Intensification of Field Pea Production: Impact on Soil Microbiology
Bibliographic record
Abstract
The economic and environmental benefits of including grain legumes in crop rotations may tempt farmers to grow them more frequently than recommended, resulting in potential changes to soil chemical, physical, and biological properties. We investigated the effects of increasing the frequency of field pea (Pisum sativum L.) (P) in a wheat (Triticum aestivum L.) (W)‐based cropping system on soil microbial biomass C (MBC), β‐glucosidase enzyme activity, bacterial diversity, and populations of Rhizobium leguminosarum bv. viceae in the last 3 yr of a 13‐yr field study. The treatments consisted of three rotations: P‐P, W‐P, and W‐W‐P. Fertilizer N at 5, 20, and 40 kg N ha−1 was applied to pea in P‐P and W‐P rotations to examine the role of starter N. Soil MBC and diversity were lower in P‐P than pea rotated with wheat, presumably due to reduced amounts and diversity of C inputs under P‐P. These reductions in soil MBC and diversity probably further reduced field pea growth and grain yields through reduced nutrient cycling. In field pea, β‐glucosidase activity increased with increasing N, suggesting that N was limiting the capacity of soil microorganisms to recycle nutrients from organic materials (including crop residues). Populations of soil rhizobia were not affected by treatment. Wheat grown after pea in the 3‐yr W‐W‐P rotation had greater MBC and β‐glucosidase activity than that in the W‐P rotation, indicating the importance of long rotations. Therefore, pea monoculture reduced soil microbial quality, with adverse effects on nutrient cycling.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".