Organic Amendment History and Crop Rotation Effects on Soil Nitrogen Mineralization Potential and Soil Nitrogen Supply in a Potato Cropping System
Bibliographic record
Abstract
Crop management practices influence readily and potentially available N in soil. In this study, we evaluated the effects of organic amendment history and crop rotation on potentially mineralizable N (N0), mineralizable N pools, and field estimates of soil N supply in potato (Solanum tuberosum L.) production, and evaluated a suite of N availability measures to detect changes in these parameters. Preplant soil samples (top 15‐cm) were collected from the potato year of a rotation trial in Maine during 2004 and 2005. Treatments included three crop rotations, with and without a history of organic amendment [solid beef (Bos taurus) manure] application: PB, potato‐barley (Hordeum vulgare L.); PSPB, potato‐soybean [Glycine max (L.) Merr.]‐potato‐barley; and PSBA/T, potato‐soybean‐barley‐alfalfa (Medicago sativa L.)/timothy (Phleum pratense L.). The N0 and mineralizable N pools were determined by aerobic incubation at 25°C and periodic leaching for 24 wk with a fixed‐k approach. On average, historically amended soil had 35% higher values of N0, and an 8% higher proportion of mineralizable N partitioned to the stable mineralizable N pool, compared with nonamended soil. Lower values of N0, mineralizable N pools and some measures of N availability were measured in PSBA/T compared with PB and PSPB rotations. All tested measures of N availability detected management‐induced changes in N0 and mineralizable N pools. The preplant nitrate, UV absorbance of 0.01 M NaHCO3 extract at 205 nm and particulate organic matter (POM)‐N were the best predictors of field‐based indices of soil N supply (r2 = 0.50 to 0.73). Management‐induced changes in the size and quality of mineralizable N should be considered in developing best N management programs through organic amendment application and crop rotations.
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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".