Digital Mapping of Soil Organic Matter and Cation Exchange Capacity in a Low Relief Landscape Using LiDAR Data

Abstract

Soil organic matter content (SOM) and cation exchange capacity (CEC) are important agronomic soil properties. Accurate, high-resolution spatial information of SOM and CEC are needed for precision farm management. The objectives of this study were to: (1) map SOM and CEC in a low relief area using only lidar elevation-based terrain attributes, and (2) compare the prediction accuracy of SOM and CEC maps created by universal kriging, Cubist, and random forest with Soil Survey Geographic (SSURGO) database. For this study, 174 soil samples were collected from a depth from 0 to 10 cm. The topographic wetness index, topographic position index, multi resolution valley bottom flatness, and multi resolution ridge top flatness indices generated from the lidar data were used as covariates in model predictions. No major differences were found in the prediction performance of all selected models. For SOM, the predictive models provided results with coefficient of determination (R2) (0.44–0.45), root mean square error (RMSE) (0.8–0.83%), bias (0–0.22%), and concordance correlation coefficient (ρc) (0.56–0.58). For CEC, the R2 ranged from 0.39 to 0.44, RMSE ranged from 3.62 to 3.74 cmolc kg−1, bias ranged from 0–0.17 cmolc kg−1, and ρc ranged from 0.55 to 0.57. We also compared the results to the USDA Soil Survey Geographic (SSURGO) data. For both SOM and CEC, SSURGO was comparable with our predictive models, except for few map units where both SOM and CEC were either under or over predicted.

Enzymatic Activities of Bok Choy (Brassica rapa subsp. Chinensis) Grown Soil with the Amendment of Sandwich Compost

“…CEC of Sandwich compost substrate amended soil showed significantly greater than Sandwichcompost substrate unamended ones [52]. CEC was largely affected by the amount of organic matter [53][54][55] including Sandwich compost substrate. erefore, large percentages of organic matter contribute to more negative sites (e.g., lignin derivatives including humic-like substances) and thus, a high CEC value [56].…”

Mapping subsurface tile lines on a research farm using aerial photography, paper maps, and expert knowledge

“…The Agronomy Center for Research and Education (ACRE) in Tippecanoe County, IN, USA (40°28′12″N, 86° 59′31″W) (Figure 1) was established in 1949 as a field research station for soils and crops research. As of 2020, it consisted of 570 ha (Rahmani et al., 2022). ACRE is located on a low relief, gently undulating Wisconsin age till plain and contains 14 different soil map units, with Chalmers (fine‐silty, mixed, superactive, mesic Typic Endoaquolls), Raub (fine‐silty, mixed, superactive, mesic Aquic Argiudolls), and Drummer (fine‐silty, mixed, superactive, mesic Typic Endoaquolls) soils as the dominant soil types.…”

“…The Agronomy Center for Research and Education (ACRE) in Tippecanoe County, IN, USA (40˚28′12″N, 86˚59′31″W) (Figure 1) was established in 1949 as a field research station for soils and crops research. As of 2020, it consisted of 570 ha (Rahmani et al, 2022) The tile lines at ACRE were typically installed at 0.9 m (3 ft) depth and at 20.16 m (66 ft) spacings. The lateral tile lines, which carry water from the fields to the larger main or submain tile lines, are typically 10 cm in diameter.…”

Digital assessments of soil organic carbon storage using digital maps provided by static and dynamic environmental covariates

“…It was also found that 19% of the study area was not identified as valley bottom areas. The ability and performance of TWI as well as MrVBF in predicting soil organic matter (SOM) was also documented by Rahmani et al (2022).…”