Season-aware irrigation for lowland rice: Aquacrop-ospy-guided rules to enhance water utilization and increase dry yields in Malang

Prakasa, Johan Ericka Wahyu ORCID: https://orcid.org/0000-0001-5571-9328, Hanani, Ajib and Utama, Shoffin Nahwa ORCID: https://orcid.org/0000-0001-9843-199X (2026) Season-aware irrigation for lowland rice: Aquacrop-ospy-guided rules to enhance water utilization and increase dry yields in Malang. Season-Aware Irrigation for Lowland Rice: Aquacrop-OSPy-Guided Rules to Enhance Water Utilization and Increase Dry Yields in Malang, 29 (1). pp. 18-23. ISSN 2615-7136

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Abstract

Strategies that respond to rainfall and soil moisture dynamics, rather than fixed schedules. This study utilized the AquaCrop-OSPy model, an open source Python implementation of FAO AquaCrop v7.1 parameterized for paddy rice on clay-loam soil to compare two irrigation rules in Malang, Indonesia, over a decade (2015–2024) across three key planting periods: rainy season (January), transitional season (March), and dry season (June). The first schedule was a rain-aware basin top-up rule (0/10/50 mm based on 3–7-day rainfall) versus a moisture-threshold rule that triggers 50/10/0 mm when the root-zone depletion exceeds 70%, between 70–90%, or less than 90% of RAW. In the rainy and transitional plantings, the moisture threshold rule reduced seasonal irrigation by 26% and 15% with no loss in yield (≤0.5% difference). During the dry-season planting, it increased yield by 11.4% with a moderate water trade-off (+21%). Consequently, water-use efficiency improved when rainfall contributed to crop demand, while targeted applications stabilized dry-season yields. These results show that simple, physiologically-based thresholds based on Aquacrop OSPy’s paddy-rice parameters provide a feasible approach to year-round rice cultivation with more efficient water usage.

Item Type:	Journal Article
Keywords:	aquacrop-ospy; fuzzy logic; internet of things; irrigation scheduling; water-use efficiency
Subjects:	07 AGRICULTURAL AND VETERINARY SCIENCES > 0701 Agriculture, Land and Farm Management > 070108 Sustainable Agricultural Development 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Johan Ericka Wahyu Prakasa
Date Deposited:	25 Sep 2026 13:58

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