Current Agricultural and Environmental Policies in Benin Republic
Bibliographic record
Abstract
The need to feed the population growth conducts to the development of material intensive production systems in many countries. However the absence of adequate policies has adverse consequences on the environment and the performance of the agricultural and rural sectors. Benin Republic, through its Strategic Plan for Agricultural Sector Development (PSDSA) focuses on improving food and nutrition security, improving farm level income and building resilience to climate change within the Government Action Plan (PAG Bénin Révélé) 2016-2021. The aim of this study is to analyze the current agricultural policies and his link with the current environmental policies in Benin Republic. The data from Ministries and Structures in charge of Agriculture, Environment, Health and Human being were used. Reports from some international organizations such as FAO, PNUD and FIDA were also used. It is observed and planed an increase in cultivation area, all crops yields and crops production from 2016 to 2021. That will involve the more use of agricultural machinery, fertilizers and pesticides. Added to the current environment challenges, it appears the risk of soil degradation, deforestation, water and air pollution, then global impact on the environment when this plan will be implemented. It is right that some Environment Impact Assessment (EIA) are purposed for many of the actions. However, these EIA are sometime neglected and sacrificed for the profitability of agricultural production. To achieve this agricultural goal without affect the environment, the respect of the adequate law and EIA for each single activity becomes necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".