Union Budget wants digital farming; this 30-year-old Punjab farmer is already doing it

The Union Budget’s emphasis reflects a growing recognition that agriculture today depends not only on MSP and inputs, but on timely, accurate and local weather intelligence. Climate volatility—hailstorms, heat stress, dense fog, unseasonal rain and flash floods—now directly determines farm incomes. Yet centralised, generic forecasts often fail to capture region-specific micro-conditions.

Even the India Meteorological Department (IMD) faces limitations. Punjab has only about 30–35 automatic weather stations (AWS), many with limited public access or functionality. During last year’s floods, IMD forecasts for dam catchment areas underestimated rainfall by several multiples, exposing the urgent need for more precise, localised prediction systems, a senior IMD official acknowledged.

This is precisely the gap Baljinder’s work addresses. A farmer predicting for farmers, Baljinder Singh cultivates seven acres himself. Without government backing, he has created a Punjab-specific digital weather and risk advisory network, issuing alerts in simple Punjabi via Facebook, Instagram, and YouTube.

Today, his platform reaches 2.25 lakh followers on Facebook, 1.78 lakh on Instagram, and a rapidly growing YouTube audience—farmers who often listen to advisories while working in their fields.

The alerts cover a wide range of risks: unseasonal rain during sowing and harvesting, hailstorms and winter blizzards, cyclonic conditions, dense fog affecting crops, heat stress and cold waves, lightning and short-duration intense rainfall during the monsoon. The platform provides nowcasts for the next 3–5 hours, mid-range forecasts, and long-term forecasts, enabling farmers to make both immediate and planned decisions.

On June 18, 2025, Baljinder warned—several days in advance—that monsoon intensity would sharply increase, potentially flooding 5–7 districts of Punjab, even before official agencies declared the monsoon’s arrival. The forecast later proved accurate.

How he predicts: ground data meets global models

Unlike generic forecasts, Baljinder’s predictions combine ground-level data with advanced global analysis. Punjab’s lack of dense weather infrastructure pushed him to act independently. Over the years, he has invested his own money to install private AWS in open, pollution-free rural locations. Each unit—powered by solar panels and equipped with rain gauges, air-pressure sensors and mobile data transmission—costs him Rs 20,000–25,000.

These stations provide hyper-local readings, which he integrates with supercomputer-based global weather models, satellite and cloud-motion analysis, European lightning radar systems, and ocean temperature and pressure patterns. This combination allows nowcasting (3–5 hours ahead) and 10–15 day medium-range forecasts—exactly the time window farmers need for operational decisions. Today, Baljinder is supported by a small team of fellow farmers who help maintain stations and relay local observations, gradually expanding coverage across districts.

How it all began: One saved crop

The journey began in November 2017, during the wheat sowing season. At 22, Baljinder analysed weather patterns before sowing and warned his father to delay wheat sowing by a few days due to an approaching spell of heavy rain. Most farmers in the village had already sown their wheat. Days later, unseasonal rainfall damaged freshly sown fields, forcing re-sowing and heavy losses. Baljinder’s family escaped unscathed.

Word spread quickly. Fellow farmers began approaching him with a simple question: “If you know this in advance, why don’t you tell us too?” What started as curiosity soon turned into commitment.

From passion to platform

Baljinder’s fascination with weather dates back to childhood, when he watched his grandfather predict rain simply by observing wind direction and atmospheric signs. After completing his BSc in 2017, and unable to pursue formal studies in meteorology due to family constraints, he continued learning independently—studying meteorology online, analysing satellite images and global data.

That same year, he quietly launched a Facebook page—“Mausam Punjab Da.” For nearly three years, he kept his identity hidden, focusing solely on accuracy and clarity in Punjabi. As the page began receiving nearly 95 per cent positive feedback, followers urged him to reveal his name and identity.

Language: the real innovation

“One of the biggest barriers to digital adoption is language,” Baljinder says. From the start, his advisories avoided technical jargon, charts or English terminology.

“They don’t read forecasts,” he explains. “They listen.” That simple decision turned complex weather science into a practical, everyday farm tool.

From relief to prevention

Punjab routinely witnesses massive donations after floods and disasters—but little investment in prevention. “Forecasting is the first line of defence,” Baljinder says. “If farmers know the risk ten days in advance, losses can be reduced drastically.”

His personal investment has grown from Rs 20,000 initially to nearly Rs 3–4 lakh today. Ironically, his social-media earnings remain modest. In September last year, when his reach touched nearly 10 million, his Facebook earnings were around Rs 35,000. By January this year, despite a reach of about 1 million, his monthly earnings fell to just Rs 8,000–9,000. Whatever he earns from social media—or saves from farming—goes straight back into installing AWS units.

What he needs now

During Union Agriculture Minister Shivraj Singh Chouhan’s visit to Ludhiana a few months ago, Baljinder, along with other progressive farmers, met him and sought support for expanding the AWS network across Punjab. Baljinder believes that while donations flow generously after disasters, investing in preventive systems beforehand could significantly reduce damage.

“If resources are spent before disasters strike, the impact can be avoided to a large extent,” he says. If backed, Baljinder’s model could evolve from a passionate prototype into a state-wide safety net for farmers.

Pargat Singh, another progressive farmer who runs a large digital platform focused on agriculture-related activities and cropping systems, and is also the author of two books on agriculture, says he regularly follows Baljinder’s weather advisories.

For now, he observes, Punjab’s most effective early-warning system still runs on a farmer’s phone—and on his belief that prevention is far better than compensation.