<![CDATA[In monsoon climates, efficient irrigation for lowland rice requires adaptive 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 Keywords: Aquacrop-OSPy, fuzzy logic, internet of Things, irrigation scheduling, water-use efficiency ]]>
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