Short‐ and Long‐Term Labile Soil Carbon and Nitrogen Dynamics Reflect Management and Predict Corn Agronomic Performance
Bibliographic record
Abstract
Labile soil organic matter plays an extremely important role in crop nutrient acquisition, but quantifying this pool can be prohibitively expensive to farmers. A better understanding of rapid and inexpensive measures of labile organic matter could lead to new tools for predicting soil N supply and crop performance. Toward this end, we quantified several simple measures of labile C and N over the course of the corn ( Zea mays L.) growing season in a long‐term systems trial to determine:(i) the temporal dynamics of these measures, (ii) the long‐term response of these measures to management, and (iii) the capacity of these measures to predict corn agronomic performance. We found that all labile soil measures (permanganate oxidizable carbon [POXC], C mineralization, N mineralization, and soil inorganic N) varied temporally and responded to long‐term differences in management. Corn grain and vegetative biomass also responded to long‐term treatment differences and these differences were strongly related to the measured labile soil C and N fractions. The history of crop rotation had a greater influence than management regime on all soil measures, with the exception of POXC. Of all the measures, C mineralization was the best predictor of agronomic performance both individually ( r = 0.61–0.78, depending on corn stage), and when modeled with multiple indicators (six out of nine models). The results presented here demonstrate the strong relationship between crop growth and labile organic matter dynamics, and provide further evidence that C mineralization is an inexpensive, but sensitive predictor of corn agronomic performance.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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 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".