Meteorology and successful food production are inextricably linked. Dacom’s decision-supporting models provide high-quality weather forecasts and well-measured weather data from a weather station, which provides a good predictive service. In recent years, new meteorological technology has been developed at an enormous rate, which can improve the predictive value and ease of use of agricultural advisory services. These are techniques that affect data quality as an innovative automated way of delivering through APIs. In this project we investigate whether this technology is suitable for improving the advisory models and building and testing a software prototype.
The intended result is:
1. The scientific analysis of data sets of various weatherproviders, with the purpose of answering the question whether:
— there is a low-cost world-wide and automatically invoked weather forecast of sufficient quality for irrigation advice is available.
— an automatically-invoked weather forecast of sufficient quality for fungal disease advice is available.
— automatically invoke world-wide data sets of calculated weather and whether these data sets are of sufficient quality to replace measured weather station data.
2. Testing and demonstrating the operating principle via a software prototype renewed irrigation advice and mold advice.