Algorithms for Weather‐based Management Decisions in Major Rainfed Crops of India: Validation Using Data from Multi‐location Field Experiments

Crop weather calendars (CWC) serve as tools for taking crop management decisions. However, CWCs are not dynamic, as they were prepared by assuming normal sowing dates and fixed occurrence as well as duration of phenological stages of rainfed crops. Sowing dates fluctuate due to variability in monsoon onset and phenology varies according to crop duration and stresses encountered. Realizing the disadvantages of CWC for issuing accurate agromet advisories, a protocol of dynamic crop weather calendar (DCWC) was developed by All India Coordinated Research Project on Agrometeorology (AICRPAM). The DCWC intends to automatize agromet advisories using prevailing and forecasted weather. Different modules of DCWC, namely, Sowing & irrigation schedules, crop contingency plans, phenophase-wise crop advisory, and advisory for harvest were prepared using long-term data of ten crops at nine centers of AICRPAM in eight states in India. Modules for predicting sowing dates and phenology were validated for principal crops and varieties at selected locations. The predicted sowing dates of 10 crops pooled over nine centers showed close relationships with observed values (r(2) of .93). Predicted phenology showed better agreement with observed in all crops except cotton (Gossypium L.; at Parbhani) and pigeon pea [Cajanus cajan (L.) Millsp.] (at Bangalore). Predicted crop phenology using forecasted and realized weather by DCWC are close to each other, but number of irrigations differed, and it failed for accurate prediction in groundnut at Anantapur in drought year (2014). The DCWCs require further validation for making it operational to issue agromet advisories in all 732 districts of India.