Soil friability (F) is one of the key indicators of soil structural conditions and can be used to assess the environmental impacts of different tillage and no-tillage practices on crop growth and yield. F can be affected by several driving factors such as soil (physical and chemical parameters) and topographic attributes and different land uses as well. Few studies have been carried out to identify those driving factors as well as to quantify their relationships with F at watershed scale. The objectives of this study were (i) to characterize the spatial variability of F and further soil and topographic attributes across a spatial transect of 11.2 km in length using classical statistics; (ii) to quantify the spatial relationships of F and driving factors using the state-space approach; and (iii) to compare the performance of state-space models in estimating F with those of corresponding linear and multiple regression models. The transect was established in the Micaela river watershed (MRW), Southern Brazil, with equidistantly distributed soil sampling points. Soil textural fractions (sand, clay and silt contents), organic matter content, bulk density, macroporosity, and F were determined in the 0−0.10 m layer at each sampling point. The digital elevation model provided elevation and soil slope as topographic attributes while a land use map was obtained from satellite images. All state-space models achieved better results in describing the spatial relationships of F and the further soil and topographic attributes than the corresponding multiple linear regression models. State-space analysis showed that soil slope could be used as a proxy to predict local variations of F in the MRW. The spatial covariance information should be included in the development of pedotransfer functions for estimating the spatial variation of F and the type of land use (as a soil structural indicator) should be investigated in future studies at watershed scale.

土壤脆性（F）是土壤结构条件的关键指标之一，可用于评估不同耕作和免耕方式对作物生长和产量的环境影响。F可能受到多种驱动因素的影响，例如土壤（物理和化学参数）和地形属性以及不同的土地用途。很少进行研究来识别这些驱动因素以及在分水岭尺度上量化它们与F的关系。这项研究的目的是：（i）使用经典的统计数据，在长度为11.2 km的空间样线上表征F的空间变异性以及其他土壤和地形属性；（ii）使用状态空间方法量化F和驱动因子的空间关系；（iii）将状态空间模型在估计F时的性能与相应的线性和多元回归模型的性能进行比较。该样条线建立在巴西南部的米卡埃拉河流域（MRW）上，等距分布的土壤采样点。在每个采样点的0-0.10 m层中确定土壤质地分数（沙，粘土和粉沙含量），有机质含量，堆积密度，大孔隙度和F。数字高程模型将高程和土壤坡度作为地形属性，而从卫星图像获得土地使用图。与相应的多元线性回归模型相比，所有状态空间模型在描述F的空间关系以及其他土壤和地形属性方面都取得了更好的结果。状态空间分析表明，土壤坡度可以作为预测MRW中F的局部变化的代用品。空间协方差信息应包括在pedotransfer函数的开发中，以估计F的空间变化，并应在未来的分水岭规模研究中调查土地利用的类型（作为土壤结构指标）。