Assessing the impact of various irrigation technologies on agricultural production: A water-energy‑carbon nexus perspective
Bibliographic record
Abstract
Effective coordination of water, energy, and carbon is vital for the sustainable development of irrigated agriculture. However, limited research has been conducted on the impact of irrigation technology on the coupling and coordination relationship of these elements, especially on the North China Plain (NCP) where irrigation is applied extensively. This study establishes a water-energy‑carbon (WEC) nexus framework based on footprint theory and energy analysis. Water utilization, energy consumption, and carbon emissions in a wheat production system under conventional irrigation (CI), sprinkler irrigation (SI), and drip irrigation (DI) technology on the NCP from 2000 to 2019 were quantified. Subsequently, the coupling coordination degree (CCD) model is used to analyze the interactions and correlations of the WEC nexus. Results indicated that SI and CI effectively reduced water consumption and mitigated water degradation, but this came at the expense of increased energy consumption and carbon emissions. The irrigation process represented the predominant share of energy consumption, representing 40.54 % and 37.64 % of the production-based energy consumption under SI and DI, respectively. The primary contributors to the production-based carbon footprint under CI, SI, and DI were N fertilizer (23.67 %), pipeline production (59.10 %), and irrigation electricity (21.85 %), respectively. The CCD range of WEC systems under the three irrigation technologies varied from 0.35 to 0.50 on the NCP during the investigation period. There were some slight differences in the average annual CCD between each irrigation technology, with DI (0.43) > SI (0.40) > CI (0.39). SI and DI was in basic coordination, while CI was in imbalanced type. Meanwhile, the spatial heterogeneity of CCD was fully reflected over time. Promoting water-saving irrigation technologies, developing clean energy, controlling the expansion of irrigation areas, and strengthening the connections among various subsystems are crucial measure to achieve regional WEC nexus coupling coordination.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".