Selecting soil hydraulic properties as indicators of soil health: Measurement response to management and site characteristics
Bibliographic record
Abstract
Abstract Farmers, scientists, and other soil health stakeholders require interpretable indicators of soil hydraulic function. Determining which indicators to use has been difficult because of measurement disconformity, spatial and temporal variability, recently established treatments, and the effect of site characteristics on management practice differences. The North American Project to Evaluate Soil Health Measurements includes 124 sites uniformly sampled across a range of soil health management practices in North America in 2019. We compare and recommend indicators of hydraulic function that best characterize soil health. We assessed the relationship of each indicator to a suite of soil inherent properties and climate variables, the response of each indicator to soil health management practices, the effect that soil inherent properties (clay content, sand content, and pH) and climatic variables (10‐yr mean annual precipitation and temperature) had on response to management practices, and the relationship among the responses of the indicators to soil health management practices. Field capacity measured on intact cores (θ FC_INTACT ) was the best measure of soil hydraulic function, because it responded to management, represents a direct measure of soil hydraulic function, is proximal to stakeholder values, and its response to management was not significantly influenced by inherent and climatic variables. Other suitable indicators are bulk density, soil organic carbon (SOC), and aggregate stability, which are not direct measures of soil hydraulic function but do respond to management and may be practical in situations in which measuring θ FC_INTACT is not. This study informs selection of soil health indicators to measure soil hydraulic function.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".