Forecasting Rice Status for a Food Crisis Early Warning System Based on Satellite Imagery and Cellular Automata in Malang, Indonesia

Sujarwo, Aditya Nugraha Putra, Raden Arief Setyawan, Heitor Mancini Teixeira, Uma Khumairoh

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

The increasing population in Indonesia is challenging rice production to feed more people while rice fields are being converted to other land-use land cover (LULC). This study analyzes land use in 2015, 2017, 2019, 2021, and 2025 using an artificial neural network cellular automata (ANN-CA) and rice data from Statistics Indonesia to predict future rice status in Malang Districts, Indonesia. The primary LULC change driver was the rapid conversion of rice fields, which had their area reduced by 18% from 2019 to 2021 and 2% from 2021 to 2025. Rice fields are mainly being converted to settlements and buildings. The Kappa coefficient of simulation achieved 88%, with 91 accuracies. The model predicted a 2% lower rate of rice production but a 3% higher demand in 2025 compared to 2021. Lower rice production and higher demand are predicted to reduce the rice surplus by 57% in 2025, suggesting that the Malang district might lower its supply of rice to other areas by 2025. Our study provides a food crisis early warning system that decision makers can use to form adequate strategic plans and solutions to combat food insecurity.
Original languageEnglish
Article number8972
Pages (from-to)1-14
JournalSustainability
Volume14
Issue number15
DOIs
Publication statusPublished - 1 Aug 2022

Keywords

  • agricultural production
  • artificial neural network
  • food security
  • land use changes

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