Long-Term Organic Inputs Determine Soil Productivity Better in Sorghum-Cowpea Rotation Than in Sorghum Monoculture
Bibliographic record
Abstract
Information on long-term fertilization combined with crop rotation can contribute to better management of West African Lixisols. There is little information on how long-term organic inputs influence soil chemical properties under cereal monoculture versus a rotation with a legume. Here, we investigated how fertilization regimes with emphasis on organic inputs influence soil chemical properties in sorghum monoculture compared to sorghum-cowpea rotation. The long-term field trial of Saria in Burkina Faso, which has been in operation since 1960, was used for this purpose. Soils were sampled at the 0-20 cm depth to determine their organic C, total N, total P, mineral N, available P, pHwater, exchangeable basic cations, and cation exchange capacity. The best soil properties were exhibited with the application of 40 t ha-1 of manure. Recycling of sorghum residues combined with mineral fertilization led to a decrease in mineral N and available P but maintained a higher level of total N and P compared to exclusive mineral fertilization. Organic inputs determined soil properties and sorghum yield better (R2 = 0.89) in rotation than in monoculture. Our results show that a better productivity of the studied Lixisol requires an application of manure at more than 5 t ha-1 combined with mineral fertilization. In addition, a rotation including a legume and a regular recycling of crop residues is necessary.
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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".