Soil health and management assessment kit (SOHMA KIT®): Development and validation for on-farm applications
Bibliographic record
Abstract
Soil health is a foundation for long-term soil multifunctionality, sustaining crop yields and enhancing crop resilience to climate change. Nevertheless, soil health assessments are often complex, costly and time-consuming, which acts as a barrier to farmers adopting them. Thus, we hypothesized that a simplified, on-farm approach to evaluate soil health, composed by key indicators, could effectively detect changes in soil health across different land management systems. This study aimed to (i) validate the Soil Health and Management Assessment Kit (SOHMA KIT®) as a reliable tool for on-farm soil health assessment, (ii) compare its performance with standard laboratory methods, and (iii) assess its sensitivity for detecting soil health improvements induced by cover crops. The validation study was conducted in two long-term field experiments in the Brazilian savanna (Cerrado biome), where different cover crop systems were evaluated. After extensive work involving literature review, selection and development of methods, the SOHMA KIT® was created. The SOHMA KIT® integrates seven soil health indicators from physical (infiltration, aggregate stability, Visual Evaluation of Soil Structure - VESS), chemical (pH), and biological (catalase enzyme, macrofauna, biogenic aggregates) components into a Soil Health Index (SHI). In the validation tests, results showed that the SHI increased around 35 % in diversified cropping systems. Strong correlations between SOHMA KIT® and standard methods were observed for key indicators (e.g., infiltration: r = 0.71, aggregate stability: r = 0.40, pH: r = 0.88). Despite its portability and cost-effectiveness, the toolkit has some limitations, such as it is recommended that users have a basic training for assessing visual indicators, and the assessment is focused only on topsoil layers. However, the SOHMA KIT® is user-friendly and scalable, being a valuable tool for on-farm decision-making, regenerative agriculture, and large-scale soil health monitoring. • SOHMA KIT® is a new on-farm framework for assessing soil health. • SOHMA KIT® evaluates physical, chemical, and biological soil indicators. • SOHMA KIT® consists of seven field-based, time- and cost-effective methods. • The indicators have been validated for reliability, sensitivity and practicality for the Brazilian savannah. • Cover crop improved the Soil Health Index by up to 35 % detected by SOHMA KIT®.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".