PROPOSED CHANGES TO THE SOIL FAMILY TAXON WITHIN THE CANADIAN SYSTEM OF SOIL CLASSIFICATION

The soil family was developed in the 1960s as the fourth level of taxa within the hierarchical structure of the Canadian System of Soil Classification. The original aim of the soil family category was to provide a framework for checking and establishing limits for soil series while providing a link between the series and the subgroup level. Its intended use was to define and group numerous soil series based on soil characteristics important for the purpose of applying appropriate management practices. In the current Canadian System of Soil Classification, taxa at the family level represent subdivisions of the subgroups. Classification of mineral soils at the family level is based on properties of the parent materials which include particle-size; soil mineralogy; reaction (soil pH); calcareousness; depth to bedrock and permafrost; as well as climactic factors: soil temperature and soil moisture regimes. The soil family particle-size classes were originally intended as a compromise between both agronomic and engineering influences; however, the resulting product has limited functionality because of differences in definitions between engineering and agronomic grain-sizes and non-alignment with soil textural classes. Consequently, classification and use of the family taxon has largely been ignored. Some adjustments to the family taxon for mineral soils and terric layers in organic soils are proposed including realignment of classes in the current family particle-size triangle to follow the divisions of the soil textural classes. Minor adjustments to mineralogy classes and depth to bedrock are also proposed.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Soil organic carbon content: decreases partly attributed to dilution by increased depth of cultivation in southern Ontario

Soil organic carbon contents and depths of Ap horizons (i.e., cultivated topsoil) from Ontario soil survey reports were reviewed, analyzed, and compared from 1950 to 2019. Organic carbon concentrations have declined from 2.85% to 2.34% in Ap horizons, whereas depths have increased by 40%. Considering the entire Ap horizon depth, we show that soil carbon stocks (kg C·ha−1) may be constant or increasing. Losses of organic carbon due to cultivation should not be discounted; however, dilution of organic carbon within a deeper plow layer may contribute significantly to observed decreases in organic carbon concentrations in topsoil.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

A framework for recalibrating pedotransfer functions using nonlinear least squares and estimating uncertainty using quantile regression

Pedotransfer functions (PTFs) have been developed for many regions to estimate values missing from soil profile databases. However, globally there are many areas without existing PTFs, and it is not advisable to use PTFs outside their domain of development due to poor performance. Further, developed PTFs often lack accompanying uncertainty estimations. To address these issues, a framework is proposed where existing equation-based PTFs are recalibrated using a nonlinear least squares (NLS) approach and validated on two regions of Canada; this process is coupled with the use of quantile regression (QR) to generate uncertainty estimates. Many PTFs have been developed to predict soil bulk density, so this variable is used as a case study to evaluate the outcome of recalibration. New coefficients are generated for existing soil bulk density PTFs, and the performance of these PTFs is validated using three case study datasets, one from the Ottawa region of Ontario and two from the province of British Columbia, Canada. The improvement of the performance of the recalibrated PTFs is evaluated using root mean square error (RMSE) and the concordance correlation coefficient (CCC). Uncertainty estimates produced using QR are communicated through the mean prediction interval (MPI) and prediction interval coverage probability (PICP) graphs. This framework produces dataset-specific PTFs with improved accuracy and minimized uncertainty, and the method can be applied to other regional datasets to improve the estimations of existing PTF model forms. The methods are most successful with large datasets and PTFs with fewer variables and minimal transformations; further, PTFs with organic carbon (OC) as one of or the sole input variable resulted in the highest accuracy

An Extensive Field-Scale Dataset of Topsoil Organic Carbon Content Aimed to Assess Remote Sensed Datasets and Data-Derived Products from Modeling Approaches

The geosciences suffer from a lack of large georeferenced datasets that can be used to assess and monitor the role of soil organic carbon (SOC) in plant growth, soil fertility, and CO2 sequestration. Publicly available, large field-scale georeferenced datasets are often limited in number and design to serve these purposes. This study provides the first publicly accessible dataset of georeferenced topsoil SOC measurements (n = 840) over a 26-hectare (ha) agricultural field located in southern Ontario, Canada, with a sampling density of ~32 points per ha. As SOC is usually influenced by site topography (i.e., slope and landscape position), each point of the database is associated with a wide range of remote sensing topographic derivatives; as well as with normalized difference vegetation index (NDVI) based value. The NDVI data were extracted from remote sensing Sentinel-2 imagery from over a five-year period (2017–2021). In this paper, the methodology for topsoil sampling, SOC measurement in the lab, as well as producing the suite of topographic derivatives is described. We discuss the opportunities that the database offers in terms of spatially explicit and continuous soil information to support international efforts in digital soil mapping (i.e., SoilGrids250m) as well as other potential applications detailed in the discussion section. We believe that the database with very dense point location measurements can help in conducting carbon stocks and sequestration studies. Such information can be used to help bridge the gap between ground data and remotely sensed datasets or data-derived products from modeling approaches intended to evaluate field-scale rates of agricultural carbon accumulation. The generated topsoil database in this study is archived and publicly available on the Zenodo open-access repository