The d-iap assesses the drought impacts on various crops which have been calibrated and included in the AquaCrop database, such as barley, cassava, cotton, dry bean, maize, paddy rice, potato, quinoa, sorghum, soybean, sugar beet, sugarcane, sunflower, teff, tomato, and wheat. The initial step involved the spatial allocation of these crops globally, based on “area harvested” data from the year 2000 at the national level, using the FAOSTAT statistical database. Although a generic cultivar was considered for each crop, some crop parameters adjustments were made among different cultivable zones, taking into account information on local cultivar types and management practices. Specifically, the following parameters were adjusted for each area based on expert knowledge and global bibliographic research: reference harvest index (HIo), plant density, and maximum canopy cover (CCx) associated with the plant density (Raes et al., 2023). Thus, a global crop database was generated for d-iap, incorporating these parameters at national level, and at subnational levels where applicable.

A key variable for conducting AquaCrop simulations is the sowing date, which varies annually and is primarily determined by precipitation patterns. Therefore, it was decided to determine it dynamically based on the occurrence of precipitation. Defining a potential sowing window was crucial for this process. Currently, various databases provide information on sowing and harvesting windows, but they have limitations due to the significant temporal and spatial variability of crop calendars. The FAO offers a valuable tool called the Crop Calendar, but it is primarily limited to agricultural areas in developing countries of Africa and Asia. To address this, the FAO Crop Calendar database was supplemented with other sowing date calendars.

One such database is from the University of Wisconsin-Madison (USA), which digitizes and georeferences existing observations of crop planting and harvesting dates. This database provides gridded maps of planting dates for 19 crops at two different resolutions (5 minutes and 0.5 degrees) and in two formats (netCDF and ArcINFO ASCII). Additionally, the database was enhanced with data from the Global Information and Early Warning System on Food and Agriculture (FAO-GIEWS), several sources from the United States Department of Agriculture (USDA, including, USDA crop production maps), scientific publications, and expert knowledge from the development team in various cropping regions. While most data were specified at the national level, subnational data were included for large countries. This regionalization effort extended to 35 countries (20% of total countries), such as Australia, Brazil, Canada, DR Congo, China, Ghana, or India.

Additionally, in regions where a crop is grown more than once per year, different growing seasons with distinct planting windows have been introduced for that crop. The start and end dates of the sowing period were integrated into the crop database (3053 entries). Each country's ISO code (ISO 3166-1 alpha-2) was incorporated to facilitate efficient data processing. For countries with regional or subnational information, ISO codes were included at a lower level (ISO 3166-2), associating crop values with specific states, regions, provinces, or districts as per available data. Integration of geographical coordinates from the simulation grid with ISO codes was achieved using the Nominatim API through a Python script, linking each cell's geographical coordinates with its corresponding country or region. This approach ensured that the simulation grid and crop database are geospatially linked.