SPATIAL DEPENDENCIES WITHIN MISSISSIPPI’S PRIMARY FOREST PRODUCTS MANUFACTURERS
We determined whether wood-using mills’ locations spatially depended upon timber product harvest levels and the number of complementary species group mills within and between Mississippi counties. County mill count, either pine or hardwood, was the dependent variable. County timber product harvest l...
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description | We determined whether wood-using mills’ locations spatially depended upon timber product harvest levels and the number of complementary species group mills within and between Mississippi counties. County mill count, either pine or hardwood, was the dependent variable. County timber product harvest levels (thousand green tons) for pine sawtimber, pine pulpwood, and pine poles along with count of hardwood mills were pine model predictors; the hardwood model included hardwood sawtimber and pulpwood harvests and pine-type mill count. Poisson regression models were augmented to Spatial Lag of X models as necessary to account for spatial dependencies. Pine product harvesting direct effects were absent. Own-county pine pulpwood harvests positively influenced pine mill counts in neighboring counties (t = 3.21, p = 0.0013); pine sawtimber to less so (t = 1.77, p = 0.0766); while pine pole harvests produced the opposite effect (t = -1.96, p = 0.0505). Pine sawtimber and pulpwood competition increased with procurement radii. Greater hardwood pulpwood harvesting (t = 4.44, p < 0.0001) and pine mill count (t = 2.70, p = 0.0085) indicated a significant own-county hardwood mill presence. This is germane to log trucking output, wood utilization efficiency, standing timber prices, and consequently timberland value. |
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County mill count, either pine or hardwood, was the dependent variable. County timber product harvest levels (thousand green tons) for pine sawtimber, pine pulpwood, and pine poles along with count of hardwood mills were pine model predictors; the hardwood model included hardwood sawtimber and pulpwood harvests and pine-type mill count. Poisson regression models were augmented to Spatial Lag of X models as necessary to account for spatial dependencies. Pine product harvesting direct effects were absent. Own-county pine pulpwood harvests positively influenced pine mill counts in neighboring counties (t = 3.21, p = 0.0013); pine sawtimber to less so (t = 1.77, p = 0.0766); while pine pole harvests produced the opposite effect (t = -1.96, p = 0.0505). Pine sawtimber and pulpwood competition increased with procurement radii. Greater hardwood pulpwood harvesting (t = 4.44, p < 0.0001) and pine mill count (t = 2.70, p = 0.0085) indicated a significant own-county hardwood mill presence. This is germane to log trucking output, wood utilization efficiency, standing timber prices, and consequently timberland value.</description><identifier>EISSN: 1946-7664</identifier><language>eng</language><publisher>Athens: The MCFNS Publisher</publisher><subject>Dependent variables ; Forest products ; Freight transportation ; Hardwoods ; Mills ; Pine ; Regression analysis ; Regression models ; Spatial dependencies ; Timber ; Wood</subject><ispartof>International journal of mathematical and computational forestry & natural-resource sciences, 2022-10, Vol.14 (2), p.1</ispartof><rights>2022. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). 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County mill count, either pine or hardwood, was the dependent variable. County timber product harvest levels (thousand green tons) for pine sawtimber, pine pulpwood, and pine poles along with count of hardwood mills were pine model predictors; the hardwood model included hardwood sawtimber and pulpwood harvests and pine-type mill count. Poisson regression models were augmented to Spatial Lag of X models as necessary to account for spatial dependencies. Pine product harvesting direct effects were absent. Own-county pine pulpwood harvests positively influenced pine mill counts in neighboring counties (t = 3.21, p = 0.0013); pine sawtimber to less so (t = 1.77, p = 0.0766); while pine pole harvests produced the opposite effect (t = -1.96, p = 0.0505). Pine sawtimber and pulpwood competition increased with procurement radii. Greater hardwood pulpwood harvesting (t = 4.44, p < 0.0001) and pine mill count (t = 2.70, p = 0.0085) indicated a significant own-county hardwood mill presence. This is germane to log trucking output, wood utilization efficiency, standing timber prices, and consequently timberland value.</description><subject>Dependent variables</subject><subject>Forest products</subject><subject>Freight transportation</subject><subject>Hardwoods</subject><subject>Mills</subject><subject>Pine</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Spatial dependencies</subject><subject>Timber</subject><subject>Wood</subject><issn>1946-7664</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNotjd9KwzAchYMgbM69Q8DrQtKkv6SXoU1doP9sUsSr0abtxRA31-3e1_D1fBI7FA4cPjh85w6tacwhEAB8hR7m-UAIgKTxGr3YWjmjcpzqWpepLhOjLX41bmdKXBhrb6lr8_P1bXHdmEI1bzirGm3dglXaJs7iQpVtphLXNrqxj-h-6t7ncfvfG9Rm2iW7IK-eTaLy4EQluwTDSCSLJiYIiKgf-j4SLPYxl4R6SeUEIuYe5Oj9AD3pKKdyWUsmwo714BnboKc_7-l8_LyO82V_OF7PH8vlPhQcZCTCiLBfWVtEHg</recordid><startdate>20221030</startdate><enddate>20221030</enddate><creator>McConnell, Eric</creator><creator>Crosby, Michael</creator><general>The MCFNS Publisher</general><scope>7SC</scope><scope>7ST</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>KR7</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PATMY</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>SOI</scope></search><sort><creationdate>20221030</creationdate><title>SPATIAL DEPENDENCIES WITHIN MISSISSIPPI’S PRIMARY FOREST PRODUCTS MANUFACTURERS</title><author>McConnell, Eric ; 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County mill count, either pine or hardwood, was the dependent variable. County timber product harvest levels (thousand green tons) for pine sawtimber, pine pulpwood, and pine poles along with count of hardwood mills were pine model predictors; the hardwood model included hardwood sawtimber and pulpwood harvests and pine-type mill count. Poisson regression models were augmented to Spatial Lag of X models as necessary to account for spatial dependencies. Pine product harvesting direct effects were absent. Own-county pine pulpwood harvests positively influenced pine mill counts in neighboring counties (t = 3.21, p = 0.0013); pine sawtimber to less so (t = 1.77, p = 0.0766); while pine pole harvests produced the opposite effect (t = -1.96, p = 0.0505). Pine sawtimber and pulpwood competition increased with procurement radii. Greater hardwood pulpwood harvesting (t = 4.44, p < 0.0001) and pine mill count (t = 2.70, p = 0.0085) indicated a significant own-county hardwood mill presence. This is germane to log trucking output, wood utilization efficiency, standing timber prices, and consequently timberland value.</abstract><cop>Athens</cop><pub>The MCFNS Publisher</pub><oa>free_for_read</oa></addata></record> |
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subjects | Dependent variables Forest products Freight transportation Hardwoods Mills Pine Regression analysis Regression models Spatial dependencies Timber Wood |
title | SPATIAL DEPENDENCIES WITHIN MISSISSIPPI’S PRIMARY FOREST PRODUCTS MANUFACTURERS |
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