Improving Soil Fertility and Crops Yield through Maize-Legumes (Common bean and Dolichos lablab) Intercropping Systems
Bibliographic record
Abstract
Declining crops yield in the smallholder farmers cropping systems of sub-Saharan African (SSA) present the need to develop more sustainable production systems. Depletion of essential plant nutrients from the soils have been cited as the main contributing factors due to continues cultivation of cereal crops without application of organic/ inorganic fertilizers. Of all the plant nutrients, reports showed that nitrogen is among the most limiting plant nutrient as it plays crucial roles in the plant growth and physiological processes. The most efficient way of adding nitrogen to the soils is through inorganic amendments. However, this is an expensive method and creates bottleneck to smallholder farmers in most countries of sub-Saharan Africa. Legumes are potential sources of plant nutrients that complement/supplement inorganic fertilizers for cereal crops because of their ability to fix biological nitrogen (N) when included to the cropping systems. By fixing atmospheric N2, legumes offer the most effective way of increasing the productivity of poor soils either in monoculture, intercropping, crop rotations, or mixed cropping systems. This review paper discuses the role of cereal legume intercropping systems on soil fertility improvement, its impact on weeds, pests, diseases and water use efficiency, the biological nitrogen fixation, the amounts of N transferred to associated cereal crops, nutrients uptake and partition, legume biomass decomposition and mineralization, grain yields, land equivalent ratio and economic benefits.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".