Crop Growth Monitoring System (CGMS)
The Crop Growth Monitoring System is the core of the MARS Crop Yield Forecast System (MCYFS) currently used in forecasting activities in Europe by AGRI4CAST action. The role of CGMS is providing reliable and timely spatial information about crop status in Europe, which would be used in different statistical procedures to produce a yield prevision.
Meteorological variables such as daily average, minimum and maximum temperatures, rainfall, etc. may help in the understanding of crop development dynamics and yield along the season. Weather data comes from different sources: (1) direct observations from meteorological stations, (2) meteorological products resulting from weather modelling –ECMWF– or (3) remote sensing observations from meteorological platforms.
Weather data is then processed to generate spatial layers (maps) of all the products comprised in the dataset. The processing and storage of meteorological is as close as possible to the acquisition time. The number of observations during the crop season and time delay between these observations and the product availability date are the main variables determining the ability of the system to produce up to date crop estimations. For that reason the sources for meteorological data fulfil two main requirements:
A crop model is a group of algorithms that simulates the functioning of a given crop. Those groups of algorithms mimic the main physiological plant processes –such as light interception, respiration, carbon assimilation, grain production– through a set of assumptions and calibration parameters. Basically, the input datasets of crop models integrate meteorological data (temperature, rainfall, solar radiation, etc.), soil information (soil water capacity, soil depth) and management practices (e.g. irrigation). The outputs are usually indicators of crop development such as the biomass produced, the leaf area produced, the biomass allocated in the storage organs (grain in the case of cereals), etc.
In forecasting activities at national or regional scales crop models play a major role. They provide the basis to assess the influence of weather on crop yield. Rather than producing a specific value describing the actual harvested crop yield, they calculate a set of indicators describing the inter-annual variability of crop biophysical parameters that can be statistically related to official yield figures to produce forecasts.
The operational models currently are integrated in the BioMA (Biophysical Models Application) platform, a new infrastructure for crop modelling already developed within the MARS Unit integrating the existing models (WOFOST, for cereals and tubers; WARM for rice; LINGRA for pastures) in a more efficient environment.
The crop yield forecast procedure assesses yield forecasts in ton.ha-1 fresh weight using different statistical methods and software tools. Two different approaches are developed with the aim to predict the yield.
(1) The first one consists in a classic regression approach, in which the focus is on the relationship between a dependent variable - the yield - and one or more independent parameters related to climate/ weather effects,
(2) while the second is based on analogies between the contingent conditions and the past, investigating years that behave similarly with respect to selected events and reporting their measured effects on the actual state in order to predict final consequences.
The yield "predictors" consist in the products previously generated by crop modelling solutions: meteorological impact evaluation (minimum or maximum temperature, rain, radiation level, etc.), crop status assessment (e.g. soil moisture, development stage) and crop growth expectations (e.g. potential yield biomass, potential yield storage).