Microbial community composition and activity in paired irrigated and non-irrigated pastures in New Zealand
Bibliographic record
Abstract
Context Microorganisms are key for carbon (C) and nitrogen (N) cycling in soils supporting agricultural production. Aims We investigated the impacts of irrigation on microbial community structure and activity in New Zealand on 28 paired non-irrigated and irrigated grazed pasture sites where C and N had decreased under irrigation. Methods Microbial community structure and microbial biomass (phospholipid fatty acids) and activity (basal respiration, substrate-induced respiration (SIR), aerobically mineralisable N (AerMN)) were assessed. Key results Microbial biomass did not differ between irrigated and non-irrigated soils, but irrigated soils had increased gram-negative bacteria (P < 0.05), lower gram-positive:gram-negative ratio (P < 0.001) and lower fungal:bacterial ratio (P < 0.001) compared to non-irrigated soils. SIR and AerMN were greater in irrigated compared to non-irrigated soils. There were no differences in basal respiration between irrigation treatments. Greater prevalence of gram-negative bacteria (r-strategist) as well as decreases in actinomycetes and fungal to bacterial ratio, and increased SIR and AerMN suggest more rapid cycling of C and nutrients in irrigated systems where C had been lost. Conclusions We found clear evidence that irrigation alters microbial community structure and activity in New Zealand pasture systems. Implications Irrigation driven alteration of microbial populations may contribute to losses of soil SOM and soils’ ability to deliver ecosystem 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".