Geography

Document Type

Article

Abstract

Farm size is a key characteristic of agricultural systems, closely linked to farming and land management practices. However, data on farm size are often difficult to obtain, especially in smallholder-dominated regions, and where available, they are rarely spatially explicit. This study examines the use of field size as a proxy for farm size, aiming to identify field size thresholds that can distinguish farms of different sizes. Using household-level data from Zambia’s Crop Forecast Survey, we applied both continuous and categorical methods to quantify the field-to-farm size relationship. Non-parametric Theil–Sen regression revealed a generally linear relationship between log-transformed field and farm sizes, particularly at the survey enumeration area scale, where farm size is proportional to field size (slope ≈ 1) and field size alone predicts farm size with a median bias of 0.017 ha. A Gaussian Naive Bayes classifier was also developed to assign fields to three categories: very small (A: (Formula presented) (Formula presented)  ha), small (B: 2–5 ha), and medium (C: 5–20 ha), and a coarser two-class scheme (S: (Formula presented) (Formula presented)  ha; M: 5–20 ha) that has been applied in previous studies. The Gaussian model identified field size thresholds at 0.58–0.6 ha (A–B) and 1.18–1.69 ha (B–C), depending on the prior farm size distribution. For the S–M scheme, thresholds ranged from 0.96–1.4 ha. Using these thresholds, national classification achieved F1 scores of 0.78, 0.59, and 0.77 for A, B, and C, respectively, with B more prone to misclassification. The two-class model achieved 0.87 and 0.88 for S and M, indicating more robust classification performance under a coarser scheme. Kernel density analysis shows that field-size distributions within farm categories and the separability of these distributions, vary across regions, with the underlying factors responsible for these differences remaining an open question for future research. These results demonstrate the potential of field size as a spatial proxy for farm size to support remote sensing based farming system classification in data-scarce regions. © 2026 The Author(s). Published by IOP Publishing Ltd.

Publication Title

Environmental Research: Food Systems

Publication Date

3-2026

Volume

3

Issue

1

ISSN

2976-601X

DOI

10.1088/2976-601X/ae490a

Keywords

farm size, field size, remote sensing, smallholders, Zambia

Cross Post Location

Student Publications

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Included in

Geography Commons

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.