Abstract
This study presents a comprehensive assessment of paddy cropping efficiency and production zones across 33 districts in Telangana, India, over two decades (2001–2020). The research integrates production zone dynamics, trend analysis, and machine learning classification to reveal significant agricultural and spatial development. Over the two-decade period, districts engaged in structured paddy cultivation increased dramatically. In Decade 1 (2001-2010), only 9 districts engaged in structured paddy cultivation, while 24 remained in the “Others” category. By Decade 2(2011-2020), active production zones expanded significantly, with Primary zone districts doubling from 3 to 6, and both Secondary and Tertiary zones increasing more than fourfold, from 3 to 13 each. Cropping efficiency zones also underwent major transformation: High Intensity Cropping Zone (HICZ) districts rose from 5 to 17, while Highly Inefficient districts dropped from 23 to zero. The Mann-Kendall trend test revealed a decline in increasing trends within the Most Efficient Cropping Zone (MECZ), from 5 districts in Decade 1 to 3 in Decade 2, while decreasing trends rose from 3 to 5. A machine learning model trained on 395 districts achieved perfect classification performance, with precision, recall, and F1 Scores precision, recall, and F1-score of 1.00 for both HICZ and NECZ. These findings highlight a positive trajectory in agricultural engagement and efficiency, while emphasizing the need for continued support in moderately performing zones and validation of predictive tools for broader application.