A New Spatial-Temporal Modelling Approach for Predicting Rice Drought in Indonesia Using the Standardized Precipitation Index

Authors

DOI:

https://doi.org/10.36253/ijam-3677

Keywords:

Drought, rice, prediction, SPI3, NDVI

Abstract

Agricultural drought poses a major threat to rice production in Indonesia, highlighting the need for dynamic prediction to support timely and effective management strategies. This study aims to develop a new approach for predicting rice drought stress that incorporates the characteristics of SPI3, emphasizing onset and trends, and to evaluate the model’s accuracy in predicting rice drought. The onset of SPI3 denotes conditions at the start of the planting season, while the SPI3 trend represents the four-month gradient from planting to harvest. The Normalized Difference Vegetation Index (NDVI) derived from MODIS was utilized to validate the spatial and temporal predictions of rice drought using the Proportion Correct (PC) method. The model performs most reliably in capturing severe droughts during the dry season, with accuracies in the very high drought category ranging from 60% to 85%. Performance declines in March and August, highlighting challenges during the transitions between wet and dry seasons.  During the El Niño year, predictions aligned with observed very high drought (PC: 59–77%), whereas in the La Niña year, they matched the low drought category (PC: 72–76%). Comparable prediction accuracies in Indramayu and Bone indicate the feasibility of developing a generalized model for Indonesia’s diverse rice-producing areas. Future improvements should integrate higher-resolution data and machine learning, account for local irrigation practices, and expand validation across regions to enhance model transferability and comprehensively assess its performance.

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Published

2025-10-27

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Surmaini, E., Misnawati, Ramadhani, F., Dewi, E. R., Sarvina, Y., Syahputra, M. . R., … Aziz, A. (2025). A New Spatial-Temporal Modelling Approach for Predicting Rice Drought in Indonesia Using the Standardized Precipitation Index. Italian Journal of Agrometeorology. https://doi.org/10.36253/ijam-3677

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Copyright (c) 2025 Elza Surmaini, Misnawati, Fadhlullah Ramadhani, Elsa Rakhmi Dewi, Yeli Sarvina, Muhammad Ridho Syahputra, Woro Estiningtyas, Yayan Apriyana , Erni Susanti, Amiral Aziz

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