{"id":6521,"date":"2025-12-01T15:35:16","date_gmt":"2025-12-01T14:35:16","guid":{"rendered":"https:\/\/ist.blogs.inrae.fr\/afs\/?p=6521"},"modified":"2025-12-03T10:14:19","modified_gmt":"2025-12-03T09:14:19","slug":"lidar-estimated-height-in-a-young-scots-pine-pinus-sylvestris-l-genetic-trial-supports-high-accuracy-early-selection-for-height","status":"publish","type":"post","link":"https:\/\/ist.blogs.inrae.fr\/afs\/2025\/12\/01\/lidar-estimated-height-in-a-young-scots-pine-pinus-sylvestris-l-genetic-trial-supports-high-accuracy-early-selection-for-height\/","title":{"rendered":"LiDAR-estimated height in a young Scots pine (Pinus sylvestris L.) genetic trial supports high-accuracy early selection for height"},"content":{"rendered":"<script type='text\/javascript' src='https:\/\/d1bxh8uas1mnw7.cloudfront.net\/assets\/embed.js'><\/script><p><strong><a href=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2025\/12\/25_12_01_Liziniewicz-M.png\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-6525 alignright\" src=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2025\/12\/25_12_01_Liziniewicz-M-300x258.png\" alt=\"\" width=\"233\" height=\"200\" srcset=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2025\/12\/25_12_01_Liziniewicz-M-300x258.png 300w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2025\/12\/25_12_01_Liziniewicz-M-640x551.png 640w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2025\/12\/25_12_01_Liziniewicz-M.png 685w\" sizes=\"auto, (max-width: 233px) 100vw, 233px\" \/><\/a>Key message\u00a0<\/strong><\/p>\n<p align=\"justify\">Enhancing the efficiency and precision of breeding programs necessitates the implementation of \u201chigh-throughput\u201d phenotyping. By employing various sensors for rapid and frequent measurements, we can gather extensive datasets crucial for conventional breeding efforts. This approach not only holds promise for improving forest production but also for evaluating emerging challenges such as fungal infestations and drought damage. Our research demonstrates the efficiency of utilizing height data derived from LiDAR analysis to identify superior genotypes within the Scots pine breeding program, aimed at enhancing volume production.<\/p>\n<p><strong>Keywords\u00a0<\/strong><br \/>\nDrones;\u00a0UAV; Efficient phenotyping; Height measurements; Forest genetics<\/p>\n<div class='altmetric-embed' data-badge-type='donut' data-doi='10.1186\/s13595-025-01283-w'  style='float: right; ' ><\/div>\n<p><strong>Publication<\/strong><br \/>\nLiziniewicz, M., Almqvist, C., Helmersson, A.\u00a0<i>et al.<\/i>\u00a0LiDAR-estimated height in a young Scots pine (<i>Pinus sylvestris<\/i>\u00a0L.) genetic trial supports high-accuracy early selection for height.\u00a0<i>Annals of Forest Science<\/i>\u00a082, 12 (2025). <a href=\"https:\/\/doi.org\/10.1186\/s13595-025-01283-w\">https:\/\/doi.org\/10.1186\/s13595-025-01283-w<\/a><\/p>\n<p><strong>Handling editor\u00a0<\/strong><br \/>\nShuguang Liu<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Key message\u00a0 Enhancing the efficiency and precision of breeding programs necessitates the implementation of \u201chigh-throughput\u201d phenotyping. By employing various sensors for rapid and frequent measurements, we can gather extensive datasets crucial for conventional breeding efforts. This approach not only holds promise for improving forest production but also for evaluating emerging challenges such as fungal infestations [&hellip;]<\/p>\n","protected":false},"author":240,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14,1,109,15],"tags":[],"class_list":["post-6521","post","type-post","status-publish","format-standard","hentry","category-article-type","category-non-classe","category-open-access","category-research-paper","cat-14-id","cat-1-id","cat-109-id","cat-15-id"],"_links":{"self":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/6521","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/users\/240"}],"replies":[{"embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/comments?post=6521"}],"version-history":[{"count":5,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/6521\/revisions"}],"predecessor-version":[{"id":6574,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/6521\/revisions\/6574"}],"wp:attachment":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/media?parent=6521"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/categories?post=6521"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/tags?post=6521"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}