Show simple item record

dc.contributor.authorGhamkhar, K.en
dc.contributor.authorIrie, Kenjien
dc.contributor.authorHagedorn, Michaelen
dc.contributor.authorHsiao, Jeffreyen
dc.contributor.authorFourie, Jacoen
dc.contributor.authorGebbie, S.en
dc.contributor.authorHoyos-Villegas, V.en
dc.contributor.authorGeorge, R.en
dc.contributor.authorStewart, Alanen
dc.contributor.authorInch, C.en
dc.contributor.authorWerner, Arminen
dc.contributor.authorBarrett, B. A.en
dc.date.accessioned2020-03-12T22:40:41Z
dc.date.available2019-07-10en
dc.date.issued2019-07-10en
dc.date.submitted2019-07-02en
dc.identifier.issn1746-4811en
dc.identifier.urihttps://hdl.handle.net/10182/11583
dc.description.abstractBackground: In-field measurement of yield and growth rate in pasture species is imprecise and costly, limiting scientific and commercial application. Our study proposed a LiDAR-based mobile platform for non-invasive vegetative biomass and growth rate estimation in perennial ryegrass (Lolium perenne L.). This included design and build of the platform, development of an algorithm for volumetric estimation, and field validation of the system. The LiDAR-based volumetric estimates were compared against fresh weight and dry weight data across different ages of plants, seasons, stages of regrowth, sites, and row configurations. Results: The project had three phases, the last one comprising four experiments. Phase 1: a LiDAR-based, field-ready prototype mobile platform for perennial ryegrassrecognition in single row plots was developed. Phase 2: real-time volumetric data capture, modelling and analysis software were developed and integrated and the resultant algorithm was validated in the field. Phase 3. LiDAR Volume data were collected via the LiDAR platform and field-validated in four experiments. Expt.1: single-row plots of cultivars and experimental diploid breeding populations were scanned in the southern hemisphere spring for biomass estimation. Significant (P < 0.001) correlations were observed between LiDAR Volume and both fresh and dry weight data from 360 individual plots (R² = 0.89 and 0.86 respectively). Expt 2: recurrent scanning of single row plots over long time intervals of a few weeks was conducted, and growth was estimated over an 83 day period. Expt 3: recurrent scanning of single-row plots over nine short time intervals of 2 to 5 days was conducted, and growth rate was observed over a 26 day period. Expt 4: recurrent scanning of paired-row plots over an annual cycle of repeated growth and defoliation was conducted, showing an overall mean correlation of LiDAR Volume and fresh weight of R² = 0.79 for 1008 observations made across seven different harvests between March and December 2018. Conclusions: Here we report development and validation of LiDAR-based volumetric estimation as an efficient and effective tool for measuring fresh weight, dry weight and growth rate in single and paired-row plots of perennial ryegrass for the first time, with a consistently high level of accuracy. This development offers precise, non-destructive and cost-effective estimation of these economic traits in the field for ryegrass and potentially other pasture grasses in the future, based on the platform and algorithm developed for ryegrass.en
dc.format.extent12en
dc.language.isoenen
dc.publisherBMC (part of Springer Nature)en
dc.relationThe original publication is available from - BMC (part of Springer Nature) - https://doi.org/10.1186/s13007-019-0456-2en
dc.relation.urihttps://doi.org/10.1186/s13007-019-0456-2en
dc.rights© The Author(s) 2019en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectbiomassen
dc.subjectdry matteren
dc.subjectpastureen
dc.subjectyielden
dc.subjectgrassen
dc.subjectgrowth rateen
dc.subjectLiDARen
dc.subjectLolium perenneen
dc.subjectperennial ryegrassen
dc.subjectPlant Biology & Botanyen
dc.titleReal-time, non-destructive and in-field foliage yield and growth rate measurement in perennial ryegrass (Lolium perenne L.)en
dc.typeJournal Article
lu.contributor.unitLincoln Universityen
lu.contributor.unitLincoln Agritechen
dc.identifier.doi10.1186/s13007-019-0456-2en
dc.subject.anzsrc070304 Crop and Pasture Biomass and Bioproductsen
dc.subject.anzsrc070302 Agronomyen
dc.subject.anzsrc060705 Plant Physiologyen
dc.subject.anzsrc0601 Biochemistry and Cell Biologyen
dc.subject.anzsrc0607 Plant Biologyen
dc.subject.anzsrc1001 Agricultural Biotechnologyen
dc.relation.isPartOfPlant Methodsen
pubs.notesArticle number: 72en
pubs.organisational-group/LU
pubs.organisational-group/LU/Lincoln Agritech
pubs.organisational-group/LU/Research Management Office
pubs.organisational-group/LU/Research Management Office/QE18
pubs.publication-statusPublisheden
pubs.volume15en
dc.identifier.eissn1746-4811en
dc.rights.licenceAttributionen
dc.rights.licenceAttributionen
lu.identifier.orcid0000-0002-0039-8202
lu.identifier.orcid0000-0002-7734-8262


Files in this item

Default Thumbnail

This item appears in the following Collection(s)

Show simple item record

Creative Commons AttributionCreative Commons Attribution
Except where otherwise noted, this item's license is described as Creative Commons AttributionCreative Commons Attribution