Can India Boost Silk Production To 60,000 Tonnes By 2030-31?
On Sunday, Union Minister of Textiles, Giriraj Singh said that the government is setting a target to escalate the country’s silk production to 60,000 tonnes by 2030-31 and a resolution of making elevate India on the first position globally by in the silk production by 2028-29. Notably, production of silk in India has increased to 42,000 tonnes at at present, an increase from 26,000 tonnes in 2014, the minister said further. The minister had also participated in the 77th Foundation Day Programme of the Central Silk Board in Benagluru. He stated that the Central government is committed to the expansion of the sericulture sector of the country and the economic progress of silk farmers. Revealing about the aspiration of raising the annual income of farmers to Rs 10 lakh to Rs 12 lakh, he also said that the target would be achieved by improving the production of mulberry leaves and increasing cocoon rearing cycles.
What did the Minister state further about Silk production of India?
Focusing on the new innovations from scientists, hybrid varieties and modern machinery must reach farmers directly, Singh further stated that the demand for Indian silk is rising in the international market at a time which China’s production is declining. In addition, the minister said that boosting the entire value chain from sericulture to reeling and weaving, has taken the country’s textile market to $300 billion dollars and ensured that minimum economic security for weavers and the working class are the primary priorities of the govt.
What did the Padmini Singla say about Silk production target?
Padmini Singla, Joint Secretary, Ministry of Textiles and Vice Chairperson of CSB, highlighted the strategic process of the sericulture sector and asserted that an ambitious target has been set to produce 60,000 metric tonnes of silk in the country by 2030-31. Not only this, but to achieve this target, high-yielding silkworm hybrids and machinery are being introduced, with integrating AI, GIS and machine learning technologies to inspect quality and improve production capacity.
