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.

缺乏指导方针来帮助土地管理者找到合适的区域，以便在其土地上种植新的防护林带农林业系统。这项研究的目的是为加拿大萨斯喀彻温省广泛的气候和土壤区域的落叶，针叶和灌木防护林农林业系统建立土地适宜性图。使用多元主成分分析（PCA），主成分回归（PCR），模糊逻辑分析和GIS映射技术分析了空间防护林数据和50个预测变量套件。使用五十个空间数据集作为防护林建立预测变量（4组）：21种气候（1980-2010年正常值），13种土地管理，14种土壤和2种地形标准。防护林碳清单空间层用作防护林建立指标数据集。使用PCA和PCR分析，确定了所有预测变量的总体重要性（累积负荷：正或负），并通过GIS中的加权和叠加来创建防护林适宜性图。测得的防护林带适宜性水平与观察到的防护林带碳储量之间的统计显着正相关被用于评估最终的落叶（486万公顷（Mha）研究区域；p  = 0.0033，R ²  = 0.79），针叶树（1.96 Mha；p  = 0.0008，R ²  = 0.77）和灌木适宜性图（2.06 Mha；p  = 0.0002，R ²  = 0.83）。确定了另外的8.76、7.90和9.77 Mha分别适合种植未来的落叶，针叶和灌木防护林系统，这些系统被绘制为高于平均水平或高度适宜的土地。防护林适宜性制图是一种在大型景观中划定土地并对其进行排名的方法。这项研究中采用的方法可以使加拿大乃至全世界的其他造林和农林收养研究受益。