Title: An evaluation framework to build a cost-efficient crop monitoring system. Experiences from the extension of the European Crop Monitoring System
Authors: LOPEZ LOZANO RAULBARUTH BETTINA
Citation: AGRICULTURAL SYSTEMS vol. 168 p. 231-246
Publisher: ELSEVIER SCI LTD
Publication Year: 2019
JRC N°: JRC109029
ISSN: 0308-521X (online)
URI: https://www.sciencedirect.com/science/article/pii/S0308521X17310259?via%3Dihub
http://publications.jrc.ec.europa.eu/repository/handle/JRC109029
DOI: 10.1016/j.agsy.2018.04.002
Type: Articles in periodicals and books
Abstract: This paper presents an evaluation framework followed to identify cost-efficient alternatives to extend the MARS Crop Yield Forecasting System (MCYFS), run by the European Commission Joint Research Centre since 1992, to other main producing areas of the world: Eastern European Neighbourhood, Asia, Australia, South America and North America. These new systems would follow the principles and components of the MCYFS Europe: a meteorological data infrastructure, a remote sensing data infrastructure, a crop modelling platform, statistical tools, a team of analysts and a crop area estimation component. The framework designed evaluates the performance of the possible MCYFS-like system realizations against six defined objectives and their costs. Possible monitoring systems are based on a combination of different technical solutions for each of the MCYFS components, and are evaluated through an automatic algorithm that calculates the expected system performance –relying on a priori expert judgement–, the costs, and possible risks to construct some technical solutions, to finally identify the cost-efficient ones. A baseline system achieving the minimum reliability threshold was identified as the most efficient starting point for the MCYFS extension in all the geographical areas. Such system would be built upon: (i) near real-time reanalysis meteorological products; (ii) remote sensing data from low-resolution (~1km) platforms with a long-term product archive; (iii) crop models based on crop-specific model calibration from experimental data published in scientific literature; (iv) statistical methods based on trend and regression analysis applied to national level; (v) a team of analysts with specific technical profiles (on meteorology, remote sensing, and agronomy); and (vi) digital classification of very high resolution imagery supported by non-expensive ground surveys for area estimation. In countries where accessibility to local data and resources is high the baseline system can be upgraded enhancing some of the components: sub-national statistical analysis with additional statistical methods like multiple regression or scenario analysis; recruitment of experts on local agricultural conditions in the team of analysts; local calibration of crop models with experimental data; and exploiting high and low resolution biophysical products from remote sensing for crop monitoring.
JRC Directorate:Sustainable Resources

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