Soil Degradation and Climate Change Relationships: A Review
Bibliographic record
Abstract
Climate change, driven by anthropogenic greenhouse gas (GHG) emissions like carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O), is expected to worsen extreme weather events such as droughts, altered precipitation, floods, and wildfires by the century's end. These extreme weather events can exacerbate soil degradation, diminishing soil quality and productivity, with significant implications for food security globally. In this review, we describe the interconnections between climate change and soil degradation, especially those that control nutrient cycling and GHG emissions. The key climate change drivers of soil degradation are extreme precipitation patterns and elevated atmospheric temperatures, which intensify both short- and long-term effects on soil physical, chemical, and biological properties. The rise in temperatures can lead to increased soil compaction, destabilizing soil structure and reducing soil porosity. This impairs soil aeration, diminishing both macro and microbial activity, which disrupts nutrient cycling and contributes to soil degradation. Increased flooding promotes leaching and soil erosion which increases soil organic matter (SOM) and nitrogen (N) losses. This destabilizes soil N stocks and can retard proper crop growth. Frequent droughts inhibit enzymatic activities such as phosphatase responsible for phosphorus (P) mineralization, reducing the amount of phosphates available for plant uptake. Additionally, the continuous rise in temperature increases microbial activity resulting in increased SOM decomposition and release of CO2 into the atmosphere contributing to global warming. The changing precipitation patterns, especially intensive precipitation, increase anaerobic soil conditions which decrease soil microbial activity, thereby disrupting nutrient cycling. The above changes referred to as climate change-induced soil degradation in this review, alter the capability of soil properties to sustain food security and soil health necessitating the integration of adaptation and mitigation strategies to ensure sustainable functioning of terrestrial agroecosystems. Addressing this critical issue, we have identified the challenges in mitigating these impacts and proposed remediation strategies based on existing scientific knowledge.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".