Potential contribution of water management practices under intensive crop production to climate-change-associated global warming
Bibliographic record
Abstract
Optimizing water management practices to mitigate greenhouse gas (GHG) emissions in agroecosystems is of increasing interest under climate change. However, previous studies have focused more on paddy fields, little is known as to how water table management in subsurface drained fields or how irrigation practices in humid areas might affect the global warming potential (GWP). Drawing upon experimental data from four intensive crop production sites in Eastern Canada operating under different water management practices, the DeNitrification-DeComposition (DNDC) biogeochemical model was used to assess potential GHG emissions under twelve different sets of General Circulation Models coupled with Regional Climate Models (GCM-RCM) climate projections (2046–2075). Simulations showed that water table control/sub-irrigation and sprinkler irrigation might decrease GWP by allowing greater soil organic carbon (SOC) sequestration, despite increased N 2 O and CO 2 emissions than non-irrigated systems. While drip irrigation marginally increased the SOC sequestration, GWP still increased because the C gains from the residues generated by the greater crop biomass were offset by increased CO 2 and N 2 O emissions. Compared with surface drip irrigation, subsurface drip irrigation reduced the GWP by reducing N 2 O emissions, as well as increasing crop yield and SOC stock. Although greater crop biomass was expected to return to soil under projected climate change, simulations showed a consistent increase in the GWP of tomato cropping system alone under climate change. • GHG emissions would increase under climate change projections. • Water table control/subirrigation could decrease the GWP by allowing greater SOC sequestration. • Sprinkler irrigation enhanced SOC and reduced the GWP due to improved crop biomass. • Subsurface drip irrigation increased the GWP due to rising N 2 O emissions and SOC loss.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".