{"id":6981,"date":"2026-07-08T10:54:05","date_gmt":"2026-07-08T08:54:05","guid":{"rendered":"https:\/\/ist.blogs.inrae.fr\/afs\/?p=6981"},"modified":"2026-07-08T10:54:05","modified_gmt":"2026-07-08T08:54:05","slug":"correction-factors-can-overcompensate-log-transformation-bias-in-allometric-biomass-models","status":"publish","type":"post","link":"https:\/\/ist.blogs.inrae.fr\/afs\/2026\/07\/08\/correction-factors-can-overcompensate-log-transformation-bias-in-allometric-biomass-models\/","title":{"rendered":"Correction factors can overcompensate log-transformation bias in allometric biomass models"},"content":{"rendered":"<script type='text\/javascript' src='https:\/\/d1bxh8uas1mnw7.cloudfront.net\/assets\/embed.js'><\/script><p><span style=\"color: #0d5c06;font-size: 10pt\"><strong>Research paper<\/strong><\/span><\/p>\n<p><strong><a href=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2026\/07\/26-07-08_Dutca-I.png\"><img loading=\"lazy\" decoding=\"async\" class=\"size-medium wp-image-6982 alignright\" src=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2026\/07\/26-07-08_Dutca-I-300x122.png\" alt=\"\" width=\"300\" height=\"122\" srcset=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2026\/07\/26-07-08_Dutca-I-300x122.png 300w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2026\/07\/26-07-08_Dutca-I-640x260.png 640w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2026\/07\/26-07-08_Dutca-I.png 685w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a>Key message<\/strong><\/p>\n<p align=\"justify\">Fitting allometric biomass models using ln-ln transformation approach can produce biased estimates, as in certain conditions correction factors were shown to overcompensate the back transformation bias. However, weighted nonlinear regression approach consistently provided unbiased estimates. Given the difficulty of correctly identifying error distributions, especially with unreliable normality tests, cautious method selection is essential to avoid biased biomass predictions.<\/p>\n<p><strong>Keywords<\/strong><br \/>\nLogarithmic transformation; Weighted nonlinear model; Biomass prediction bias<\/p>\n<div class='altmetric-embed' data-badge-type='donut' data-doi='10.1186\/s13595-026-01340-y'  style='float: right; ' ><\/div>\n<p><strong>Publication<\/strong><br \/>\nDutc\u0103, I. Correction factors can overcompensate log-transformation bias in allometric biomass models.\u00a0<i>Annals of Forest Science<\/i>\u00a083, 24 (2026). <a href=\"https:\/\/doi.org\/10.1186\/s13595-026-01340-y\">https:\/\/doi.org\/10.1186\/s13595-026-01340-y<\/a><\/p>\n<p><strong>Handling editor<\/strong><br \/>\nErwin Dreyer<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Research paper Key message Fitting allometric biomass models using ln-ln transformation approach can produce biased estimates, as in certain conditions correction factors were shown to overcompensate the back transformation bias. However, weighted nonlinear regression approach consistently provided unbiased estimates. Given the difficulty of correctly identifying error distributions, especially with unreliable normality tests, cautious method selection [&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-6981","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\/6981","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=6981"}],"version-history":[{"count":3,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/6981\/revisions"}],"predecessor-version":[{"id":6985,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/6981\/revisions\/6985"}],"wp:attachment":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/media?parent=6981"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/categories?post=6981"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/tags?post=6981"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}