Do We Listen or Ignore Indigenous Practices? The Machobane Farming System - An Indigenous Farming Practice of Lesotho
Bibliographic record
Abstract
Aim: To evaluate the adaptive capacity of the Machobane Farming System, an indigenous practice to improve soil fertility and maize productivity compared to other non-Machobane farming practices. Study design: The study was conducted in four agroecological zones of Lesotho: Mountain, Foothills, low lands and Senqu river valley. Soil samples were collected at random from the non-Machobane farming practicing fields and Machobane farming practicing fields and the soil physicochemical and microbiological analyses were conducted to evaluate the soil quality. Structured and non-structured questionnaires were used to gather information from Focus Group Discussion (FGD) on the type of farming practices used and other demographic data. Results and Discussion: The MfS were found to be less affected and resilient to climate change with multiple benefits such as moisture conservation, slow release of nutrients and cross migration of microorganisms to the intercropping plants in the field unlike other farming practicing fields. An increased number of soil fertility indicator microorganisms such as Bacillus spp and Nitrogen fixing bacteria were seen to have increased the production of food crops (P>0.05) almost all the year round. An intensive relay cropping of one acre would be sufficient to ensure food security for an average family of 5 members. Conclusion: Currently, the Machobane Farming System (MfS) is adopted by many households in Lesotho using biochar and compost.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".