Analysis of meteorological and climatic data allows to provide near real-time information about the crop state, in quality and quantity, with the possibility of early warning on alarm/alert situations so that timely interventions can be planned and undertaken. Crop forecasting philosophy is based on various kind of data collected from different sources: meteorological data, agrometeorological phenology, yield, soil water holding capacity, remotely sensed, agricultural statistics. Based on meteorological and agronomic data, several indices are derived which are deemed to be relevant variables in determining crop yield, for instance crop water satisfaction, surplus and excess moisture, average soil moisture, etc.
Crop forecasting is the art of predicting crop yields tons/ha and production before the harvest actually takes place, typically a couple of months in advance. Crop forecasting relies on computer programmes that describe the plant-environment interactions in quantitative terms. Such programmes are called “models”, and they attempt to simulate plant-weather-soil interactions. They need, therefore, information and data on the most important factors that affect crop yields – the model inputs. After passing “through” the model, the inputs are converted to a number of outputs, such as maps of crop conditions and yields.