Abstract
This article introduces and evaluates a Soil Trafficability Model (STRAM) designed to estimate and forecast potential rutting depth on forest soils due to heavy machine traffic. This approach was developed within the wood-forwarding context of four harvest blocks in Northern and Central New Brunswick. Field measurements used for model calibration involved determining soil rut depths, volumetric moisture content, bulk density, soil resistance to cone penetration (referred to as cone index, or CI), and the dimensionless nominal soil cone index (NCI) defined by the ratio of CI over wheel foot print pressure. With STRAM, rut depth is inferred from: 1) machine dimensions pertaining to estimating foot print area and pressure; 2) pore-filled soil moisture content and related CI projections guided by year-round daily weather records using the Forest Hydrology Model (ForHyM); 3) accounting for within-block soil property variations using multiple and Random Forest regression techniques. Subsequent evaluations of projected soil moisture, CI and rut-depth values accounted for about 40 (multiple regression) and 80 (Random Forest) percent of the corresponding field measured values.
Highlights
With the wide-spread use of modern mechanized harvest machinery, forest and agricultural planners have to deal with the effects of heavy machine loads on soil rutting, compaction, erosion, and subsequent reductions in crop yields (Brady & Weil, 2008)
With Soil Trafficability Model (STRAM), rut depth is inferred from: 1) machine dimensions pertaining to estimating foot print area and pressure; 2) pore-filled soil moisture content and related cone index (CI) projections guided by year-round daily weather records using the Forest Hydrology Model (ForHyM); 3) accounting for within-block soil property variations using multiple and Random Forest regression techniques
While Random Forest (RF) emulates field-measured values for MCPS, CI and rut depth considerably better than best-fitted multivariate regression (MR) values, it can only do so by systematically tracking those variables that best account for the overall data variations, including outliers
Summary
With the wide-spread use of modern mechanized harvest machinery, forest and agricultural planners have to deal with the effects of heavy machine loads on soil rutting, compaction, erosion, and subsequent reductions in crop yields (Brady & Weil, 2008). Soil displacement occurs when soils within depressions and along slopes are moist to wet at or above field capacity (Raper, 2005; Naghdi et al, 2009). Depending on their slope orientations, ruts increase, decrease, or collect run-off (Sutherland, 2003; Antille & Godwin, 2013; Poltorak et al, 2018)
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