{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T03:37:26Z","timestamp":1782790646188,"version":"3.54.5"},"reference-count":56,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,11]],"date-time":"2020-09-11T00:00:00Z","timestamp":1599782400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001044","name":"Dairy Australia","doi-asserted-by":"publisher","award":["Dairy On PAR"],"award-info":[{"award-number":["Dairy On PAR"]}],"id":[{"id":"10.13039\/501100001044","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Agriculture, Nature and Food Quality, Agrifirm Plant B.V., ZLTO and Kverneland Group Mechatronics B.V.","award":["Public-Private Partnership Precision Agriculture 2.0"],"award-info":[{"award-number":["Public-Private Partnership Precision Agriculture 2.0"]}]},{"name":"Ministry of Agriculture, Nature and Food Quality and ZuivelNL","award":["Public-Private Partnership Amazing Grazing"],"award-info":[{"award-number":["Public-Private Partnership Amazing Grazing"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crude protein estimation is an important parameter for perennial ryegrass (Lolium perenne) management. This study aims to establish an effective and affordable approach for a non-destructive, near-real-time crude protein retrieval based solely on top-of-canopy reflectance. The study contrasts different spectral ranges while selecting a minimal number of bands and analyzing achievable accuracies for crude protein expressed as a dry matter fraction or on a weight-per-area basis. In addition, the model\u2019s prediction performance in known and new locations is compared. This data collection comprised 266 full-range (350\u20132500 nm) proximal spectral measurements and corresponding ground truth observations in Australia and the Netherlands from May to November 2018. An exhaustive-search (based on a genetic algorithm) successfully selected band subsets within different regions and across the full spectral range, minimizing both the number of bands and an error metric. For field conditions, our results indicate that the best approach for crude protein estimation relies on the use of the visible to near-infrared range (400\u20131100 nm). Within this range, eleven sparse broad bands (of 10 nm bandwidth) provide performance better than or equivalent to those of previous studies that used a higher number of bands and narrower bandwidths. Additionally, when using top-of-canopy reflectance, our results demonstrate that the highest accuracy is achievable when estimating crude protein on its weight-per-area basis (RMSEP 80 kg.ha\u22121). These models can be employed in new unseen locations, resulting in a minor decrease in accuracy (RMSEP 85.5 kg.ha\u22121). Crude protein as a dry matter fraction presents a bottom-line accuracy (RMSEP) ranging from 2.5\u20133.0 percent dry matter in optimal models (requiring ten bands). However, these models display a low explanatory ability for the observed variability (R2 &gt; 0.5), rendering them only suitable for qualitative grading.<\/jats:p>","DOI":"10.3390\/rs12182958","type":"journal-article","created":{"date-parts":[[2020,9,11]],"date-time":"2020-09-11T09:05:16Z","timestamp":1599815116000},"page":"2958","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Retrieval of Crude Protein in Perennial Ryegrass Using Spectral Data at the Canopy Level"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0665-9426","authenticated-orcid":false,"given":"Gustavo","family":"Togeiro de Alckmin","sequence":"first","affiliation":[{"name":"School of Technology, Environments and Design, University of Tasmania-Discipline of Geography and Spatial Sciences, Hobart, TAS 7005, Australia"},{"name":"Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9468-4516","authenticated-orcid":false,"given":"Arko","family":"Lucieer","sequence":"additional","affiliation":[{"name":"School of Technology, Environments and Design, University of Tasmania-Discipline of Geography and Spatial Sciences, Hobart, TAS 7005, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerbert","family":"Roerink","sequence":"additional","affiliation":[{"name":"Wageningen Environmental Research-Earth Informatics, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5381-0208","authenticated-orcid":false,"given":"Richard","family":"Rawnsley","sequence":"additional","affiliation":[{"name":"Tasmanian Institute of Agriculture-Centre for Dairy, Grains and Grazing, 16-20 Mooreville Rd, Burnie, TAS 7320, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Idse","family":"Hoving","sequence":"additional","affiliation":[{"name":"Wageningen Livestock Research-Livestock and Environment, De Elst 1, 6700 AH Wageningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5549-5993","authenticated-orcid":false,"given":"Lammert","family":"Kooistra","sequence":"additional","affiliation":[{"name":"Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1111\/j.1365-2494.2011.00795.x","article-title":"Pasture-based dairy farm systems increasing milk production through stocking rate or milk yield per cow: Pasture and animal responses","volume":"66","author":"Garcia","year":"2011","journal-title":"Grass Forage Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.3168\/jds.S0022-0302(87)80115-7","article-title":"Use of Near Infrared Reflectance Spectroscopy in Forage Testing","volume":"70","author":"Jones","year":"1987","journal-title":"J. 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