Modelling spatio-temporal patterns of soil carbon and greenhouse gas emissions in grazing lands: Current status and prospects
Bibliographic record
Abstract
The sustainability of grazing lands lies in the nexus of human consumption behavior, livestock productivity, and environmental footprint. Due to fast growing global food demands, many grazing lands have suffered from overgrazing, leading to soil degradation, air and water pollution, and biodiversity losses. Multidisciplinary efforts are required to understand how these lands can be better assessed and managed to attain predictable outcomes of optimal benefit to society. This paper synthesizes our understanding based on previous work done on modelling the influences of grazing of soil carbon (SC) and greenhouse gas emissions to identify current knowledge gaps and research priorities. We revisit three widely-used process-based models: DeNitrification DeComposition (DNDC), DayCent, and the Pasture Simulation model (PaSim) and two watershed models: The Soil & Water Assessment Tool (SWAT) and Variable Infiltration Capacity Model (VIC), which are widely used to simulate C, nutrient and water cycles. We review their structures and ability as process-based models in representing key feedbacks among grazing management, SOM decomposition and hydrological processes in grazing lands. Then we review some significant advances in the use of models combining biogeochemical and hydrological processes. Finally, we examine challenges of incorporating spatial heterogeneity and temporal variability into modelling C and nutrient cycling in grazing lands and discuss their weakness and strengths. We also highlight key research direction for improving the knowledge base and code structure in modelling C and nutrient cycling in grazing lands, which are essential to conserve grazing lands and maintain their ecosystem goods and services.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".