Micronutrients in the Soil and Wheat: Impact of 84 Years of Organic or Synthetic Fertilization and Crop Residue Management
Bibliographic record
Abstract
Crop residues are an important source of plant nutrients. However, information on the various methods of residue management on micronutrients in soil and wheat (Triticum aestivum L.) over time is limited. A long-term (84-year) agroecosystem experiment was assessed to determine the impact of fertilizer type and methods of crop residue management on micronutrients over time under dryland winter wheat-fallow rotation. The treatments were: no N application with residue burning in fall (FB), spring (SB), and no residue burn (NB); 45 kg N ha−1 with SB and NB; 90 kg N ha−1 with SB and NB; pea vines; and farmyard manure (FYM) and a nearby undisturbed grass pasture (GP). Wheat grain, straw, and soil samples from 1995, 2005, and 2015 were used to determine tissue total and soil Mehlich III extractable Mn, Cu, B, Fe, and Zn, and soil pH. After 84 years, extractable Mn and B in the top 10 cm of soil decreased in all plots, except for B in FYM and SB. The FYM plots had the highest extractable Mn (114 mg kg−1) in the top 10 cm soil; however, it declined by 33% compared to the GP (171 mg kg−1). Extractable Zn in the top 10 cm of soil increased with FYM while it decreased with inorganic N application in 2015; however, total Zn in grain increased by 7% with inorganic N (90 kg ha−1) application compared to FYM application. The results suggest that residue management had similar impact on soil micronutrients. Inorganic N and FYM application can be integrated to reduce micronutrient losses from cultivation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".