Citation

Bhalerao, Pramod and Kumar, Krishna and Jamadar, Irshad Sikandar and Saleel, C. Ahamed and Krishnan, Shashikumar and Khan, Sher Afghan (2026) Long-term trends and variability in sugarcane production: a five-district comparative analysis with meteorological context in Maharashtra and Karnataka, India. Scientific Reports, 16 (1). ISSN 2045-2322

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Abstract

The climate-resilient planning and policy development for major cane-growing regions depends on the understanding of sugarcane production patterns which exist over long time periods and show fluctuations at the district level. The study analyzes sugarcane production data from five Indian districts by comparing the 22 years of crop-year data which includes records from 1999 to 2021 for Ahmednagar, Solapur, and Nashik in Maharashtra and Bellary and Dharwad in Karnataka. Parametric and non-parametric methods (linear regression, Mann–Kendall test, Sen’s slope) are applied to quantify trends; period-wise summaries, structural-break detection (Pettitt test), and coefficient-ofvariation and extreme-year statistics are reported for all five districts. Ahmednagar and Nashik show significant positive yield trends; Solapur has the largest scale and lowest yield variability; Bellary has the highest mean yield (89.46 t/ha). Period-wise analysis identifies three regimes (1999–2006, 2007– 2013, 2014–2020) with rising mean yields in most districts. Yield is relatively more stable than area and production (lowest CV in yield). For Ahmednagar, combining meteorological data to identify very good crop years shows that the drought index, moisture sufficiency, heat stress days, and yield are closely related. Extreme high and low yield years can be explained by the variations in these indices. The comparative framework and the district-level evidence enable providing distinct inputs for water-risk management, climate services, and stabilization measures in the water-limited sugarcane systems of the Deccan Plateau.

Item Type:	Article
Uncontrolled Keywords:	Sugarcane, production trend
Subjects:	H Social Sciences > HF Commerce > HF3000-4055 By region or country
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	01 Jul 2026 01:26
Last Modified:	01 Jul 2026 01:26
URII:	http://shdl.mmu.edu.my/id/eprint/16170

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