Potentiel de Productivité et Efficacité Technique du Secteur Agricole en Afrique
Bibliographic record
Abstract
This study utilizes frontier metaproduction functions to analyze inter‐region agricultural productivity differences. Technical efficiency scores are examined through estimation of stochastic frontiers for 16 African countries divided into three different regions (West Africa, East and Southern Africa, and North Africa) from 1970 to 2001. The idea is to explore the differences in efficiency and technological gaps of agricultural sector. Apart of common traits that characterize African agricultural sector, countries exhibit national and regional specificities. These diversities are such that it is difficult to make valuable generalizations. It appears from the results that: in West Africa, the level of technology is relatively good, meaning that there is no problem of input constraints. By contrast, the efficiency with which inputs are used is very low. The situation is very different in the East and Southern Africa, with the level of technology relatively low and appreciable technical level. At least, the North Africa countries make a performing mixture between technology and efficiency. Cette étude utilise les Meta frontières de production pour analyser les différences inter‐régionales de la productivité agricole. Les niveaux d'efficacité technique sont examinées par l'estimation des frontières stochastiques de 16 pays africains regroupés en trois régions (l'Afrique de l'Ouest, l'Afrique de l'Est et Australe, et l'Afrique du Nord), sur une période allant de 1970 à 2001. L'idée étant d'explorer les différences d'efficacité et les écarts technologiques du secteur agricole. Au‐delà des simples traits communs qui caractérisent le secteur agricole africain, on trouve des expériences nationales et régionales dont il est difficile, du fait de leur grande diversité, de tirer des généralisations valables. Des résultats de l'étude, il ressort que: en Afrique de l'Ouest, le niveau technologique est relativement satisfaisant, traduisant le fait que la présence des inputs ne représente pas une contrainte. Par contre le niveau d'efficacité avec lequel ces intrants sont utilisés est assez faible. La situation est tout autre en Afrique de l'Est et Australe avec un niveau technologique relativement faible et un niveau d'efficacité appréciable. L'Afrique du nord enfin fait un savant dosage entre efficacité et technologie.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".