Soil nutrient management in Haiti, pre-Columbus to the present day: lessons for future agricultural interventions
Bibliographic record
Abstract
One major factor that has been reported to contribute to chronic poverty and malnutrition in rural Haiti is soil infertility. There has been no systematic review of past and present soil interventions in Haiti that could provide lessons for future aid efforts. We review the intrinsic factors that contribute to soil infertility in modern Haiti, along with indigenous pre-Columbian soil interventions and modern soil interventions, including farmer-derived interventions and interventions by the Haitian government and Haitian non-governmental organizations (NGOs), bilateral and multilateral agencies, foreign NGOs, and the foreign private sector. We review how agricultural soil degradation in modern Haiti is exacerbated by topology, soil type, and rainfall distribution, along with non-sustainable farming practices and poverty. Unfortunately, an ancient strategy used by the indigenous Taino people to prevent soil erosion on hillsides, namely, the practice of building conuco mounds, appears to have been forgotten. Nevertheless, modern Haitian farmers and grassroots NGOs have developed methods to reduce soil degradation. However, it appears that most foreign NGOs are not focused on agriculture, let alone soil fertility issues, despite agriculture being the major source of livelihood in rural Haiti. In terms of the types of soil interventions, major emphasis has been placed on reforestation (including fruit trees for export markets), livestock improvement, and hillside erosion control. For many of these interventions, there is limited independent, peer-reviewed data as to their success or long-term effect. By comparing soil interventions in Haiti with interventions that have been effective globally, we have identified several intervention gaps. The most important soil intervention gaps in Haiti include inadequate farmer training (extension) in soil management, and lack of technical support for legume and cover crops and for livestock pastures. We discuss the policy failures of different stakeholders working in Haiti, potential remedies, their costs, and likely long-term effects. We hope that this review will inform future efforts to improve soil fertility in Haiti.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".