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Cereal yield estimation system

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This project aims to develop a pilot cereal yield estimation system for the Agricultural Department of the Regional Government of Castilla y Leon, Spain (JCL) The system will be based on crop growth models and time series of high resolution satellite images. The output from the system will be reliable, timely and objective predictions of expected cereal yields for the study's trial site and for each of the agricultural districts within the site This information will feed into and complement the JCL's current forecasting programme to support the decisions that depend on it.
The JCL uses yield estimations throughout the season, as a vital input to policy making. The Studies and Statistics department receives requests for information from many sources: grain storage suppliers, seed wholesalers, researchers, buyers and many other interested parties. As well as keeping themselves informed the data is also of value to the JCL for determining compensation payments for losses due to bad weather paid for by the Integral Insurance fund that they manage.
Much of the emphasis and development in recent years has been driven by Community needs at European level. Hence programmes such as MARS have .developed sophicticated techniques geared primarily at the services of the Commssion in the administration of the Common Agricultural Policy and the statistical information required by Eurostat. However, as stated above, this information is not suited to the operational needs of regional autorities like the JCL.
The main objective of the project is to produce cereal yield predictions based on time series of high resolution satellite images and crop growth models for the regional government of Castilla y Leon. This requires:
- the transfer of technology developed by Logica and the University of Nottingham to Southern Europe,
- to develop crop growth models for cereals which can be parameterisedfrom satellite derived data.
- to implement a pilot Yield Estimation System and to compare the quality ofthe yield predictions with predictions obtained by traditional techniques. The secondary objective is to profit from the CEO programme and, through using the CEO, to help the programme to develop. To achieve these two aims the project will:
- use the CEO-ES to identify suitable technologies and alternative EO data sour - make the results of the project, together with sets of input data, availableCEO users to permit independent corroboration of results and simultaneousdiffusion of the technology.

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