{"id":4637,"date":"2021-02-02T13:58:03","date_gmt":"2021-02-02T12:58:03","guid":{"rendered":"https:\/\/ist.blogs.inrae.fr\/afs\/?p=4637"},"modified":"2021-02-02T13:58:03","modified_gmt":"2021-02-02T12:58:03","slug":"comparison-of-two-parameter-recovery-methods-for-the-transformation-of-pinus-sylvestris-yield-tables-into-a-diameter-distribution-model","status":"publish","type":"post","link":"https:\/\/ist.blogs.inrae.fr\/afs\/2021\/02\/02\/comparison-of-two-parameter-recovery-methods-for-the-transformation-of-pinus-sylvestris-yield-tables-into-a-diameter-distribution-model\/","title":{"rendered":"Comparison of two parameter recovery methods for the transformation of Pinus sylvestris yield tables into a diameter distribution model"},"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\/2021\/02\/Mauro-2021.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-4640 alignright\" src=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2021\/02\/Mauro-2021-300x261.png\" alt=\"\" width=\"300\" height=\"261\" srcset=\"https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2021\/02\/Mauro-2021-300x261.png 300w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2021\/02\/Mauro-2021-768x668.png 768w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2021\/02\/Mauro-2021-640x556.png 640w, https:\/\/ist.blogs.inrae.fr\/afs\/wp-content\/uploads\/sites\/5\/2021\/02\/Mauro-2021.png 820w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a>Key message<\/strong><\/p>\n<p align=\"justify\">We successfully transformed <em>Pinus sylvestris<\/em> yield tables into diameter distribution models. The best results were obtained with the parameter recovery method based on both mean and quadratic mean diameter, which explained 70% of the variability of frequencies by diameter classes and provided better results in the analysis of errors. On the other hand, the method based on stand density, dominant diameter and quadratic mean diameter explained less variability of frequencies by diameter classes (64.4%).<\/p>\n<p><strong>Abstract<\/strong><\/p>\n<p align=\"justify\"><strong>Context<\/strong> Old datasets used to develop yield table models can be recovered to transform those yield tables into diameter distribution models that provide a more detailed description of size variability and forest structure.<br \/>\n<strong>Aims<\/strong> We used archived measurements collected to develop yield table models for <em>Pinus sylvestris<\/em> L in central Spain, to transform those yield tables into a diameter distribution model by using parameter recovery methods.<br \/>\n<strong>Methods<\/strong> We compared two different parameter recovery methods, one based on both mean and quadratic mean diameter and another one based on dominant diameter, stand density and quadratic mean diameter and used a set of 104 even aged plots to analyze the performance of the said methods for the transformation of <em>Pinus sylvestris<\/em> L yield tables in central Spain into a diameter distribution model.<br \/>\n<strong>Results<\/strong> The parameter recovery method based on both mean and quadratic mean diameter explained 70% of the variability of frequencies by diameter classes and provided better results than the method based on stand density, dominant diameter and quadratic mean diameter that explained 64.4% of the variability of frequencies by diameter classes. However, more important than the method itself were the errors that propagated from the models predicting the different variables used in the parameter recovery.<br \/>\n<strong>Conclusion<\/strong> Based on the results from the analysis of errors by diameter classes, the method using both mean and quadratic mean diameter outperformed the method using dominant diameter, stand density and quadratic mean diameter and is the best option to transform <em>P. sylvestris<\/em> yield tables into diameter distribution models.<\/p>\n<p><strong>Keywords<\/strong><br \/>\nParameter recovery method; Diameter distribution; Growth model; Stand model; Yield table<\/p>\n<div class='altmetric-embed' data-badge-type='donut' data-doi='10.1007\/s13595-021-01028-5'  style='float: right; ' ><\/div>\n<p><strong>Publication<\/strong><br \/>\nMauro, F., Garc\u00eda-Abril, A., Ayuga-T\u00e9llez, E. et al. Comparison of two parameter recovery methods for the transformation of Pinus sylvestris yield tables into a diameter distribution model. Annals of Forest Science 78, 12 (2021). <a href=\"https:\/\/doi.org\/10.1007\/s13595-021-01028-5\">https:\/\/doi.org\/10.1007\/s13595-021-01028-5<\/a><\/p>\n<p><strong>For the read-only version of the full text:<\/strong><br \/>\n<a href=\"https:\/\/rdcu.be\/ceDe0\">https:\/\/rdcu.be\/ceDe0<\/a><\/p>\n<p><strong>Data availability<\/strong><br \/>\nThe datasets generated during and\/or analyzed during the current study are available at the Zenodo repository: <a href=\"https:\/\/doi.org\/10.5281\/zenodo.3934885\">https:\/\/doi.org\/10.5281\/zenodo.3934885<\/a><\/p>\n<p><strong>Handling Editor<\/strong><br \/>\nC\u00e9line Meredieu<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Key message We successfully transformed Pinus sylvestris yield tables into diameter distribution models. The best results were obtained with the parameter recovery method based on both mean and quadratic mean diameter, which explained 70% of the variability of frequencies by diameter classes and provided better results in the analysis of errors. On the other hand, [&hellip;]<\/p>\n","protected":false},"author":109,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14,110,15],"tags":[],"class_list":["post-4637","post","type-post","status-publish","format-standard","hentry","category-article-type","category-data-in-repository","category-research-paper","cat-14-id","cat-110-id","cat-15-id"],"_links":{"self":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/4637","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\/109"}],"replies":[{"embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/comments?post=4637"}],"version-history":[{"count":0,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/posts\/4637\/revisions"}],"wp:attachment":[{"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/media?parent=4637"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/categories?post=4637"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ist.blogs.inrae.fr\/afs\/wp-json\/wp\/v2\/tags?post=4637"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}