A critical issue in the context of food policy in Indonesia is the accuracy of crops statistics, particularly rice and maize. In 2018, Statistics Indonesia (BPS), in collaboration with the Indonesian Agency for Assessment and Application of Technology (BPPT), successfully implemented the area sampling frame (ASF) method to improve the accuracy of paddy harvested area estimation, which previously was estimated by conventional methods, mainly by the human eye (‘eye-estimate’). The achievement has encouraged BPS to replicate the method to estimate the harvested area of maize, for which there were indications it suffered from overestimation. In 2019, BPS initiated a pilot project on the implementation of the ASF for maize. One of the most challenging aspects in replicating the ASF method for maize is the frame construction. This issue arises due to insufficient spatial information on land that is specifically dedicated to maize cultivation. To address this challenge, BPS constructed the frame by making use of different sources of spatial information. This paper provides a comprehensive look at the development of the ASF for maize statistics. The discussion in this paper covers two main issues, namely the methodology applied and the business process of data collection.
A new land cover classification based stratification method for area sampling frame construction