Abstract

This article focuses on modelling and mapping the productivity of black (Picea mariana) and white spruce (Picea glauca) plantations across the Black Brook forest management area in northwestern New Brunswick, Canada, encompassing about 200,000 ha. This effort involved establishing 3500 50 m2 survey plots, each informing about: plantation age (15 to 43 years), planted species type, stem count, tree height, basal area, and wood volume. All of this was supplemented with location-specific productivity predictors, i.e., xy location and specifications pertaining to soil type, soil drainage (established through digital elevation modelling by way of the depth-to-water index DTW), and years since thinning (pre-commercial and commercial), and. The DTW index, as it emulates the elevation rise away from open water features such as streams, rivers and lakes, allowed the re-mapping of existing soil borders by topographic position and drainage association. Non-linear regression analysis revealed that plantation height, basal area and volume all increased with plantation age, as to be expected. Pre-commercial thinning in plantations <30 years old had a positive while the more recent commercial thinning still had the negative effect on standing wood volume and mean annual volume increment (MAI). White spruce MAI generally exceeded black spruce (MAI) by a factor of 1.25. Poor and excessive soil drainage reduced MAI. Best growth performances occurred on plantations established on well-drained calcareous soils. The best-fitted results so obtained allowed for generating black and white spruce MAI maps across the forest management area by ridge-to-valley soil and DTW location at 10 m resolution. These maps were subsequently used for site-by-site silvicultural evaluation and ranking purposes.

Highlights

  • Of forest plantation productivities are affected by forest management actions including species selection and stocking level to promote plantation growth and yields and by climate and soil conditions

  • This article focuses on modelling and mapping the productivity of black (Picea mariana) and white spruce (Picea glauca) plantations across the Black Brook forest management area in northwestern New Brunswick, Canada, encompassing about 200,000 ha

  • This effort involved establishing 3500 50 m2 survey plots, each informing about: plantation age (15 to 43 years), planted species type, stem count, tree height, basal area, and wood volume. All of this was supplemented with location-specific productivity predictors, i.e., xy location and specifications pertaining to soil type, soil drainage, and years since thinning, and

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Summary

Introduction

Of forest plantation productivities are affected by forest management actions including species selection and stocking level to promote plantation growth and yields and by climate and soil conditions. This article focuses on aspects pertaining to how differences in soil quality and drainage affect the growth of black and white spruce plantations, with soil type referring to differences in landforms and related surface expressions and geological substrates. Soil-related growth and yield affecting differences refer to rooting depth, organic matter and coarse fragment content, and nutrient availabilities as modified by rate of soil weathering, litter decomposition, soil moisture regime and climate. Black Brook forest management area within northwestern New Brunswick, Canada: location, extent, terrain elevation, black and white spruce plantation survey plots, and distribution of soils with calcareous content. This article focuses on stem count, tree height, basal area and wood volume growth in select black and white spruce plantations across a 250,000 ha forest management area in northwestern New Brunswick (Figure 1). Soil drainage type was adjusted to conform to the DTM-derived depth-to-water pattern (DTW) across the land as generated from a 10-m resampled LiDAR-generated digital terrain model (DTM) for the area

Study Area
DTM-Based DTW Delineations
Soil Association and Mode of Surface Deposition
Plantation Survey
Data Management
Regression Model and Analysis
Analytical Limitations
Results
Best-Fitted Regression Coefficients
Limitations
MAI Productivity Mapping
Concluding Remarks

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