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The Chaoyang profile represents a rare multi-period, continuous and complete sequence of aeolian paleo-deposits with a stable sedimentary origin and multi-stage paleoclimatic cycles. Benchmark profiles including soil types at different pedogenic stages can be used for the recognition and classification of paleosols and paleoclimate reconstruction. The loess–paleosol sequence benchmark profile (LBP) is also helpful in comparing the results of paleoenvironment reconstruction from different ecological regions. In this study, a loess–paleosol profile derived from thick loess in Chaoyang city of Liaoning province, Northeast China, was investigated as a well-preserved LBP that included various paleosol types. To determine the nature and origin of the Chaoyang profile, the geographic, stratigraphic and morphological characteristics were described in the field. Bulk samples from 42 horizons were collected for chemical and physical analysis, and sub-sampling of 946 samples at 2 cm intervals from the surface to the bottom were taken to measure grain size distributions and magnetic susceptibility. Results showed that the 19.85 m thick loess–paleosol profile had been continuously deposited since 423 ka BP. The upper part (0–195 cm), or UPP, was predominantly of aeolian loess deposition origin but was mixed with water-reworked materials from a nearby secondary loess source. The middle part (195–228 cm), or MIP, was also indirectly affected by the water-reworking process through the leaching of materials from the overlying UPP. The lower part (228–1985 cm), or LOP, was characterized by four reddish stratigraphic layers interbedded with five yellowish ones, indicating several types of paleosols developed under different ecological environments. The multi-stage paleoclimatic cycles as evidenced by morphological and physical characteristics as well as age dating and magnetic susceptibility correlated well with the Lingtai section and LR04 benthic δ18O. Because of these attributes, the Chaoyang profile can be deemed as a benchmark loess–paleosol profile for the recognition and classification of paleosols and paleoclimate reconstruction in Northeast China. The differences in morphological and physical properties between paleosols and loess suggest different soil fertility and agronomic properties and need further studies to assess their functionality with climate fluctuation.

High-resolution maps of soil health measurements could help farmers finetune input resources and management practices for profit maximization. Within-field soil heath variations can be mapped using local topography and apparent electrical conductivity (ECa) as predictors. To address these issues, a study was conducted in Texas Blackland Prairie soils with the following objectives: (i) to assess and map within-field soil health variations using machine learning; (ii) to evaluate the usefulness of topography and ECa as soil health predictors; and (iii) to quantify the relationship between ECa and soil health index and use ECa to estimate soil health spatial distribution. We collected 218 topsoil (0–15 cm) samples following a 35 m × 35 m grid design and analyzed for one-day CO2, organic C, organic N, and soil health index (SHI) based on the Haney Soil Health Tool. A random forest model was applied to predict and map those properties on a 5 m × 5 m grid where ECa, and terrain attributes were used as predictors. Furthermore, the empirical relationship between SHI and ECa was established and mapped across the field. Results showed that the study area was variable in terms of one-day CO2, organic C, organic N, SHI, and ECa distribution. The ECa, wetness index, multiresolution valley bottom flatness, and topographic position index were among the top predictors of soil health measurements. The model was sufficiently robust to predict one day CO2, organic C, organic N (R2 between 0.24–0.90), and SHI (R2 between 0.47–0.90). Overall, we observed a moderate to strong spatial dependency of soil health measurements which could impact within-field yield variability. The study confirmed the applicability of easy to obtain ECa as a good predictor of SHI, and the predicted maps at high resolution which could be useful in site-specific management decisions within these types of soils.