Crop Residue Input and Decomposition in a Temperate Maize-Soybean Intercrop System
Bibliographic record
Abstract
Producers in the Argentine Pampa are implementing legume-based intercropping to maintain crop productivity. The objectives of this study were to quantify carbon (C) and N inputs from crop residues and their rate of decomposition in maize (Zea mays L.) and soybean (Glycine max L. Merr.) sole crops and two intercropping systems [1:2 intercrop (one row of maize and two rows of soybeans) and 2:3 intercrop (two rows of maize and three rows of soybeans)]. Carbon input from crop residues was significantly greater (P < 0.05) in the maize sole crop and 2:3 intercrop, but N input from crop residues was not significantly different between treatments. The amount of C and N remaining after 9 months of crop residue decomposition was significantly different between treatments, with the significantly lowest amount of residue C and N remaining in the soybean sole crop. The decay rate constant (k) and half-lives (t1/2) for crop residues C and N were significantly different between treatments. The highest k value was observed in the soybean sole crop and the lowest in the maize sole crop, whereas values for the intercrops were between those of the sole crops. Based on the values of crop residue input, the contribution of this organic material, the more difficult to decompose portion of soil organic matter was greatest in the maize sole crop followed by the 2:3 and 1:2 intercrops and the soybean sole crop. The amount of N mineralized was greatest in the soybean sole crop, followed by the maize sole crop, and the 2:3 and 1:2 intercrops. Results from this study contributed further information on the most optimal non-legume-legume configuration to maximize the long-term sequestration of C in the soil.
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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) | 0.000 | 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".