Greenhouse gas emissions and carbon sequestration associated with Integrated Crop–Livestock–Forestry (ICLF) systems
Bibliographic record
Abstract
Greenhouse gas (GHG) emissions from the Agriculture, Forest, and Other Land Use sector account for 23% of net global anthropogenic emissions. However, effective conservation agriculture practices can sequester carbon (C) up to 1 m in soil depth and vegetation biomass. Integrated Crop–Livestock–Forestry (ICLF) systems attempt to ensure sustainable agricultural production by combining various agricultural, livestock, and forestry production systems. This bibliographic review aims to present and discuss ICLF systems and their advantages and disadvantages compared with conventional systems, achieving a better understanding of the sources and sinks of GHGs (CH 4 , N 2 O, and CO 2 ). Integrated systems exist in tropical and temperate climates with different practices, arrangements, designs, and modalities. Intercropping species with different root structures provides more ecological interactions that enhance biodiversity, soil quality, and C sequestration while reducing GHG emissions. Yet, ICLF systems are complex and require technical assistance and support for being implemented, besides an extensive initial investment that increases the cost of operation. A relevant carbon sink in ICLF systems is carbon from tree biomass, where the wood can be used for various purposes. Timber for sawmills and general construction has a longer C immobilization time. Methane from enteric fermentation is the greatest contributor to GHG emissions in livestock and ICLF systems. Nitrous oxide is released primarily from synthetic fertilizers (when applied), manure deposited on pastures, and the decomposition of plant residues. Carbon dioxide is emitted to a lesser extent from the application of lime and urea. Many studies do not include all compartments in the C balance and often focus on only one GHG or compartment of C. Accordingly, more studies on the sources and sinks of C and their potential to offset GHG emissions in terms of CO 2 equivalent are urged.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".