Genetic diversity of Bradyrhizobia nodulating promiscuous soybean varieties in soils amended with phosphorus and lime in two contrasting sites in Kenya
Bibliographic record
Abstract
African agriculture is highly diverse, with major farming systems matched to the major agroecologies.In each country or region there are localised agroecological gradients, and large differences between regions in terms of access to markets.Within each village a wide diversity of farming livelihoods can be found -differing in production objectives and resource endowments.Differences in soil fertility are partly derived from inherent differences in properties (the 'soilscape') but are strongly influenced by past management, particularly by the rates and quality of organic manures added to the soils.It is clear that 'one-size-fits-all' or silver bullet solutions that are generally applicable for enhancement of soil fertility simply do not exist.Further, although research has focused on 'best bet' technologies for different regions, a better conceptualization is 'best fit' technologies for specific situations.Although the heterogeneity in African farming is at first sight bewildering, systematic analysis across farming systems in West, East and southern Africa reveals repeating patterns of management.These repeating patterns of allocation of nutrient resources and management methods lead to self-organization among smallholder farms.The past management of fields leads to extreme differences in fertilizer use efficiency, e.g. from 5 kg grain kg N -1 to 50 kg grain kg N -1 between fields of the same farm.By categorizing field types within agroecological zones in simple terms, easily recognizable by farmers, 'rules-of-thumb' can be derived for highly-efficient management of scarce nutrient resources in these heterogeneous environments.Success of legume-based technologies for soil fertility improvement, such as grain legume/cereal rotations or legumes for animal fodder also varies enormously depending on the soil fertility status of fields.New approaches for enhancing productivity in Africa must take account of, and harness, the dynamic nature of farming systems and the heterogeneity between regions, farmers and their fields.Our proposed approach represents a substantial shift in concept from traditional 'blanket recommendations' to focus on the targeting of bestfit technologies to different farmers and crops within production systems using simple 'rules-of-thumb' derived from scientific principles and local farmers' knowledge.
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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 | 0.001 | 0.001 |
| 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".