For Assam tea, erratic climate and stagnant prices present a crisis, PgII

Key Highlights:

• Assam's tea industry, a vital economic sector employing over 12 lakh workers, faces a crisis due to erratic climate patterns and stagnant prices.
• Climate change is causing extended dry spells, abrupt rainfall, and rising temperatures, leading to unpredictable tea yields and stressed tea plants.
• Tea prices have only increased by 4.8% annually in the last three decades, failing to keep pace with inflation and rising production costs.
• A study projects that by 2050, key tea-growing regions in Assam may become less suitable due to rising temperatures and altered rainfall patterns.

Detailed Insights:

• The traditional climate cycle in Assam is becoming increasingly uncertain, with persistent heat and delayed rainfall impacting tea cultivation.
• Rising temperatures above 35º C hinder nutrient absorption in tea plants, making them vulnerable to pests and diseases.
• Climate-resilient practices such as using seed-grown varieties, soil conservation, and agroforestry are being encouraged to adapt to the changing climate.
• Economic diversification into other sectors like fruits, spices, and tourism can help cushion the tea industry against climate risks.
• The tea tribes, a significant political constituency, are likely to raise concerns about climate-driven hardships in the upcoming 2026 State elections.
• The average minimum temperature in Assam has risen by 1º C in the last 90 years, with the region losing around 200 mm of rainfall a year during this time.
• Multistakeholder programmes like ‘trustea’ are promoting sustainable practices and integrated pest management to build climate resilience in the tea supply chain.

Scientific/Technical Concepts Involved:

• Global Circulation Models: Climate models used to project future climate scenarios based on various factors.
• RCP (Representative Concentration Pathway): Greenhouse gas concentration trajectories used in climate modeling by the IPCC.
• MaxEnt: A species distribution model used to predict the potential geographic distribution of species.