Improving The Accuracy of Area Sampling Frame Estimators for Agricultural Surveys Using Unequal Clustered Segment Sampling: The Case of Indonesia

Abstract

Accurate rice production data are vital for maintaining national food security and formulating effective agricultural policies. In Indonesia, the Area Sampling Frame (KSA) method has been widely implemented to estimate rice harvest areas using segments of 300 meters×300 meters represented by nine observation points. However, this approach faces limitations, particularly the risk of undercoverage bias when estimating areas across different rice growth stages, especially if the observation points fall outside the target rice-growing regions  as population area. To address this issue, the present study introduces the Unequal Clustered Segment Sampling method as an alternative to the traditional KSA approach. The Unequal Clustered Segment Sampling method improves estimation accuracy by refining the sampling frame and excluding non-target segments, spatial points located outside actual rice-growing regions. Through a design-based estimation framework, the proposed method accounts for unequal cluster sizes, allowing a more representative depiction of field conditions. The empirical results demonstrate that the Unequal Clustered Segment Sampling method significantly reduces bias and enhances the precision of rice area estimates compared to the conventional KSA. These findings suggest that incorporating unequal clustered segment sampling designs into KSA-based surveys can yield more reliable and representative estimates, particularly in heterogeneous or fragmented agricultural landscapes.