Shelterbelt systems establishment in Saskatchewan, Canada: a multi-criteria fuzzy logic approach to land suitability mapping


There is lack of guidelines helping land managers to locate suitable areas for planting new shelterbelt agroforestry systems on their landbases. The goal of this study was to create land suitability maps for deciduous, coniferous, and shrub shelterbelt agroforestry systems establishment across a wide range of climatic and soil zones of Saskatchewan, Canada. Spatial shelterbelt data and a suite of 50 predictor variables were analyzed using multivariate principal component analysis (PCA), principal component regression (PCR), fuzzy logic analysis, and GIS mapping techniques. Fifty spatial datasets were used as shelterbelt establishment predictor variables (4 groups): 21 climate (1980–2010 normals), 13 land management, 14 soils, and 2 topographic criteria. A shelterbelt carbon inventory spatial layer was used as the shelterbelt establishment indicator dataset. Using PCA and PCR analyses, the overall importance (cumulative loading: positive or negative) of all predictor variables was determined and used to create shelterbelt suitability maps by means of weighted-sum overlays in GIS. Statistically significant positive correlations between mapped shelterbelt suitability levels and observed mean shelterbelt carbon stocks were used to evaluate the resulting deciduous (4.86 million hectares (Mha) study area; p = 0.0033, R2 = 0.79), coniferous (1.96 Mha; p = 0.0008, R2 = 0.77), and shrub suitability maps (2.06 Mha; p = 0.0002, R2 = 0.83). Additional 8.76, 7.90, and 9.77 Mha were identified as suitable for planting future deciduous, coniferous, and shrub shelterbelt systems, respectively, mapped as above-average or high suitability land. Shelterbelt suitability mapping is a means to delineating and ranking the land across large landscapes. The approach employed in this study can benefit other afforestation and agroforestry adoption studies across Canada and the world.

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This research was done by a team of researchers at the Centre for Northern Agroforestry and Afforestation at the University of Saskatchewan. Funding was provided by Agriculture and Agri-Food Canada (AAFC)’s Agricultural Greenhouse Gases Program (AGGP). We thank the AAFC Agroforestry Development Centre at Indian Head, SK for providing the shelterbelt tree database. We are grateful to D. Cerkowniak of AAFC at Saskatoon, SK for technical advice with the Soil Landscapes of Canada dataset, and J. Piwowar of the University of Regina for the digitized shelterbelts dataset.

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Correspondence to Beyhan Y. Amichev.

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Four tables are provided with detailed descriptions of 21 climate (Table S1), 13 land management (Table S2), 14 soils (Table S3), and 2 topography (Table S4) variables used to assess and map land suitability for shelterbelt establishment in the agricultural region of Saskatchewan, Canada. (PDF 171 kb)

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Amichev, B.Y., Laroque, C.P., Belcher, K.W. et al. Shelterbelt systems establishment in Saskatchewan, Canada: a multi-criteria fuzzy logic approach to land suitability mapping. New Forests 51, 933–963 (2020).

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  • Principal component analysis (PCA)
  • Principal component regression (PCR)
  • Fuzzy logic analysis
  • Shelterbelt agroforestry systems
  • Carbon sequestration rates
  • Agricultural land suitability