Integrated Soil Fertility Management in Bean-Based Cropping Systems of Eastern, Central and Southern Africa
Bibliographic record
Abstract
Integrated Soil Fertility Management in Bean-Based Cropping Systems of Eastern, Central and Southern Africa 241 to improve sustainably bean crop productivity and production in various production environments in ECSA.A range of technologies have been evaluated and developed to address the regional constraints, and efforts were made to promote the promising technologies widely.Several technologies have been developed through collaborative research efforts within PABRA.The objective has been developing strategies and technologies that enhance resilience to environmental stresses and improve bean productivity and product quality.These include: (i) development of diagnostic tools for soil fertility assessment that are adapted to local conditions; (ii) replenishing soil nutrient pools, maximizing on-farm recycling of nutrients, and reducing nutrient losses to the environment; and (iii) improving the efficiency of external inputs.As common bean can derive part of its N from the atmosphere under low input agriculture (Giller et al., 1998), improving biological nitrogen fixation using seeds inoculation with appropriate rhizobacteria and soil management was considered.Currently recommended ISFM options in bean based cropping systems include farmyard manure, compost, biomass transfer, green manure and cover crops, liming, phosphate rock (PR) and mineral fertilizers in different combinations with organic resources.The soil management options are complemented by utilization of resilient bean germplasm that perform well under low soil fertility conditions.This paper reviews ISFM options developed by the ECABREN and SBRN and the approaches for effective and efficient delivery of these technologies to farmers.
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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".