Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/30271
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dc.contributor.authorBrinkhoff, Jamesen
dc.contributor.authorRobson, Andrew Jen
dc.date.accessioned2021-03-25T03:30:44Z-
dc.date.available2021-03-25T03:30:44Z-
dc.date.issued2021-06-15-
dc.identifier.citationAgricultural and Forest Meteorology, v.303, p. 1-13en
dc.identifier.issn1873-2240en
dc.identifier.issn0168-1923en
dc.identifier.urihttps://hdl.handle.net/1959.11/30271-
dc.description.abstractEarly crop yield forecasts provide valuable information for growers and industry to base decisions on. This work considers early forecasting of macadamia nut yield at the individual orchard block level with input variables derived from spatio-temporal datasets including remote sensing, weather and elevation. Yield data from 2012–2019, for 101 blocks belonging to 10 orchards, was obtained. We forecast yield on each test year from 2014–2019 using models trained on data from years prior to the test year. Forecasts are generated in January, for the coming harvest in March–September. A linear model using ridge regularized regression produced consistently good predictions compared with other machine learning algorithms including lasso, support vector regression and random forest. Adding meteorological variables offered little improvement over using only remote sensing variables. The 2019 forecast root mean square error at the block level was 0.8 t/ha, and mean absolute percentage error was 20.9%. When block level predictions were aggregated across the multiple orchards per region, production prediction errors were between 0–15% from 2016–2019. The ridge regression model can be easily implemented in GIS platforms to deliver block-level yield forecast maps to end users.en
dc.languageenen
dc.publisherElsevier BVen
dc.relation.ispartofAgricultural and Forest Meteorologyen
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleBlock-level macadamia yield forecasting using spatio-temporal datasetsen
dc.typeJournal Articleen
dc.identifier.doi10.1016/j.agrformet.2021.108369en
dcterms.accessRightsUNE Greenen
local.contributor.firstnameJamesen
local.contributor.firstnameAndrew Jen
local.subject.for2008070699 Horticultural Production not elsewhere classifieden
local.subject.seo2008820206 Macadamiasen
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolSchool of Science and Technologyen
local.profile.emailjbrinkho@une.edu.auen
local.profile.emailarobson7@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.publisher.placeNetherlandsen
local.identifier.runningnumber108369en
local.format.startpage1en
local.format.endpage13en
local.identifier.scopusid85101409973en
local.peerreviewedYesen
local.identifier.volume303en
local.access.fulltextYesen
local.contributor.lastnameBrinkhoffen
local.contributor.lastnameRobsonen
dc.identifier.staffune-id:jbrinkhoen
dc.identifier.staffune-id:arobson7en
local.profile.orcid0000-0002-0721-2458en
local.profile.orcid0000-0001-5762-8980en
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/30271en
local.date.onlineversion2021-02-24-
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleBlock-level macadamia yield forecasting using spatio-temporal datasetsen
local.relation.fundingsourcenoteThis project is being delivered by Hort Innovation – with support from the Australian Government Department of Agriculture and Water Resources as part of its Rural R&D for Profit program – and UNE as the co-investor for ST19008 and ST19015.en
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorBrinkhoff, Jamesen
local.search.authorRobson, Andrew Jen
local.open.fileurlhttps://rune.une.edu.au/web/retrieve/7ff75f3a-baa9-49ce-8bf9-c9ffd0358376en
local.uneassociationYesen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.identifier.wosid000639140200003en
local.year.available2021en
local.year.published2021en
local.fileurl.openhttps://rune.une.edu.au/web/retrieve/7ff75f3a-baa9-49ce-8bf9-c9ffd0358376en
local.fileurl.openpublishedhttps://rune.une.edu.au/web/retrieve/7ff75f3a-baa9-49ce-8bf9-c9ffd0358376en
local.subject.for2020300899 Horticultural production not elsewhere classifieden
local.subject.for2020300206 Agricultural spatial analysis and modellingen
local.subject.seo2020260507 Macadamiasen
dc.notification.token2f361d0c-016d-4e85-b398-82908e9658faen
local.codeupdate.date2021-12-07T08:16:44.031en
local.codeupdate.epersonjbrinkho@une.edu.auen
local.codeupdate.finalisedtrueen
local.original.for2020undefineden
local.original.seo2020260507 Macadamiasen
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School of Science and Technology
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