Site Index Models for Loblolly Pine Forests in the Southern United States Developed with Forest Inventory and Analysis Data

IF 1.5 4区农林科学 Q2 FORESTRY

Forest Science Pub Date : 2023-09-13 DOI:10.1093/forsci/fxad039

Mukti Ram Subedi, Dehai Zhao, Puneet Dwivedi, Bridgett E Costanzo, James A Martin

{"title":"Site Index Models for Loblolly Pine Forests in the Southern United States Developed with Forest Inventory and Analysis Data","authors":"Mukti Ram Subedi, Dehai Zhao, Puneet Dwivedi, Bridgett E Costanzo, James A Martin","doi":"10.1093/forsci/fxad039","DOIUrl":null,"url":null,"abstract":"Abstract Accurate productivity estimates are essential to assess the overall sustainability of forest resources. Site index (SI) models for loblolly pine (Pinus taeda L.) in plantation and natural forests of the southeastern United States were developed using the Forest Inventory and Analysis (FIA) database. We extracted short (~20 years), unbalanced panel data from the FIA database. Ten different nonlinear models derived from the base models using the algebraic difference approach (ADA) or the generalized algebraic difference approach (GADA) were fitted to the extracted data. The performance of the models was ranked based on a variety of fit and evaluation statistics. The results showed that all top three models were derived using the GADA approach. The best model for loblolly pine plantation and natural forest stands was derived from the Hossfeld model and the Chapman–Richards model, respectively. The best-fitted models for planted forests were also compared with previously developed models. This study demonstrated that base-age invariant and polymorphic SI models could be developed using short panel data extracted from FIA data. The SI models presented here can be used as a height growth model component in forest growth and yield model systems. Study Implications: Improved site index equations for assessing the site quality of loblolly pine plantation and natural stands are now available to stakeholders at the policy, management, and operational levels. Activities such as forest management, restoration, and wildlife management, which require site quality data, will benefit from the new models. Furthermore, the approach of deriving panel data based on Forest Inventory and Analysis data offers information on developing and updating models for other species. Finally, the approach of this study, to use permanent plot measurement data in developing growth and yield models, is cost-effective and time-efficient.","PeriodicalId":12749,"journal":{"name":"Forest Science","volume":"82 1","pages":"0"},"PeriodicalIF":1.5000,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Forest Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/forsci/fxad039","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"FORESTRY","Score":null,"Total":0}

引用次数: 0

Abstract

Abstract Accurate productivity estimates are essential to assess the overall sustainability of forest resources. Site index (SI) models for loblolly pine (Pinus taeda L.) in plantation and natural forests of the southeastern United States were developed using the Forest Inventory and Analysis (FIA) database. We extracted short (~20 years), unbalanced panel data from the FIA database. Ten different nonlinear models derived from the base models using the algebraic difference approach (ADA) or the generalized algebraic difference approach (GADA) were fitted to the extracted data. The performance of the models was ranked based on a variety of fit and evaluation statistics. The results showed that all top three models were derived using the GADA approach. The best model for loblolly pine plantation and natural forest stands was derived from the Hossfeld model and the Chapman–Richards model, respectively. The best-fitted models for planted forests were also compared with previously developed models. This study demonstrated that base-age invariant and polymorphic SI models could be developed using short panel data extracted from FIA data. The SI models presented here can be used as a height growth model component in forest growth and yield model systems. Study Implications: Improved site index equations for assessing the site quality of loblolly pine plantation and natural stands are now available to stakeholders at the policy, management, and operational levels. Activities such as forest management, restoration, and wildlife management, which require site quality data, will benefit from the new models. Furthermore, the approach of deriving panel data based on Forest Inventory and Analysis data offers information on developing and updating models for other species. Finally, the approach of this study, to use permanent plot measurement data in developing growth and yield models, is cost-effective and time-efficient.

查看原文本刊更多论文

基于森林清查和分析数据的美国南部火炬松立地指数模型

准确的生产力估算对于评估森林资源的整体可持续性至关重要。利用森林清查与分析(FIA)数据库，建立了美国东南部人工林和天然林火炬松立地指数(SI)模型。我们从FIA数据库中提取了短期(~20年)的不平衡面板数据。利用代数差分法(ADA)或广义代数差分法(GADA)在基本模型的基础上推导出10种不同的非线性模型，并对提取的数据进行拟合。根据各种拟合和评估统计数据对模型的性能进行排名。结果表明，前3个模型均采用GADA方法推导。火炬松人工林和天然林林分的最佳模型分别为Hossfeld模型和Chapman-Richards模型。最适合人工林的模型也与以前开发的模型进行了比较。该研究表明，可以使用从FIA数据中提取的短面板数据建立基本年龄不变和多态SI模型。本文提出的SI模型可以作为森林生长和产量模型系统的高度生长模型成分。研究意义:改进的立地指数方程用于评估火炬松人工林和天然林分的立地质量，现在可供政策、管理和操作层面的利益相关者使用。需要现场质量数据的森林管理、恢复和野生动物管理等活动将受益于这些新模型。此外，基于森林清查和分析数据得出面板数据的方法为开发和更新其他物种的模型提供了信息。最后，本研究的方法是使用永久地块测量数据来开发生长和产量模型，具有成本效益和时间效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Forest Science 农林科学-林学

CiteScore

2.80

自引率

7.10%

发文量

审稿时长

3 months

期刊介绍： Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management. Forest Science is published bimonthly in February, April, June, August, October, and December.