University of Bonn, Institute of Crop Science and Resource Conservation (INRES), Crop Science Group, Katzenburgweg 5, 53115 Bonn, Germany.
University of Helsinki, Department of Agricultural Sciences\, 00014 Helsinki, Finland.
Sci Total Environ. 2022 Jul 1;828:154567. doi: 10.1016/j.scitotenv.2022.154567. Epub 2022 Mar 14.
Water erosion is one of the soil degradation processes driven by environmental and field factors such as rainfall intensity, slope gradient, dynamics of vegetation cover, soil characteristics, and management practices. Most of the studies assess the separate contribution of these factors under controlled conditions. However, there is a lack of adequate knowledge regarding the complex interactions between prevailing factors and soil erosion processes under heterogeneous field conditions. This study investigated 16 combinations of 5 factors at 4 levels of each factor on the soil erosion process using Taguchi's fractional factorial experiment design, identifying the factor combinations resulting in maximum sediment yield, runoff, organic carbon, and nitrogen losses. We considered the factors: Soil organic matter and silt content (SiltOM), vegetation cover (VC), slope steepness (SS), rainfall intensity (RI), and depth to a loamy layer (DLL). The interactive effects of these factors and their combinations were visualized from the analysis of signal-to-noise (S/N) responses. Results indicated that interactions between the selected factors and soil erosion processes exist and multiple linear regression models were developed to predict sediment yields, runoff, carbon, and nitrogen losses at the sub-field scale. Results revealed that 1) RI with 40.6% showed the highest contribution to sediment yield followed by SS (23.8%), VC (17.74%), SiltOM (14.77%), and DLL (3.17%), indicating a strong rainfall-erosion relationship; 2) the combination of levels of factors generating highest sediment yield was determined; 3) A simple multiple linear regression model developed for predicting local sediment yield showed the highest agreement with field observations (R = 82.5%). The findings suggest that Taguchi design could be used reliably for modeling soil erosion at field and sub-field scales. Using local calibration data such models have great potential for soil erosion risk assessments at the field scale, especially in areas where contributing factors and factor levels change at small spatial scales.
水蚀是由降雨强度、坡度梯度、植被覆盖动态、土壤特性和管理实践等环境和田间因素驱动的土壤退化过程之一。大多数研究在受控条件下评估这些因素的单独贡献。然而，对于在异质田间条件下普遍存在的因素与土壤侵蚀过程之间的复杂相互作用，缺乏足够的知识。本研究使用田口分因子实验设计，在 5 个因素的 4 个水平下，对 16 种因素组合进行了土壤侵蚀过程的研究，确定了导致最大泥沙产量、径流量、有机碳和氮素流失的因素组合。我们考虑了土壤有机质和粉土含量（SiltOM）、植被覆盖（VC）、坡度陡峭度（SS）、降雨强度（RI）和粘土层深度（DLL）等因素。从信号噪声（S/N）响应分析中可以看出这些因素及其组合的相互作用。结果表明，所选因素与土壤侵蚀过程之间存在相互作用，并建立了多元线性回归模型，以预测次田间尺度的泥沙产量、径流量、碳和氮素流失。结果表明：1）RI 以 40.6%的贡献率最高，其次是 SS（23.8%）、VC（17.74%）、SiltOM（14.77%）和 DLL（3.17%），表明降雨与侵蚀之间存在很强的关系；2）确定了产生最大泥沙产量的因素组合；3）开发的用于预测局部泥沙产量的简单多元线性回归模型与田间观测结果具有最高的一致性（R = 82.5%）。研究结果表明，田口设计可用于可靠地模拟田间和次田间尺度的土壤侵蚀。使用局部校准数据，此类模型在田间尺度上进行土壤侵蚀风险评估具有很大的潜力，尤其是在空间尺度较小的情况下，导致因素和因素水平发生变化的地区。
