AI Weather Forecasts May Help Farmers Combat Climate Risks, But Bring New Concerns

AI is changing agriculture by helping farmers predict weather, manage crops, and streamline operations, however, high costs, social inequalities, and environmental risks mean it also comes with serious challenges

Every planting decision made by farmers involves multiple risks, which are becoming more severe as a result of climate change, as noted in a new analysis by The Conversation (TC).

Weather stands as a major risk factor, harming both agricultural production and farmers’ financial stability. TC gives the examples of how a delayed monsoon season compels South Asian rice farmers to either start over with new plantings or change their agricultural production, resulting in lost time and income.

This means that accessing reliable and timely weather forecasts can help farmers optimize their planting schedules and fertilizer usage. However, TC argues that many low- and middle-income nations face significant challenges accessing reliable forecasts since the technology tends to be very expensive.

A new wave of AI-powered weather forecasting models has the potential to change this divide. AI models can deliver accurate, localized predictions at a fraction of the computational cost of conventional physics-based models.

AI allows national meteorological agencies in developing countries to provide farmers with timely, localized information about changing rainfall patterns.

Unlike traditional models, which require expensive supercomputers and focus on temperate regions, AI models can run on laptops and provide forecasts globally.

TC reports that new systems such as Pangu-Weather and GraphCast demonstrate equivalent or superior performance to leading physics-based models for temperature forecasts. Once trained, AI models produce results within minutes rather than hours, enabling farmers to make swift, informed decisions.

The challenge is tailoring forecasts to real-world needs. “To unlock its full potential, AI forecasting must be connected to the people whose decisions it’s meant to guide,” TC notes.

Organizations like AIM for Scale, together with international entities, train users and create agricultural decision-focused forecasts for governments. In India, accurate monsoon forecasts helped farmers select optimal planting strategies, improving investments and reducing risk.

AI weather forecasting is now at a critical stage, and with proper support, low- and middle-income nations can provide farmers with essential timely information.

AI technology also drives significant changes beyond weather prediction. Tavant implements AI solutions that enhance farm management, supply chains, and sales operations.

Its AI Agent accelerators, developed with Microsoft Copilot Studio, include ‘Sales Assistant’, which lets farmers purchase seeds, fertilizers, and other supplies via email or messaging, and ‘Virtual Agronomist’, which provides AI-based real-time crop guidance.

Emerging tools such as MIT’s robotic pollinators and the University of Sydney’s SwagBot complement these solutions, illustrating a sustainable, high-tech agricultural future.

Recent research identifies three major AI-related issues: predictive dissonance between models, techno-indecisiveness causing decision delays, and readiness deficit from insufficient preparedness for AI disruptions. Overreliance can lead to poor management, including excessive fertilizer use, which harms soil health and long-term productivity.

Another scientific review reported that high costs prevent small farms from accessing AI, automation threatens jobs, and corporate control of data can create inequities. Additionally, the researchers point out that socially, AI can deepen digital divides, perpetuate biases, and erode traditional farming practices.

Furthermore, the research points out that ethical concerns include environmental damage and animal welfare, while complex algorithms make transparency difficult.

Addressing these risks requires equitable access, digital training, bias mitigation, data governance, and ethical guidelines for sustainable AI adoption.