The DSSAT CANEGRO model was calibrated and verified using field experimental data from five Tamil Nadu Agroclimatic Zones (1981–2022). The genetic coefficients of the sugarcane cultivar (CO-86032) were calculated. R² obtained between measured and simulated stalk fresh mass was 0.9 with the nRMSE (0.01) and RMSE (1.6) and R² between measured and simulated sucrose mass was 0.9 with the nRMSE (0.16) and RMSE (1.2). For yield R² obtained between measured and simulated was 0.9 with the nRMSE (0.01) and RMSE (1.6). As a result, the CANEGRO model may be used to mimic the phenology and yield features of the sugarcane cultivar in Tamil Nadu's Agro Climatic Zones. Temperature increases in Agro Climatic Zones resulted in varying yield reductions, with 2 °C increases causing a 3% loss, 3 °C increases 5%, and 4 °C increases 9%. The Water Requirement rose throughout all of the ACZ due to the high temperature, but to differing degrees. A 2 °C increase often results in an average 4% increase in the WR. 3 °C rise in temperature increased WR to 9% and WR rose by 13% when the temperature was raised by 4 °C.

Rainfall is one of the most important climatic variables that determine the spatial and temporal patterns of climate variability of a region, which also provides useful information for the planning of water resources, agricultural production, and others. Climate change is one of the most significant worldwide issues talked among scientists and researchers, and one of the consequences of climate change is the alteration of rainfall patterns. 'India's population and the economy is linked to climate-sensitive activities, including rainfed agriculture and excess climate anomalies, deficient and flooded rainfall years have a dramatic impact on the economy as well as on the living conditions of the inhabitants of the affected region. An understanding of current and historical trends and variation is inevitable to her future development, especially in agricultural and hydrological sectors. In the present study, historical weather data for 33 years (1981-2013) was analyzed for rainfed cropping season (September - December) to understand the climatic variability in the Kanyakumari district of Tamil Nadu. The maximum daily air temperature increased on average by 0.02°C per year, whereas minimum daily air temperature remained constant during the rainfed cropping season. The high rainfall zone receives an annual and rainfed cropping average rainfall of 1307and 672 mm, respectively. Analysis of rainfall during rainfed cropping period over 33 years showed ten years had standard RF, nine years had deficit rainfall, six years had below standard RF, one year had above standard RF and seven years had excess RF. Analysis indicates that the deficit condition prevailed in every alternate year in recent decades. The onset of rainfed cropping season varied over the years (1981-2013), 13 years had onset in the slot from 1^st to 5^th  September, and in others, years onset occurred between 6 and 30^th September. Cessation also had a variation over 33 years and 16 years had cessation from 26 to 31^st December while remaining years had cessation in the period of 1-25^th December. LGP ranged from 57 to 143 days, with an average LGP of 106 days. Dry spell varied from 3 to 12 days with the mean of 6 days, and wet spell varied from 2 to 8 days with an average of 5 days.

This research uses the Markov Chain Model to analyze dry and wet spells in Telangana, India’s Adilabad district. The research intends to provide significant insights for agricultural planning in the region, which is critical considering that agriculture is the foundation of Indian economy. The study emphasizes the need to understand and effectively exploit natural resources, particularly rainfall, for the improvement and sustainability of rainfed agriculture. According to the calculation, there is a 70% chance of two consecutive wet weeks throughout the monsoon season (24th SMW to 40th SMW), resulting in about 17 weeks of monsoon rain in the Adilabad area. This enables the successful development of short-duration crops like rice. According to the study, the Markov Chain Model is effective for simulating the long-term frequency behavior of wet or dry spells, providing a comprehensive understanding of rainfall patterns and their impact on agriculture in the Adilabad District