2024-03-29T15:36:35Zhttps://repositori.uji.es/oai/requestoai:repositori.uji.es:10234/1822582023-04-26T07:15:38Zcom_10234_43662com_10234_9col_10234_43643
00925njm 22002777a 4500
dc
Pagani, Valentina
author
Guarneri, Tommaso
author
Busetto, Lorenzo
author
Ranghetti, Luigi
author
BOSCHETTI, MIRCO
author
Movedi, Ermes
author
Campos-Taberner, Manuel
author
García Haro, Francisco Javier
author
Katsantonis, Dimitrios
author
Stavrakoudis, Dimitris
author
Ricciardelli, Elisabetta
author
Romano, Filomena
author
Holecz, Francesco
author
Collivignarelli, Francesco
author
Granell, Carlos
author
Casteleyn, Sven
author
Confalonieri, Roberto
author
2019-01
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.
PAGANI, Valentina, et al. A high-resolution, integrated system for rice yield forecasting at district level. Agricultural Systems, 2019, vol. 168, p. 181-190
0308-521X
http://hdl.handle.net/10234/182258
https://doi.org/10.1016/j.agsy.2018.05.007
assimilation
blast disease
Oryza sativa L.
remote sensing
WARM model
A high-resolution, integrated system for rice yield forecasting at district level