Factors Affecting Adoption of Improved Crops by Rural Farmers in Niger
Bibliographic record
Abstract
Improved crops are advocated to meet the dual challenge of food security and the fight against poverty in developing countries. As most poor people in developing countries live in rural areas and depend on agriculture for their livelihood, an important key to get them out of poverty is to increase agricultural productivity by using technologies such as improved crops. However, the rate of improved crops adoption remains surprisingly low in Niger, one of the world poorest countries. In this paper, we examine the factors affecting adoption of improved crops by rural farmers focusing on Niger. Using the 2014’s National Survey on Households Living Conditions and Agriculture, we investigate the effect of farmers’ socioeconomic characteristics, the farm’s quality, the geographic location, the production system, the access to improved seeds and the land tenure on the probability to use improved crops rather than local crops. Our results suggest that the ownership of a government land title is the most important driver in the adoption of improved crops by rural farmers. In addition, being a female, educated, practicing polyculture, having access to improved seed increase the probability to adopt improved crops. In contrast, household size, operating on the parcel for a long period and the parcel size reduces the probability to use improved crops. These determinants of improved crops adoption should be considered in Niger’s agricultural policy to succeed in the dissemination of improved crops among rural farmers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".