Nicolas Adam Jelinski

This paper establishes standardized terminology and field documentation protocols for cryostructures and cryogenic soil structures in permafrost‐affected soils and provides brief guidance on descriptions of ground ice morphology and ice volume estimates. We consolidate permafrost terminology from Russian and North American literature, clarify long‐standing ambiguities, and provide explicit guidelines that align with US Department of Agriculture‐Natural Resources Conservation Service soil description standards. Our scheme makes critical distinctions between cryostructure, the distribution of ice within soil, and cryogenic soil structure, the morphological structure of soil resulting from ice formation. The scheme organizes cryostructures into three main categories: non‐segregated ice, visible segregated ice, and ice matrices. We introduce standardized codes and parameters for field descriptions of ice and soil that enable machine‐readable data collection compatible with existing soil information systems. This standardization will significantly enhance the integration of field observations into landscape‐scale assessments of permafrost stability, infrastructure vulnerability, and ecosystem response to permafrost thaw, addressing an urgent need for quantitative data to inform modeling and decision‐making in rapidly changing Arctic and subarctic environments.

Soil structure is a standard component of pedon descriptions collected to characterize the nature and function of soils as a natural body. Soil structure is an important indicator of soil development and a predictor of water, air, and root movement throughout the profile. However, inconsistencies exist within the current standardized methodologies used to describe soil structure in the field, making soil structure data difficult to use and often confusing to students encountering these concepts for the first time. Therefore, we propose an updated method of soil structure description to add an interpretive component that clearly distinguishes geogenic, pedogenic, and human‐induced structures. This method employs terminology that communicates both observable morphology and the mechanism of formation. The updated method herein differentiates three classes of structure: (a) geogenic structural formation (GS), (b) natural pedological structural formation (current nomenclature), and (c) significant human‐induced structure changes from natural pedological structure. This updated approach improves the documentation of scientifically relevant soil morphology and communicates the processes involved in the formation of all forms of soil structure. As a result, this change eases description challenges around soil structure and interpretations common in soil science classes, including soil judging.

Constraining the variability of soil organic carbon (SOC) and total nitrogen (TN) stocks across hillslopes in Low Arctic permafrost‐affected landscapes remains a significant challenge for improving global estimates of permafrost SOC stocks. We investigated SOC and TN stocks across hillslopes at two sites in the Arctic Foothills of Alaska, United States (Happy Valley and Sagwon Hills). Average SOC and TN stocks for the 0–1‐m depth interval were high (52.0 ± 15.1 kg C m⁻² and 2.74 ± 0.82 kg N m⁻²) and linearly related (R² = 0.74, p < 0.0001). Unlike soils of other permafrost and nonpermafrost landscapes, variability was greatest within rather than between hillslope positions. Furthermore, SOC and TN stocks in the surface 1 m did not exhibit strong patterns by hillslope position and were only weakly associated with major geomorphic parameters that typically predict SOC and TN stocks well in other landscapes. Although sampling at upper hillslope positions was largely limited to depths of less than 1.5 m due to the presence of coarse fragments in reworked glacial till, deeper observations at lower hillslope positions (footslopes, toeslopes, and basins) revealed significantly larger SOC stocks (92.0 ± 18.0 kg C m⁻² at 2 m; 117.1 ± 10.4 kg C m⁻² at 3 m). The unique small‐scale variability in ice content, cryoturbation, patterned ground, and organic layer thickness on these broad, Low Arctic sites contributes to the relatively homogeneous distribution of SOC and TN stocks across hillslope positions in the top 1 m, but a future focus on deeper sampling may reveal greater differences in SOC and TN stocks.

A major challenge to implementing effective soil health assessments is how to distinguish the effects of management from underlying soil variability driven by inherent soil properties. This challenge has important consequences for the use of soil health indicators as tools for monitoring and assessment because soil‐forming factors constrain the range of indicator values and the magnitude of management‐induced changes. Here, we present results from a statewide survey of 15 soil health indicators measured on 30 fields on commercial farms across four major land resource areas in Minnesota. Fields included in the study differed in tillage, cover crop implementation, and crop rotation. Within each field, we collected samples from upper and lower hillslope positions to quantify the effect of topography. We consistently detected differences in soil health indicators between regions (13/15 indicators) and hillslope positions (8/15 indicators). However, only wet aggregate stability was sensitive to management across regions and years, highlighting the importance of physical indicators of soil health. This result was surprising in light of existing literature supporting the sensitivity of many soil health indicators to management, but it is consistent with other studies conducted in high organic matter soils in the Upper US Midwest. Our results highlight the need for regionally representative datasets to guide the development of interpretations and benchmarks for soil health indicators. This is particularly important when soil health indicators are applied outside traditional research contexts (such as in commercial soil health testing), where interpretation must take place without the benefit of historical baseline data.

Gelisols (permafrost‐affected soils in US Soil Taxonomy) are extensive in Alaska, currently occurring on ∼45% of the land area of the state. Gelisol taxonomic criteria rely on the presence of near‐surface (less than 2 m deep) permafrost, but ongoing climatic and environmental change has the potential to affect the presence of near‐surface permafrost across much of Alaska throughout the 21st century. In this study, we utilized scenarios of near‐surface permafrost loss and active layer deepening through the 21st century under low (SRES B1, RCP 4.5), mid‐ (SRES A1B), and high (SRES A2, RCP 8.5) emissions scenarios, in conjunction with the statewide STATSGO soil map, to generate spatially explicit predictions of the susceptibility of Gelisols and Gelisol suborders to taxonomic change in Alaska. We find that 15%–53% of Alaskan Gelisols are susceptible to taxonomic change by mid‐century and that 41%–69% of Alaskan Gelisols are susceptible to taxonomic change by the end of the century. The extent of potential change varies between suborders and geographic regions, with Gelisols in Northern Alaska being the most resilient to taxonomic change and Western and Interior Alaskan Gelisols most susceptible to taxonomic change. The Orthel suborder is likely to be highly restricted by the late 21st century, while Histels and Tubels are more likely to be of greater extent. These results should be taken into consideration when designing initial survey and re‐mapping efforts in Alaska and suggest that Alaskan Gelisol taxa should be considered threatened soil taxa due to the proportional extent of likely loss.

Soil health assessments aim to quantify soil health status using indicators linked to ecosystem services such as yield, nutrient cycling, water cycling, or carbon storage. Many indicators are related to soil biological processes, which can be challenging to interpret because they are sensitive not only to management, but also to nonmanagement variables such as soil inherent properties, topography, and climate. Existing studies address this challenge by grouping similar soils by taxonomy, geography, or a combination of these and other variables for soil health assessment. We investigated whether grouping soils based on multiple quantitative topsoil properties could be an alternative to taxonomic or geographic groups. We used an unsupervised classification algorithm, k‐means, to cluster publicly available soil and climate data for Minnesota. Clustering into eight conceptual groups (“clusters”) based on 10 topsoil properties was determined to be the optimal algorithm output. We evaluated the ability of our soil clusters and other grouping methods to explain variance in eight soil health indicators. We found the combination of Major Land Resource Area (MLRA) and soil cluster performed best, explaining as much or more variance than other groupings for five of the eight indicators. The clusters distinguish zones of topsoil variation at the field scale, and MLRAs account for broader scale variation in climate and other landscape factors. The approach we describe is flexible and could be applied at different locations and scales to produce conceptual soil groups and associated maps to support soil health test sampling and interpretation at the field scale.

Student–student interactions are influential parts of field experiences. While competitive judging events are a fun way to engage students in field‐based learning, the focus on competition leads to an atmosphere that discourages collaboration between students. The objective of this study was to evaluate the cognitive and affective learning outcomes resulting from intercollegiate collaboration at a soil judging competition. Teams with students from two to three different universities were assigned and referred to as jumble judging teams. Jumble judging was held for the first time in the 2021 Region 5 Collegiate Soil Judging Contest. Learning outcomes were assessed using a pre‐ and postsurvey, as well as group and individual reflections completed in the field. Student responses were generally positive, with 70% of students expressing agreement or strong agreement that they would like jumble judging to be included in future contests, 54% citing jumble judging as one of the best parts of the contest, and 93% identifying learning outcomes or describing an affective learning experience resulting from jumble judging. Evidence of both cognitive and affective learning were identified through student surveys and reflections. Overall, the event created a collaborative and collegial atmosphere and increased interaction between students from different universities, while maintaining the competitive nature of the event that motivates many students to get involved with judging teams.

As with many aspects of teaching, the COVID‐19 pandemic forced soil judging teams to attempt new strategies towards achieving student learning outcomes. Soil judging Regions IV and V hosted remote regional contests in October 2020 in place of traditional, in‐person contests typically held each fall. We conducted pre‐ and post‐contest surveys to assess student learning outcomes, attitudes, and reflections on the remote contest experience compared to past, in‐person contest experiences. We received 108 total responses from students who participated in the Region IV and Region V remote soil judging contests (>80% response rate). In self‐reported learning outcomes, there were no significant gains post‐contest and there were minimal differences between students in Regions IV and V. Female students, students with more soil judging experience, and students who had taken more soil science courses agreed more strongly that soil science is important, that they planned to pursue careers in soil science, and that they gained important skills from soil judging. Finally, students who previously participated in contests reported that they gained more knowledge and enjoyed in‐person contests more than the remote contests held in Fall 2020. Thus, while it is possible to replicate some aspects of the soil judging experience in a remote contest, other aspects that are critical to student engagement are lost when teams are unable to gather at the contest location and examine soils in the field.

Mollisols are dark colored, carbon‐rich mineral soils occupying a large proportion (836 soil series) of the soils of the Central Plains of the United States. By contrast, only eight official soil series of Mollisols have been mapped in Alaska (six Haplocryolls, one Calcicryoll, one Haplogeloll). Little information exists about Geloll pedogenesis, taxonomic variability, and extent. In this study, 39 horizons were morphologically described across ten Geloll pedons in northern Alaska. Based on analogous taxonomic structure in Cryolls, two pedons would meet the criteria for a Fluventic subgroup as the organic carbon content was ≥0.3% (1.47% and 0.88%) at a depth of 125 cm below the mineral soil surface. Three pedons would meet the criteria for a Pachic subgroup because the mollic epipedon was thicker than 40 cm (52 cm, 53 cm, and 54 cm) and the texture class was finer than loamy fine sand (sandy loam). However, no Fluventic or Pachic subgroups currently exist for Haplogelolls. Two pedons were classified as Cumulic Haplogelolls, and three pedons were Typic Haplogelolls. Field and laboratory characterization allowed for the development of a Geloll pedogenic concept generally represented by well‐drained soils with limestone and/or dolomitic parent material. Such soils generally feature thin or no surficial organic horizons and large quantities of coarse fragments and coarse textured materials. Soil organic carbon calculations for suspected areas of Alaska Gelolls total 1.18 Pg; closely aligning with estimates of previous studies. Future work should expand the explanatory taxonomy based on new morphological expressions observed throughout gelic temperature regime regions.