Real-time monitoring enables Per Drop More Crop to deliver measurable water savings in critical groundwater blocks. Learn how IoT, AI, and satellite data drive sustainable irrigation and aquifer conservation.
Across India’s agricultural heartland, invisible lines are being crossed, not political boundaries, but the threshold of groundwater sustainability. In 2025, the Ministry of Jal Shakti released sobering data: out of 6,762 assessment units (blocks and taluks) across the country, 730 units – representing 10.80% of all blocks, have been categorized as “over-exploited,” where annual groundwater extraction exceeds the annually replenishable recharge.
For farmers and policymakers alike, this crisis presents an urgent paradox: how do we sustain agriculture and rural livelihoods when the water beneath our fields is disappearing faster than it can be replenished?
The answer lies in transforming how we use water. This is where the Per Drop More Crop (PDMC) scheme enters the narrative, a government initiative aimed at maximizing agricultural output while minimizing water waste through micro-irrigation. But PDMC’s success in critical groundwater blocks hinges on a critical enabler that most discussions overlook: real-time, data-driven monitoring.
To understand the urgency, consider this: India’s overall Stage of Ground Water Extraction (SoE), the ratio of annual extraction to annual extractable resources, stands at 59% nationally. While this average may sound manageable, it masks regional catastrophes. In critical and over-exploited blocks, farmers are drawing down aquifers faster than monsoon rains can replenish them, creating a ticking time bomb for agricultural collapse.
The consequences are already visible. In water-scarce districts, millions of farmers face erratic harvests. Groundwater levels plummet during dry months, forcing deeper drilling and higher energy costs. By 2030, unless extraction patterns change fundamentally, approximately 60% of India’s aquifers could face severe stress.
Yet paradoxically, inefficient irrigation practices persist. Traditional flood irrigation wastes up to 60% of water through evaporation and runoff, according to FAO estimates. Simultaneously, over-irrigation depletes aquifers, while under-irrigation leaves crops stressed and yields compromised. The challenge is not just how much water to apply, but when and where to apply it with precision, and that requires real-time visibility into soil, weather, crop needs, and aquifer health.
The Per Drop More Crop scheme, launched in 2015-16 as a component of the Pradhan Mantri Krishi Sinchayee Yojana (PMKSY), specifically targets critical groundwater blocks and water-scarce regions. The scheme provides substantial subsidies – 55% for small and marginal farmers, 45% for other farmers – to install micro-irrigation systems like drip and sprinkler irrigation.
The vision is compelling: by shifting from flood irrigation to precision irrigation, farmers can reduce water consumption while maintaining or even increasing yields. According to the Department of Agriculture and Farmers Welfare, PDMC aims to expand cultivable area under assured irrigation, improve on-farm water use efficiency, and enhance aquifer recharge.
Yet PDMC alone is incomplete. Handing a farmer a drip system without guidance on when to irrigate creates new problems. A farmer might irrigate on fixed schedules, missing soil moisture variations. Another might over-irrigate out of abundance caution. Without real-time data, PDMC becomes a halfway measure, efficient infrastructure serving inefficient decision-making.
Real-time monitoring becomes the critical link between PDMC infrastructure and water savings. In critical groundwater blocks, tracking soil moisture, groundwater levels, crop water stress, and aquifer recharge rates provides the visibility needed to ensure that every drop of water is used purposefully and extraction stays within sustainable limits.
Andhra Pradesh illustrates this imperative clearly. The state, which counts several critical groundwater blocks among its agricultural zones, implemented the Andhra Pradesh Water Resources Information and Management System (APWRIMS). The results tell a powerful story: groundwater levels improved by 2 meters across the state despite receiving 14% lower rainfall than normal. Through real-time data on soil moisture stress and targeted advisories, approximately 4,540 farmers in affected regions benefitted, with groundnut farmers increasing yields by 23% while conserving water.
How was this achieved? Through an integrated approach combining IoT sensors, satellite data, and AI-driven analytics to provide farmers and officers with actionable insights. When soil moisture sensors indicated critical stress, advisories were triggered. When satellite imagery revealed over-irrigation in certain command areas, water allocation was rebalanced. The system didn’t just identify problems, it enabled solutions in near real-time.
Effective real-time monitoring in critical groundwater blocks requires layering multiple data streams. A comprehensive system integrates:
In Odisha, the state’s unified water management system – GOWATER, developed by Vassar Labs, provides integrated monitoring across 13 major and 59 medium irrigation projects. The system enables real-time visibility into crop distribution, irrigation extent, and crop health across entire command areas. This unified view allows irrigation departments to identify water-stressed regions and optimize water releases, improving equitable distribution and water use efficiency across the canal command network.
In Telangana, the integration of real-time monitoring through systems like the Kaleshwaram Lift Irrigation Project, developed by Vassar Labs to provide decision support for irrigation scheduling, has enabled farmers to achieve water reductions of up to 35% while maintaining healthier crops and improved yields. These results directly support government water conservation objectives under PMKSY.
PDMC supported by real-time monitoring also enables holistic water security planning. In critical blocks, authorities can use aggregate data to design complementary interventions: artificial recharge structures in zones with poor recharge, watershed development in rainfed areas adjacent to irrigated zones, and even crop reorientation toward drought-resistant varieties where groundwater stress is acute.
Andhra Pradesh’s experience again illustrates this. By analyzing crop suitability against water availability through integrated data platforms, the state recommended shifting from water-intensive crops to horticulture. The result: approximately 1.85 lakh hectares transitioned to horticulture, significantly reducing groundwater demand while improving farmer incomes through higher-value crops.
The Per Drop More Crop scheme represents India’s commitment to sustainable agriculture. Yet its success in critical groundwater blocks depends on a critical addition: real-time monitoring and data-driven decision support. Without it, PDMC risks becoming an efficiency measure that delays, rather than prevents, groundwater depletion.
The enabling factors are increasingly in place. IoT sensor networks have become more cost-effective, open-source satellite data is freely available, and cloud computing can process vast datasets efficiently. Integration platforms can now weave together diverse information sources – soil moisture, weather, crop growth stages, groundwater levels – into farmer-friendly mobile applications that deliver timely, actionable insights.
For critical groundwater blocks, this transition is not aspirational, it is essential. As India faces the dual pressures of climate variability and population growth, the only viable path to sustainable agriculture runs through informed, data-driven water management.
PDMC provides the infrastructure. Real-time monitoring provides the intelligence. Together, they offer critical groundwater blocks a genuine path toward sustainability, preserving water for future generations while ensuring farmer prosperity today. The question is no longer whether such integration is possible, but how quickly governments and farming communities can implement it at scale.
