Global dataset on mean farm size reveals important subnational variability

Farm size is a key farming system characteristic at the heart of debates on productivity, land reform, smallholder production and livelihoods, and sustainability outcomes. Several global or regional datasets on farm size exist that form the foundation of research applications (e.g. estimating the contribution of smallholders to global food production) and practice (e.g. informing rural development strategies). However, existing datasets have crucial limitations as they are built using national-level statistics—which mask important subnational variability, have limited geographic coverage, and/or are based on modeled rather than empirical data. We address these limitations by developing the first global dataset of mean farm size that is both entirely empirical and at predominantly subnational scale. Our dataset is based on census or survey data for 101 countries at the smallest subnational administrative level for which data was publicly available, and at the national level for 99 additional countries, for the target year 2000. Comparing our product to existing (quasi) global farm size datasets highlights differences in spatial coverage and scale and in subnational variability, which result in important differences in farm size patterns across the globe. Our dataset thus highlights the importance of spatially-explicit high-resolution datasets at global scale, which enable more accurate and nuanced analyses of farm size as an important driver of land and food systems for policy and research.

• Publication year

  2026

• Venue

  Environmental Research: Food Systems

• Publication date

  2026-01-28

• Fields of study

  Agricultural and Food Sciences, Physics, Economics, Environmental Science

• Identifiers
  DOI 10.1088/2976-601X/ae3e93
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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