@article{Pagani2018,

title = {A high-resolution, integrated system for rice yield forecasting at district level},

author = {Valentina Pagani and Tommaso Guarneri and Lorenzo Busetto and Luigi Ranghetti and Mirco Boschetti and Ermes Movedi and Manuel Campos-Taberner and Francisco Javier Garcia-Haro and Dimitrios Katsantonis and Dimitris Stavrakoudis and Elisabetta Ricciardelli and Filomena Romano and Francesco Holecz and Francesco Collivignarelli and Carlos Granell-Canut and Sven Casteleyn and Roberto Confalonieri. },

doi = {https://doi.org/10.1016/j.agsy.2018.05.007},

issn = {0308-521X},

year  = {2018},

date = {2018-07-23},

journal = {Agricultural systems},

volume = {168},

pages = {181-190},

abstract = {To meet the growing demands from public and private stakeholders for early yield estimates, a high-resolution (2 km × 2 km) rice yield forecasting system based on the integration of the WARM model and remote sensing (RS) technologies was developed. RS was used to identify rice-cropped area and to derive spatially distributed sowing dates, and for the dynamic assimilation of RS-derived leaf area index (LAI) data within the crop model. The system—tested for the main European rice production districts in Italy, Greece, and Spain—performed satisfactorily; >66% of the inter-annual yield variability was explained in six out of eight combinations of ecotype × district, with a maximum of 89% of the variability explained for the ‘Tropical Japonica’ cultivars in the Vercelli district (Italy). In seven out of eight cases, the assimilation of RS-derived LAI improved the forecasting capability, with minor differences due to the assimilation technology used (updating or recalibration). In particular, RS data reduced uncertainty by capturing factors that were not properly reproduced by the simulation model (given the uncertainty due to large-area simulations). The system, which is an extension of the one used for rice within the EC-JRC-MARS forecasting system, was used pre-operationally in 2015 and 2016 to provide early yield estimates to private companies and institutional stakeholders within the EU-FP7 ERMES project.},

note = {IF},

keywords = {Agro-Geoinformatics, ERMES, rice crop monitoring},

pubstate = {published},

tppubtype = {article}

}

@article{GranellCanut2015,

title = {Editorial: Geospatial Data Capturing, Processing, Analysis, and Visualization in Agro-Geoinformatics},

author = { Carlos Granell-Canut and Sven Casteleyn and Clement Atzberger},

url = {http://journal.frontiersin.org/Article/10.3389/fenvs.2015.00076/abstract},

doi = {10.3389/fenvs.2015.00076},

issn = {2296-665X},

year  = {2015},

date = {2015-01-01},

journal = {Frontiers in Environmental Science},

volume = {3},

pages = {76},

keywords = {Agro-Geoinformatics, crop monitoring, data visualization, Geographic Information Systems (GIS), geospatial data, precision agriculture, remote sensing for agriculture, sustainable agriculture},

pubstate = {published},

tppubtype = {article}

}