Linking process-based potato models with light reflectance data: Does model complexity enhance yield prediction accuracy?

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cg.authorship.typesCGIAR single centreen
cg.contributor.affiliationInternational Potato Centeren
cg.creator.identifierDavid Ramirez: 0000-0003-4546-9745
cg.creator.identifierHildo Loayza: 0000-0002-4145-5453
cg.howPublishedFormally Publisheden
cg.identifier.doihttps://doi.org/10.1016/j.eja.2016.10.008en
cg.isijournalISI Journalen
cg.issn1161-0301en
cg.journalEuropean Journal of Agronomyen
cg.reviewStatusPeer Reviewen
cg.subject.cipPOTATO AGRI-FOOD SYSTEMSen
cg.subject.cipPOTATOESen
cg.subject.cipCROP AND SYSTEMS SCIENCES CSSen
cg.volume82en
dc.contributor.authorQuiróz, R.en
dc.contributor.authorLoayza, H.en
dc.contributor.authorBarreda, C.en
dc.contributor.authorGavilán, C.en
dc.contributor.authorPosadas, A.en
dc.contributor.authorRamírez, D.en
dc.date.accessioned2016-12-13T12:24:34Zen
dc.date.available2016-12-13T12:24:34Zen
dc.identifier.urihttps://hdl.handle.net/10568/78288
dc.titleLinking process-based potato models with light reflectance data: Does model complexity enhance yield prediction accuracy?en
dcterms.abstractData acquisition for parameterization is one of the most important limitations for the use of potato crop growth models. Non destructive techniques such as remote sensing for gathering required data could circumvent this limitation. Our goal was to analyze the effects of incorporating ground-based spectral canopy reflectance data into two light interception models with different complexity. A dynamic- hourly scale- canopy photosynthesis model (DCPM), based on a non-rectangular hyperbola applied to sunlit and shaded leaf layers and considering carbon losses by respiration, was implemented (complex model). Parameters included the light extinction coefficient, the proportion of light transmitted by leaves, the fraction of incident diffuse photosynthetically active radiation and leaf area index. On the other hand, a simple crop growth model (CGM) based on daily scale of light interception, light use efficiency (LUE) and harvest index was parameterized using either canopy cover (CGMCC) or the weighted difference vegetation index (CGMWDVI). A spectroradiometer, a chlorophyll meter and a multispectral camera were used to derive the required parameters. CGMWDVI improved yield prediction compared to CGMCC. Both CGMWDVI and DCPM showed high degree of accuracy in the yield prediction. Since large LUE variations were detected depending on the diffuse component of radiation, the improvement of simple CGM using remotely sensed data is contingent on an appropriate LUE estimation. Our study suggests that the incorporation of remotely sensed data in models with different temporal resolution and level of complexity improves yield prediction in potato.en
dcterms.accessRightsLimited Access
dcterms.bibliographicCitationQuiroz, R.; Loayza, H.; Barreda, C.; Gavilán, C.; Posadas, A.; Ramírez, D. A. 2017. Linking process-based potato models with light reflectance data: Does model complexity enhance yield prediction accuracy? European Journal of Agronomy. (Netherlands). ISSN 1161-0301. 82 (Part A):104-112.en
dcterms.extentp. 104-112en
dcterms.issued2017-01
dcterms.languageen
dcterms.licenseCopyrighted; all rights reserved
dcterms.publisherElsevieren
dcterms.subjectcanopyen
dcterms.subjectremote sensingen
dcterms.subjectcropping systemsen
dcterms.typeJournal Article

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