Technical Efficiency of Farms, and Fight Against Poverty: Case of the Cashew Sector in Côte d’Ivoire
Bibliographic record
Abstract
Cashew was introduced in the north of Côte d’Ivoire to support the economy in the region. This study was conducted to evaluate the technical efficiency of cashew farms in Côte d’Ivoire. The technical efficiency of producers was measured using the Data Envelopment Analysis approach, and the determinants of this efficiency were identified using a TOBIT model. Data were collected in 4 regions: GBEKE, HAMBOL, PORO and WORODOUGOU. In the four regions studied, the average technical efficiency is 49.2% in Variable Scale Efficiency (VRS) and 38.3% in Constant Return to Scale (CRS). Based on our results, the producers in the study area were not efficient. The producers who follow the good practices, have a technical coefficient estimated at 74.2%, and superior to those who follow the good practices, of which, the coefficient is estimated at 70.2%, in Variable Scale Efficiency (VRS). The technical efficiency of farms was positively influenced by the age of farms and agricultural advisory services, and negatively influenced by the pruning practice. Income from cashew farming in the study area (21,816 to 37,987 CFAF/person/year according to region) is below extreme poverty line (CFA F 122,385/year/person), leading to deteriorating cashew/food terms of trade. Cashew farming is often used as a means of land appropriation and of getting credit. Its rapid expansion has dramatically reduced land for subsistence agriculture, raising an accute food security issue. Cashew farming has helped improve poverty indicators through macroeconomic policy. However, this impetus from the agricultural sector economy remains insufficient to boost the modernization of the agricultural sector. The country still has all assets (research institutes, schools of agronomy, skills etc.) to reverse this situation. Hence the he study recommends that producers capitalize on exogenous variables which can improve agricultural efficiency. It also recommends coaching organizations to use technical efficiency measurement and identification of effectiveness determinants to better guide their coaching. As for the Government, it should redouble efforts to implement the recommended solutions in order to avoid producer impoverishment, a barrier to harmonious development in this region of Côte d’Ivoire.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".