field size data from 130,000 unique locations globally through visual interpretation of high-resolution satellite imagery.
• Developed a unique approach to quantify and map agricultural field size using crowdsourced data.

Main Results:

• Generated the most accurate global field size map available.
• Estimated that smallholder farms occupy up to 40% of global agricultural areas.
• Found potentially more smallholder farms than previous estimates of 12% and 24%.

Conclusions:

• Crowdsourcing provides a viable and accurate method for global agricultural field size mapping.
• The findings highlight the significant, underestimated contribution of smallholder farms to global agriculture.
• The open-access map and data support agricultural modeling, policy, and Sustainable Development Goals.