Analyzing Economic Sustainability of Market Gardening and Rice Farms in Southwest Benin under FAFA Project
Bibliographic record
Abstract
The issue of agricultural sustainability remaining a topical concern. While agriculture being an important sector for the development of world economies. It has noted that implementing farming sustainably for a better contribution in the next generation economic development needed. The main purpose of this paper is to analyze the economic sustainability of the rice farms and market gardening benefiting from the FAFA MC project in the Southwest juxtaposed departments in Benin. Specifically, it is first of all a question of assessing, with regard to the two types of farms, which type of farming is the most sustainable. Next, to see whether rice farms are more sustainable than market gardening farms; and finally, to highlight the relationship between economic sustainability and environmental and social sustainability. The data used where obtained from FAFA MC database (2012). Finally, a data of 48 farms were used in the analysis. The analysis techniques have consisted of three steps these are: descriptive analysis, the principal component analysis applied and an ascending hierarchical classification applied. The analysis of data supported by SPSS 2.0, Excel 2013 and SPAD 5.5 software package. To understand the economic sustainability of the farms, the sustainability indicators methodology of IDEA was employed. Overall, it emerges from the analysis of results that the farms considered have limited economic sustainability (a score of 38.25 / 100); group farms prove to be more economically sustainable than individual farms and vegetable farms are more economically sustainable compared to rice farms. So, it seems important to encourage groupings of individual farmers, to clearly define property rights on agricultural land, to sensitize producers to the application of the principles of sustainable development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".