Nematode infestation and N-effect of legumes on soil and crop yelds in legume-sorghum rotations
Bibliographic record
Abstract
The effects of cowpea (Vignaunguiculata) and-groundnut (Arachis hypogea) on succeeding sorghum yields, soil mineral N and nematode infestationwere studied during five cropping seasons (2000 to 2004) in a weakly acid Ultisol of the agronomy research station of Farakô-Ba lo-cated in the Guinean zone of Burkina Faso, West Africa. A factorial 5x5 design of five crop rotations with five fertilizer treatments in a split-plot arrangement with four replications was used.Sorghum yields were affected by the two factors (rotation with legumes and fertilizer ap-plications) during the four years. But interactions were not observed between the two factors. Monocropping of sorghum produced the lowest yields and legume–sorghum rotations increased sorghum yields by50% to 300%. Ground-nut–sorghum and cowpea–sorghum rotations increased soil mineral N by36% and 52%, re-spectively. Crop rotation influenced nematode infestation but the effects on soil and sorghum root infestation differed according to the rotation. The cowpea–sorghum rotation increased soil and sorghum root infestationby nematodes while groundnut–sorghum decree-sed the nematode population. The soil of the cowpea-sorghum rotation contained 1.5 to 2 times more nematodes than the soil of the monocropping of sorghum. In contrast, the soil ofthe groundnut–sorghum rotation contained from 17 to 19 times fewernematodes than that of themonocropping of sorghum. However, nematode infestation did not affect any of the succeeding sorghum yields. It was concluded that the parasitic effect of nematodes was limited by the predominance of positive N-effects on the development of succeeding sorghum.
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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".