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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 counties in Mississippi. County mill count for either the pine or hardwood timber industry 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 predictors for the pine model; hardwood sawtimber and pulpwood harvests along with the number of pine-type mills served likewise for the hardwood model. Poisson regression models were constructed and, as necessary, augmented to Spatial Lag of X models where spatial dependency within 100 miles of a county’s centroid was identified. Direct effects of pine product harvesting were absent. Pine mills were not located based on own-county timber resources. Some evidence for hardwood mills supporting pine mill count was found (t = 1.65, p = 0.0987). Own-county pine pulpwood harvests positively influenced pine mill counts in neighboring counties (t = 3.21, p = 0.0013); pine sawtimber to a lesser degree (t = 1.77, p = 0.0766); while pine pole harvests produced the opposite effect (t = -1.96, p = 0.0505). Competition for pine sawtimber and pulpwood increased as procurement radii increased. 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 finding is germane to log trucking output, wood utilization efficiency, standing timber prices, and consequently forestland value. [ABSTRACT FROM AUTHOR]

An examination of changes in growth, mortality, and removals of hardwood sawtimber in the eastern United States within the first two decades of the 21st century found large variations among regions and species groups. Changes in growth ranged from a 17% increase in the Lake States region to a statistically insignificant 1% in the Southern region. Most regions had relatively large increases in mortality. High levels of ash (Fraxinus spp.) mortality in the Northeast, Lake States, and Central regions likely were a result of the emerald ash borer (Agrilus planipennis). Hardwood sawtimber removals declined in all regions except the Lake States and Central regions, with the largest relative declines occurring in the Southern and Mid-Atlantic regions. With the exception of ash, there were no indications of immediate declines in eastern sawtimber volume. However, continual increases in mortality, a resurgence of removals, and reduced growth could cause sawtimber volume to plateau in the coming decades. The findings from this study indicated that there likely would be variations in these plateaus among the species groups and regions. [ABSTRACT FROM AUTHOR]

Input-output models were constructed to describe the economic impacts of timber product outputs in Ohio and its three timber market regions - the Northeast, West, and Southeast - for 2012. Impact Analysis for PLANning was used to describe these impacts in terms of employment, output, and value added based on 1) the total value of outputs delivered to market by each region’s logging sector and 2) a per-unit change in the regionalized delivered value of one million board feet (MMBF) of hardwood sawtimber. Direct impacts of timber products were greatest in the Northeast (for output and value added) and Southeast (for employment). The total economic impacts of timber products in Ohio were 2,880 employees, $287 million in output, and $147 million in value added. The per-unit impact results were more varied due to regional differences in economies and timber price determinants. Employment and output economic impacts per MMBF were both highest in the Southeast. The employment levels directly and indirectly associated with each MMBF in the West were higher than the Northeast. Value added per MMBF was highest in the Northeast across impacts.

Input-output models were constructed to describe the economic impacts of timber product outputs in Ohio and its three timber market regions - the Northeast, West, and Southeast - for 2012. Impact Analysis for PLANning was used to describe these impacts in terms of employment, output, and value added based on 1) the total value of outputs delivered to market by each region's logging sector and 2) a per-unit change in the regionalized delivered value of one million board feet (MMBF) of hardwood sawtimber. Direct impacts of timber products were greatest in the Northeast (for output and value added) and Southeast (for employment). The total economic impacts of timber products in Ohio were 2,880 employees, $287 million in output, and $147 million in value added. The per-unit impact results were more varied due to regional differences in economies and timber price determinants. Employment and output economic impacts per MMBF were both highest in the Southeast. The employment levels directly and indirectly associated with each MMBF in the West were higher than the Northeast. Value added per MMBF was highest in the Northeast across impacts. [ABSTRACT FROM AUTHOR]

Emerging markets for small-diameter roundwood along with a renewed interest in forest biomass for energy have created a need for estimates of merchantable biomass above the minimum sawlog top diameter for timber species in the national forest inventory of the United States. The Forest Inventory and Analysis (FIA) program of the USDA Forest Service recently adopted the component ratio method for estimation of aboveground live tree component biomass, which incorporates regionally specific volume models by species and species group. The merchantable bole component is the biomass of sound wood from a 1.0-ft stump height to a minimum 4.0-in. top diameter. Minimum sawlog top diameters for softwoods and hardwoods in the FIA program are substantially larger than the merchantable bole top diameter, leaving additional bole biomass potentially available for utilization. This article describes a method for estimating merchantable bole biomass for the sawlog component and the component above the minimum sawlog top diameter for timber species in the FIA program. [ABSTRACT FROM AUTHOR]

A decisionmaking model was developed to assist hardwood resource managers in determining the management potential, for sawlog production, of southern upland hardwood stands within the Cumberland Plateau, Western Highland Rim, and Upper Coastal Plain physiographic provinces. The model determines stands to be either manageable (using intermediate stand management) or in need of regeneration. Stand index values were established for even-aged stands using stocking guidelines, individual tree characteristics, and tree class. Threshold index values for continued stand management were established for four sawtimber management objectives in the Cumberland Plateau and three sawtimber management objectives in the Western Highland Rim and Upper Coastal Plain. Stand index values above or below the threshold value returned a decision to continue to manage or initiate regeneration methods for a stand, respectively. In the Cumberland Plateau and Western Highland Rim provinces, a similarity of percentage between model decisions and decisions from an expert panel of hardwood silviculturists was calculated to assist in determining model effectiveness. The overall agreement between the model and experts was 71%. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR) is increasingly used to map terrain and vegetation. Data collection is expensive, but costs are reduced when multiple products are derived from each mission. We examined how well low-density leaf-off LiDAR, originally flown for terrain mapping, quantified hardwood forest structure. We measured tree density, dbh, basal area, mean tree height, Lorey's mean tree height, and sawtimber and pulpwood volume at 114 field plots. Using univariate and multivariate linear regression models, we related field data to LiDAR return heights. We compared models using all LiDAR returns and only first returns. First-return univariate models explained more variability than all-return models; however, the differences were small for multivariate models. Multiple regression models had R2 values of 65% for sawtimber and pulpwood volume, 63% for Lorey's mean tree height, 55% for mean tree height, 48% for mean dbh, 46% for basal area, and 13% for tree density. However, the standard error of the mean for predictions ranged between 1 and 4%, and this level of error is well within levels needed for broad-scale forest assessments. Our results suggest that low-density LiDAR intended for terrain mapping is valuable for broad-scale hardwood forest inventories. [ABSTRACT FROM AUTHOR]

The objective of the study was to verify the applicability and usefulness of time series decomposition in analyzing the variability of timber prices and supply in Poland. The employed multiplicative model was the product of four components: cyclical, seasonal, and irregular fluctuations and the long-term trend. The elements of the time series were determined by means of the Census X11 method, while cyclicality was separated from the trend employing the Hodrick–Prescott filter. Data included quarterly information about the supply (volume) and prices (value) of the timber sold by the State Forests in the years 2005–2018. Analyses were performed for tree species with the greatest economic significance, that is, pine, oak, spruce, beech, birch, and alder, and for their most popular assortments: general purpose large-diameter timber (W0) and medium-diameter timber (S2A). Time series decomposition of quarterly timber production volume and prices revealed irregular, seasonal, and cyclic fluctuations. Within an annual time horizon, irregular fluctuations accounted on average for 6.7% and 28% of overall variability in timber prices and supply, respectively; they exhibited low amplitudes (+5%, −25%, respectively). Cyclical fluctuations were primarily found for prices and were characterized by substantial variations in cycle length (2–4 years) and change amplitude (3–27 Euros). Cyclical fluctuations in timber prices and supply were usually negatively correlated with each other: the upper turning points of price cycles fell near the lower turning points of supply cycles (with a shift of 1 to 3 quarters). The seasonality of prices was also inversely correlated with supply: quarters with low supply exhibited higher prices and vice versa. Seasonal fluctuations were more pronounced for timber supply (36%) as compared to timber prices (20.3%). Different seasonality patterns were found for hardwood and softwood. The lowest supply of softwood was found in the first quarter and the highest in the third quarter (spruce) or fourth quarter (pine). The supply of hardwood was the highest in the first quarter and the lowest in the third quarter. [ABSTRACT FROM AUTHOR]