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dc.contributor.authorPAGANI VALENTINAen_GB
dc.contributor.authorSTELLA T.en_GB
dc.contributor.authorGUARNERI TOMMASOen_GB
dc.contributor.authorFINOTTO GIACOMOen_GB
dc.contributor.authorVAN DEN BERG MAURITSen_GB
dc.contributor.authorMARIN FABIOen_GB
dc.contributor.authorACUTIS MARCOen_GB
dc.contributor.authorCONFALONIERI ROBERTOen_GB
dc.date.accessioned2017-08-16T00:20:57Z-
dc.date.available2017-08-14en_GB
dc.date.available2017-08-16T00:20:57Z-
dc.date.created2017-08-03en_GB
dc.date.issued2016en_GB
dc.date.submitted2017-03-14en_GB
dc.identifier.citationAGRICULTURAL SYSTEMS vol. 154 p. 45-52en_GB
dc.identifier.issn0308-521Xen_GB
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0308521X16308095?via%3Dihuben_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC106149-
dc.description.abstractTimely crop yield forecasts at regional and national level are crucial to manage trade and industry planning and to mitigate price speculations. Sugarcane is responsible for 70% of global sugar supplies, thus making yield forecasts essential to regulate the global commodity market. In this study, a sugarcane forecasting system was developed and successfully applied to São Paulo State, the largest cane producer in Brazil. The system is based on multiple linear regressions relating agro-climatic indicators and outputs of the sugarcane model Canegro to historical yield records. The resulting equations are then used to forecast the yield of the current season using 10-day period updated values of indicators and model outputs as the season progresses. We quantified the reliability of the forecasting system in different stages of the sugarcane cycle by performing cross-validations using the 2000-2013 time series of official stalk yields. Agro-climatic indicators alone explained from 38% of inter-annual yield variability (at State level) during the boom growth phase (i.e., January-April) to 73% during the second half of the harvesting period (i.e., September-October). When Canegro outputs were added to the regressor set, the variability explained increased to 63% for the boom growth phase and 90% after mid harvesting, with the best performances achieved while approaching the end of the harvesting window (i.e. at the beginning of October, SDEP = 0.8 t ha-1, R2cv = 0.93). It is concluded that the overall performances of the system are satisfactory, considering that it was the first attempt based on information exclusively retrieved from the literature. Further improvements to operationalize the system could be possibly achieved by the use of more accurate inputs possibly supplied by the collaboration with local authorities.en_GB
dc.description.sponsorshipJRC.D.5-Food Securityen_GB
dc.format.mediumOnlineen_GB
dc.languageENGen_GB
dc.publisherELSEVIER SCI LTDen_GB
dc.relation.ispartofseriesJRC106149en_GB
dc.titleForecasting sugarcane yields using agro-climatic indicators and Canegro model: a case study in the main production region in Brazilen_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.1016/j.agsy.2017.03.002en_GB
JRC Directorate:Sustainable Resources

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