Sitka spruce site index in response to varying soil moisture and nutrients in three different climate regions in Ireland

Farrelly, Niall; Dhubháin, Áiné Ní; Nieuwenhuis, Maarten

doi:10.1016/j.foreco.2011.08.012

Cited by 11 publications

(6 citation statements)

References 17 publications

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“…Geocentric approaches have been used as a basis for estimating SI or other measures of forest productivity and involve relating SI to various direct and/or indirect environmental factors [1]. Many studies have revealed environmental predictors of SI, using edaphic [2][3][4][5], topographic [6][7][8], and/or climatic [9][10][11] variables. Geocentrically-based (biophysical) SI models are independent from stand age and structure, usually have satisfactory prediction power and, therefore, provide tools that can effectively inform forest management.…”

Section: Introductionmentioning

confidence: 99%

High Resolution Site Index Prediction in Boreal Forests Using Topographic and Wet Areas Mapping Attributes

Bjelanovic

Comeau

White

2018

Forests

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Abstract:The purpose of this study was to evaluate the relationships between environmental factors and the site index (SI) of trembling aspen, lodgepole pine, and white spruce based on the sampling of temporary sample plots. LiDAR generated digital elevation models (DEM) and wet areas mapping (WAM) provided data at a 1 m resolution for the study area in Alberta. Six different catchment areas (CA), ranging from 0.5 ha to 10 ha, were tested to reveal optimal CA for calculation of the depth-to-water (DTW) index from WAM. Using different modeling methods, species-specific SI models were developed for three datasets: (1) topographic and wet area variables derived from DEM and WAM, (2) only WAM variables, and (3) field measurements of soil and topography. DTW was selected by each statistical method for each species and, in most cases, DTW was the strongest predictor in the model. In addition, differences in strength of relationships were found between species. Models based on remotely-sensed information predicted SI with a root mean squared error (RMSE) of 1.6 m for aspen and lodgepole pine, and 2 m for white spruce. This approach appears to adequately portray the variation in productivity at a fine scale and is potentially applicable to forest growth and yield modeling and silviculture planning.

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Section: Introductionmentioning

confidence: 99%

High Resolution Site Index Prediction in Boreal Forests Using Topographic and Wet Areas Mapping Attributes

Bjelanovic

Comeau

White

2018

Forests

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“…The results here are supported by the classifications of Pyatt (1995) in which peats, podzols and unflushed peaty gleys were designated as having 'poor' to 'very poor' nutrient status while surface water gleys and well-drained brown earth soils were mainly designated 'medium' status. Attempts to relate these soil classifications to soil chemistry identified soil pH, soil available base cations and NO 3 − as indicators of soil nutrient regime (Wilson et al, 2005;Farrelly et al, 2011). Additionally, soil organic carbon plays a major control on N cycling with peat soils (Rowe et al, 2012) and is often shown to have lower rates of net-N mineralization rates than mineral soils (Rangeley and Knowles, 1988;Westbrook and Devito, 2004;Sheppard et al, 2004).…”

Section: Discussionmentioning

confidence: 99%

Critical loads and nitrogen availability under deposition and harvest scenarios for conifer forests in Ireland

Johnson

Cummins

Aherne

2016

Science of The Total Environment

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“…Modelling was conducted using stepwise regression, which has been used to estimate various forest variables in different studies [59][60][61]. The response variable was specified, and a list of possible explanatory variables was provided (Equation ( 3)).…”

Section: Methodsmentioning

confidence: 99%

Using Acoustic Tomography to Model Wood Deterioration in Cedrelinga cateniformis Ducke in the Peruvian Amazon

Cardenas-Rengifo,

Baselly-Villanueva,

Chumbimune-Vivanco

et al. 2024

Forests

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Forest plantations can be established in order to restore degraded areas. Acoustic tomography, which is of increasing importance in forest management, was used in the present study to obtain information for managing plantations of Cedrelinga cateniformis Ducke in the Peruvian Amazon. The species is valuable in the timber sector of Peru, but the core wood tends to deteriorate and develop cavities. The main objective of the study was to model wood deterioration in Cedrelinga cateniformis Ducke using the data obtained through acoustic tomography. Eight plantations of varying ages were analyzed using acoustic tomography in order to obtain indicators of wood deterioration. Biometric, climatic, and edaphic data (explanatory variables) were also measured in each plantation. The indicator variables and explanatory variables were compared and evaluated using correlation and principal component analysis. Wood deterioration was modelled using stepwise regression. The indicator variables differed significantly between plantations and were mainly correlated with the biometric variables (age and diameter at breast height). The models explained 81% of the variability of pith rot. The percentage rotten area was minimal in young plantations (1%), and the opposite was observed in mature trees (21.5 to 25.6%). The study findings provide valuable information, enabling foresters to determine the optimal age and diameter for felling Cedrelinga cateniformis in plantations in the Peruvian Amazon.

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Sitka spruce site index in response to varying soil moisture and nutrients in three different climate regions in Ireland

Cited by 11 publications

References 17 publications

High Resolution Site Index Prediction in Boreal Forests Using Topographic and Wet Areas Mapping Attributes

High Resolution Site Index Prediction in Boreal Forests Using Topographic and Wet Areas Mapping Attributes

Critical loads and nitrogen availability under deposition and harvest scenarios for conifer forests in Ireland

Using Acoustic Tomography to Model Wood Deterioration in Cedrelinga cateniformis Ducke in the Peruvian Amazon

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