Assessment of the Management and Performance of Farming and Cropping Systems in Senegalese Niayes and Groundnut Basin
Bibliographic record
Abstract
Senegalese agriculture is characterized by low productivity and raises many societal concerns. These questions relate to meeting the food needs of its growing population and to the development and support of family farms and surroundings for exercising agricultural activity. To carry out effective agricultural and rural activities, it is important to know the specifics of agrarian regions by grasping the structural and functional dynamics of their agriculture. The objective of this study was to establish a framework for assessing its systems, based on overall performing. The methodology was based on multivariate and sustainability analyzes on a sample of 180 millet-based farmhouses in six collectives of Niayes and Groundnut basin. The results showed six clusters of farming types and five millet-cropping systems. By a significant association with the surroundings, biophysical and social settings of the ecosystem and technical-economic conditions of the farmhouses discriminated against 30.6% of practical decisions on millet cultivation routes. Agri-technical performances in terms of impacts, resilience, or self-regulation have shown that the progress made in terms of social well-being (workloads) and externalities on society (yields), of appropriate management agri-resources (regeneration of soil fertility), and their profitability (diversification and agricultural incomes) remains questionable.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".