Estimating Soil Surface Roughness With Models Based on the Information About Tillage Practises and Soil Parameters. (28th February 2022)

• Record Type: Journal Article
• Authors: Herodowicz‐Mleczak, Karolina; Piekarczyk, Jan; Kaźmierowski, Cezary; Nowosad, Jakub; Mleczak, Mateusz
• Is Part Of: Journal of advances in modeling earth systems. Volume 14:Number 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-28
• Subjects: soil roughness -- roughness indices -- soil properties -- tillage tools -- predictive models; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2021MS002578
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: The quantitative description of the soil surface roughness is necessary for effective monitoring of wind, water and tillage erosion, hydrological processes or greenhouse gas emissions. The aim of this work was to build soil roughness predictive models based on the type of tillage tool, the roughness indices and soil properties. The roughness formed by five tillage tools was determined. Two surface roughness indices: Height Standard Deviation (HSD) and T3D (Tortuosity index) were calculated from Digital Elevation Model. The both roughness indices demonstrated a significant correlation, however, they provided different information about soil roughness. The HSD describes roughness on the "macro, " while T3D refers to the "micro" scale. Hence, our findings show that a single index is not sufficient to describe the roughness of post‐treatment surface. The linear and random forest models were built to describe the relationships between the roughness indices, type of tillage tool and soil properties. The HSD analysis indicated that the type of tillage tools had the greatest impact on post‐treatment roughness. In contrast, T3D analysis found soil texture to have a significant effect, together with tillage tools. In all modeling scenarios, T3D was more accurately predicted than HSD by both the linear ( R 2 = 0.62 vs. R 2 = 0.60) and random forest models ( R 2 = 0.58 vs. R 2 = 0.55). Predictive soil surface roughness models can be applied effectively for estimating water retention in soil, the intensity and speed of surface water flow, soil erosion, the level of reflected shortwave solar radiation or soil properties by remote sensing techniques. Plain Language Summary: The acreage of arable soils decreases along with the intensification of agricultural production. Intensive use of farmlands can lead to soil degradation, manifested by increased susceptibility to erosion, loss of organic matter, decline in fertility and structural soil condition and reduced crop yields. Thus, soil degradation is a very important in views of world food security and environmental quality. In order to effectively limit the deterioration of soil quality as a result of agricultural activity, it is necessary to develop methods of soil condition monitoring that would enable the assessment of the risk of erosion. One of the important physical soil characteristics that can be used to assess soil condition is its surface roughness. In this study the relationships between the roughness indices, type of tillage treatment and soil properties were described using the linear and random forest models. Information about the tillage tool type is needed to predict soil surface roughness however, when soil properties are included in the modeling, the reliability of the prediction increased significantly. Predictive soil surface roughness models can be applied effectively for a range of tasks, such as estimating water retention in soil and the intensity and speed of surface water flow. Key Points: Soil surface roughness formed by five selected tillage tools was analyzed Roughness in terms of Height Standard Deviation was mainly formed by the tillage tool when in terms of T3D was influenced by both tillage tools and soil texture Predictive models for soil surface roughness were created