The pros and cons of increasing soil organic matter in dryland cropping systems
Bibliographic record
Abstract
Soil organic matter (SOM) in drylands accounts for approximately 33% of global soil organic carbon (SOC) stocks and regulates many processes. Anthropogenic activities and climatic changes have influenced, and continue to significantly influence SOM contents. However, management practices that improve the soil carbon (C) and macronutrient balance can increase or maintain SOM. These include (1) maximising C inputs from grain crops, (2) integrating livestock and pasture phases, (3) using cover crops, (4) intercropping, (5) managing tillage and stubble, and (6) organic amendments. Estimated SOC increases achievable in drylands, ranging from 60 to 114 kg C ha year−1, fall short of the ambitious ‘4 per mille’ target, which is equivalent to a 0.4% annual increase in initial soil C, or at least 240 kg C ha−1 year−1 for drylands (assuming a global mean dryland SOC stock of 60 Mg C ha−1). In dryland systems, we propose a more rational approach, advocating for context-specific optima with a clear understanding of the benefits and costs to evaluate the suitability of management practices for improving SOM. The benefits include amelioration of soil constraints, improving nutrient and water availability, enhancing system resilience and sustainability, and potential participation in C markets. However, costs can be significant and are typically divided into the following two main categories: (1) economic (e.g. financial costs required for implementing management practices), and (2) environmental (e.g. the potential for increased nutrient loss via emissions or leaching as a result of enhanced nutrient cycling). The net benefit or cost is highly context-dependent, with the unique challenges of dryland environments being often overlooked in the literature. This review examines the primary strategies for maintaining or increasing SOM in dryland arable systems, the associated benefits and costs, methods for monitoring SOC stocks, and future challenges and opportunities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".