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International articleMyanmar2025

Soil moisture sensor networks for irrigation scheduling in Myanmar's central dry zone

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Original
Published in
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Year
2025
DOI
10.48550/clmv.2026.010
Original language
English
Topic
Smart agriculture, Green transition and climate response
Published by
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Abstract

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This article evaluates the use of low-cost soil moisture sensor networks to guide irrigation scheduling in Myanmar's central dry zone. Field trial data indicate meaningful water savings without yield loss when sensor-guided scheduling replaces fixed calendar irrigation. The article discusses scaling considerations for similar dry zone contexts.

Background

Farmers in Myanmar's central dry zone have traditionally relied on fixed calendar-based irrigation schedules, an approach that often leads to either water waste or under-irrigation given highly variable soil moisture conditions.

This article evaluates whether low-cost soil moisture sensors can improve irrigation efficiency without requiring farmers to adopt complex or costly monitoring equipment.

Methods

A field trial was conducted comparing crop plots irrigated on a fixed calendar schedule against plots irrigated based on readings from low-cost soil moisture sensors installed at representative depths.

Water use and yield outcomes were recorded across two growing seasons to compare the two irrigation approaches under similar soil and crop conditions.

Key findings

Sensor-guided irrigation reduced total water use meaningfully compared with fixed calendar scheduling, while yields in sensor-guided plots remained statistically comparable to calendar-irrigated plots.

The greatest water savings occurred during periods of unexpected rainfall, when calendar-based schedules continued irrigation regardless of actual soil moisture conditions.

Policy recommendations

The article recommends piloting subsidised distribution of low-cost soil moisture sensors among farmer groups in water-stressed dry zone areas, given the observed water savings at minimal added cost.

It also recommends pairing sensor distribution with basic training on interpreting readings, since farmer understanding of sensor data was found to influence how consistently the technology was used.

Keywords:Smart agricultureGreen transition and climate response