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Plant Science Today (PST)

Research Articles

Vol. 13 No. sp5 (2026): Recent Advances in Agriculture

Long-term CERES-Maize simulations for optimising maize sowing, irrigation, phenology and yield in southern Telangana

DOI
https://doi.org/10.14719/pst.9686
Submitted
29 May 2025
Published
24-07-2026

Abstract

Maize (Zea mays L.) is a critical staple crop globally and optimising its phenology and yield under varying management practices is essential for food security, particularly in semi-arid regions such as southern Telangana, India. This study utilised the CERES-Maize model within decision support system for agrotechnology Transfer (DSSAT) v4.7 to simulate maize phenology and grain yield under different sowing dates and irrigation levels during the kharif and rabi seasons of 2016–2017 at the Agricultural Research Institute (ARI), Rajendranagar, Hyderabad. The model was calibrated using field data from four sowing dates and irrigation regimes for the hybrid DHM-117 and validation was performed across all treatments. A 35-year sensitivity analysis (1980–2015) was conducted to assess optimal sowing dates (10 June–10 August for kharif; 10 September–30 December for rabi) and irrigation levels (10–90 % depletion of available soil moisture, DASM). Results showed accurate phenology predictions (RMSE < 2 days for silking and maturity) and yield simulations (NRMSE < 10 % for grain yield). Late kharif sowing (10 August) and early rabi sowing (30 September) with moderate irrigation (30–40 % DASM) yielded the highest grain yields (8380 kg ha-1 and 10410 kg ha-1 respectively). Rainfed systems exhibited high yield variability, highlighting the need for water stress modelling. The model’s linear GDD approach underestimated phenological responses under high temperatures, suggesting non-linear functions for improved accuracy. These findings underscore the model’s utility for optimising maize management and adapting to climate variability in semi-arid environments, with implications for different maize production systems.

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