A high-resolution, integrated system for rice yield forecasting at district level
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Other documents of the author: Pagani, Valentina; Guarneri, Tommaso; Busetto, Lorenzo; Ranghetti, Luigi; BOSCHETTI, MIRCO; Movedi, Ermes; Campos-Taberner, Manuel; García Haro, Francisco Javier; Katsantonis, Dimitrios; Stavrakoudis, Dimitris; Ricciardelli, Elisabetta; Romano, Filomena; Holecz, Francesco; Collivignarelli, Francesco; Granell, Carlos; Casteleyn, Sven; Confalonieri, Roberto
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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Title
A high-resolution, integrated system for rice yield forecasting at district levelAuthor (s)
Date
2019-01Publisher
Elsevier MassonISSN
0308-521XBibliographic citation
PAGANI, Valentina, et al. A high-resolution, integrated system for rice yield forecasting at district level. Agricultural Systems, 2019, vol. 168, p. 181-190Type
info:eu-repo/semantics/articlePublisher version
https://www.sciencedirect.com/science/article/pii/S0308521X17305048Version
info:eu-repo/semantics/submittedVersionSubject
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 ... [+]
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. [-]
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Agricultural Systems, 2019, vol. 168Rights
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