Location: Dale Bumpers Small Farms Research CenterTitle: Mapping parent material as part of a nested approach to soil mapping in the Arkansas River Valley
|RICHTER, JENNY - Orise Fellow
|LIBOHOVA, ZAMIR - Natural Resources Conservation Service (NRCS, USDA)
|ADHIKARI, KABINDRA - University Of Arkansas
|FUENTES PONCE, BRYAN - University Of Arkansas
Submitted to: Catena
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/28/2019
Publication Date: 3/9/2019
Citation: Richter, J., Owens, P.R., Libohova, Z., Adhikari, K., Fuentes Ponce, B. 2019. Mapping parent material as part of a nested approach to soil mapping in the Arkansas River Valley. Catena. 178:100-108. https://doi.org/10.1016/j.catena.2019.02.031.
Interpretive Summary: Soils are affected by the length of time they have been forming, the climate, type of vegetation, parent material, and location within the landscape. These factors exert an influence on soil formation at different geographic scales and thus it is possible to map the broadest factor and subdivide it sequentially by each of the remaining factors to create a detailed soil map. This hierarchical approach to mapping is useful because it provides a framework for grouping functionally related soils together at multiple scales and organizing our conceptual understanding of soil-landscape relationships. ‘Parent material’ is the substance within which a soil develops. As with human parents and children, a soil inherits many of its physical and chemical characteristics from its parent material. Thus, properties such as the thickness of the soil, quantity and size of rocks, the rate that water can soak into the soil while also retaining water for plant growth, the inherent fertility and ability to retain nutrients for plant use, and the ease with which it can be eroded are tremendously influenced by parent material. Given that soil properties can vary drastically with different parent materials, an accurate and detailed map of parent material distribution is essential for predicting soil distribution and functioning across the landscape. The objective of this study was to develop a model to predict parent material distribution in the Arkansas River Valley using a digital mapping technique that imposes quantitative thresholds on terrain attributes (landscape features). Two major parent materials are recognizable in the study area, namely (i) sandstone and shale bedrock, and (ii) thick deposits of finer sediment eroded from nearby hills. Bedrock-derived soils are found on hills and ridges, retaining their prominence due to more resistant parent material. They are generally shallow and rocky soils prone to erosion. On the other hand, areas that are low and flat tend to accumulate sediment that is eroded from the uplands, leading to thicker soils that have a finer texture and no rock fragments. A 5-m digital elevation model was used to derive terrain attributes that were evaluated for their ability to distinguish the two parent materials using expert knowledge and field reconnaissance. Threshold values were determined for three terrain attributes to create a model of parent material distribution. The model was validated by sampling twenty soils chosen using a stratified random sampling approach. Based on these validation samples, the overall accuracy of our parent material model is 75%. The resulting parent material map was also compared with a traditional soil survey map that includes information on parent material, with 90% agreement of parent material designation between the two maps. This study confirmed the feasibility of separating parent material using digital terrain attributes in a highly weathered landscape, which can be used as an input for development of a detailed digital soil map. Incorporating parent material distribution as part of the hierarchical approach to digital soil mapping aids in constraining and predicting soil properties, enables a more straightforward examination of physiographic context, and can ultimately lead to more accurate digital soil maps for land management.
Technical Abstract: Soil mappers have traditionally relied on tacit knowledge and qualitative assessment of soil-landscape relationships to obtain the physiographic context necessary to predict soil distribution and spatial patterns. This assessment implicitly utilizes a nested hierarchical approach based on differences in the phenomenon scale of soil forming factors where climate, landscape, parent material, and topography are examined in sequence to create a model of soil-landscape relationships. Our objective was to predict parent material distribution using expert knowledge paired with quantitative digital terrain attributes as part of a hierarchical approach to digital soil mapping. The study took place at an 890 hectare research farm in Logan County, Arkansas, which is part of the Arkansas River Valley. Two major groups of parent material are identifiable in the Arkansas River Valley: residual sandstone and shale on erosional uplands, and silty/clayey pedisediment in depositional areas moved by sheet erosion from nearby uplands. A 5-m digital elevation model was used to derive thirteen terrain attributes for the study site. Three of the terrain attributes, namely topographic position index, multi-resolution valley bottom flatness, and vertical distance to channel network, were utilized as part of a rule-based approach to model parent material distribution based on preliminary reconnaissance and expert knowledge of the area. The model was validated by sampling 20 locations using a conditioned Latin hypercube sampling (cLHS) design to evaluate the prediction accuracy. Seventy-five percent of cLHS samples were accurately predicted to be residuum or pedisediment. The resulting map also had 90% agreement with the National Cooperative Soil Survey map; however, the digital map was able to provide more spatially explicit information on inclusions. Incorporating parent material distribution as part of a nested hierarchical approach to digital soil mapping aids in constraining and predicting soil properties, enables a more straightforward examination of physiographic context and can ultimately lead to more accurate digital soil maps for land management.