Soil organic carbon in irrigated agricultural systems: A meta‐analysis
Bibliographic record
Abstract
Over the last 200 years, conversion of non-cultivated land for agriculture has substantially reduced global soil organic carbon (SOC) stocks in upper soil layers. Nevertheless, practices such as no- or reduced tillage, application of organic soil amendments, and maintenance of continuous cover can increase SOC in agricultural fields. While these management practices have been well studied, the effects on SOC of cropping systems that incorporate irrigation are poorly understood. Given the large, and expanding, agricultural landbase under irrigation across the globe, this is a critical knowledge gap for climate change mitigation. We undertook a systematic literature review and subsequent meta-analysis of data from studies that examined changes in SOC on irrigated agricultural sites through time. We investigated changes in SOC by climate (aridity), soil texture, and irrigation method with the following objectives: (i) to examine the impact of irrigated agriculture on SOC storage; and (ii) to identify the conditions under which irrigated agriculture is most likely to enhance SOC. Overall, irrigated agriculture increased SOC stocks by 5.9%, with little effect of study length (2-47 years). However, changes in SOC varied by climate and soil depth, with the greatest increase in SOC observed on irrigated semi-arid sites at the 0-10 cm depth (14.8%). Additionally, SOC increased in irrigated fine- and medium-textured soils but not coarse-textured soils. Furthermore, while there was no overall change to SOC in flood/furrow irrigated sites, SOC tended to increase in sprinkler irrigated sites, and decrease in drip irrigated sites, especially at depths below 10 cm. This work sheds light on the nuances of SOC change across irrigated agricultural systems, highlights the importance of studying SOC storage in deeper soils, and will help guide future research on the impacts of irrigated agriculture on SOC.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.041 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".