A harmonised gridded input dataset for simulation modelling of the major crops of South Asia

Crop modelling at regional scales depends critically on the quality and representativeness of climate, soil, crop, and management input data. Existing global products provide detailed, bias-corrected climate data even at regional scales. For other inputs, these products provide spatial consistency, but they often underrepresent the agronomic heterogeneity that shapes crop productivity across the region. This limitation is particularly important in South Asia, where fragmented landscapes, diverse farming systems, and variable management practices create strong spatial variability. This paper presents a high-resolution gridded input dataset developed specifically for process-based crop simulation modelling across South Asia. The dataset integrates soil properties, crop distribution, the extent of rainfed and irrigated areas, crop phenology, planting dates, nitrogen application rates, and representative irrigation and fertiliser schedules. It covers 10 major crops that together account for nearly 80% of the region's agricultural area. All variables are harmonised within a unified 50 × 50 km spatial framework. The dataset is designed for integration with major crop modelling platforms, including DSSAT, APSIM, and InfoCrop. It is also compatible with ISIMIP-aligned climate inputs. The dataset was developed through a systematic synthesis of regional literature, government reports, online repositories, expert knowledge, and selected global products, and was verified with stakeholders' input. This approach ensured spatial completeness and internal consistency across variables. Soil hydraulic parameters were harmonised using pedo-transfer functions. Crop and management variables represent dominant production systems and typical farmer practices circa 2019. The modular structure allows users to apply individual layers independently or recalibrate them using local observations where available. The dataset is intended primarily for sub-national to regional-scale applications. It supports assessments of crop productivity, climate sensitivity, drought response, management interventions, and adaptation planning under current and future climate conditions. Potential applications include crop insurance, climate change impact assessment, early warning systems, and evaluation of adaptation strategies across South Asia. Some uncertainty remains unavoidable, particularly in data-sparse regions and heterogeneous smallholder systems. Management variables reflect representative regional practices and should not be interpreted as prescriptive recommendations. Similarly, the 50 × 50 km spatial resolution captures broad regional patterns but cannot fully resolve local variability. Users are therefore encouraged to conduct sensitivity and uncertainty analyses and incorporate updated or locally calibrated inputs where finer-scale applications are required. These datasets are available from Zenodo with an Open Access licence.