Abstract
Abstract. Analytical Hierarchy Process (AHP) with fuzzy logic inference on attributes was employed to determine areas most suitable for agriculture in the Gordon Cosens Forest (GCF) region within the District of Cochrane in northern Ontario, Canada. Attribute layers considered were soil texture, ELC (Ecological Land Classification) moisture regime, slope, canopy height model (CHM), distance to existing road networks and distance to water bodies. Fuzzy logic inference was utilized to rescale the attributes to a normalized range, taking into account preferability, which was then subjected to pairwise comparisons via AHP to determine the attribute layers' weightings. For the study area, the localities identified as most compatible for agricultural development include the southeastern section of the GCF at approximately 30 km south of the community of Fauquier and the westernmost area of the GCF at about 10 km east of Mattice.
Highlights
There is emerging interest in land management studies for boreal biomes, due in part to expected environmental transitions resulting from climate change (Boulanger et al., 2017) as well as inventory purposes arising from the need to model soil properties of boreal environments and peatlands (Mansuy et al, 2014)
The Consistency index (CI) for the rankings was calculated as 0.0325, which indicates that the pairwise comparisons performed for Step I in Table 1 were sufficient for consistency purposes
Agricultural development was assumed to be of a practice similar to what has previously existed for the Gordon Cosens Forest (GCF) region, which would consist of approximately 40 hectare field plots devoted to either pasture or cropland agriculture
Summary
There is emerging interest in land management studies for boreal biomes, due in part to expected environmental transitions resulting from climate change (Boulanger et al., 2017) as well as inventory purposes arising from the need to model soil properties of boreal environments and peatlands (Mansuy et al, 2014). The recent growth of digital soil mapping research (Minasny et al, 2019) has resulted in prediction maps of soil properties for certain boreal regions Such predictive mapping results can be utilized for land management and environmental studies. Prediction maps of soil properties such as texture (Walters et al, 1992) and moisture (Misra and Tyler, 1999) can relate to nutrient status and be of interest for determining land compatible for agriculture. For this analysis, soil attributes can be combined with other characteristics deemed essential for agricultural land suitability, which often correspond to slope and accessibility considerations (Grau et al, 2013)
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