Adoption of renewable soil fertility replenishment technologies in the southern African region: Lessons learnt and the way forward
Bibliographic record
Abstract
Abstract Low soil fertility is one of the most important biophysical constraints to increasing agricultural productivity in sub‐Saharan Africa. Several renewable soil fertility replenishment (RSFR) technologies that are based on nutrient re‐cycling principles have been developed in southern Africa. Some success stories have been recorded (e.g. nitrogen‐fixing legumes), but the adoption of RSFR technologies has generally lagged behind scientific advances thereby reducing the potential impacts of the technologies. This paper describes the major RSFR technologies being promoted in the region, synthesizes available information regarding their adoption by farmers, and identifies the challenges, key lessons learnt and the way forward for up‐scaling RSFR technologies in the region. The review indicated that farmer uptake of RSFR technologies depends on several factors that can be grouped into broad categories: technology‐specific (e.g. soil type, management regime), household‐specific (e.g. farmer perceptions, resource endowment, household size), policy and institutions context within which RSFR is disseminated (inputs and output prices, land tenure and property rights), and geo‐spatial (performance of species across different bio‐physical conditions, location of village). Adoption of RSFR technologies can be enhanced by targeting them to their biophysical and social niches, facilitating appropriate policy and institutional contexts for dissemination, understanding the broader context and dynamics of the adoption process, a paradigm shift in the approach to the dissemination of RSFR (e.g. expanding RSFR to high value crop systems, exploring synergy with inorganic fertilizer) and, targeted incentive systems that encourage farmers to take cognizance of natural resource implications when making agricultural production decisions .
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".