Improving women's purchasing power through land-enhancing technologies: The case of bio-reclamation of degraded lands in Niger
Bibliographic record
Abstract
In Niger, about 50% of the land surface is composed of degraded lateritic soils, and rural women farmers have limited access to productive land. Targeting largely marginalized rural women with bio-reclamation of degraded land (BDL) technologies restores their rights to earn a livelihood through agriculture. This study examines the determinants and impacts of land-enhancing technology on women farmers in Niger. Data were collected from 1,205 randomly selected women farmers in the Maradi and Zinder regions. The sample included 69% of participants into BDL program and 31% of non-participants. To account for selection bias from observable and unobservable factors, an endogenous switching regression (ESR) model was used to estimate the impact of BDL technology on women's household income. A simple probit model was used to analyze the determinants of participation. The results show that key determinants of participation in BDL include income level before participation in BDL, household size, age of participants, number of women in the household, number of children under 5 years old, sex of household head, age of household head, and institutional support. Participation in BDL positively influences participants' income (+14%); non-participants may not benefit from participating as they would probably lose 31% of their income, and the impact of participation in BDL varies widely across regions. Before the advent of BDL, the income of non-participants was higher than that of participants by 25%. It can be inferred that BDL is a pro-poor technology that is not beneficial to all women farmers. This study makes a critical contribution to the literature on land-enhancing technologies. It suggests that the impact of land-enhancing technologies, such as BDL, is closely linked to spatial, economic, environmental, temporal, and cultural contexts. Accordingly, land-enhancing technologies should target locations with large percentages of degraded farmlands and the poorest farmers. These results contribute to food security and poverty alleviation policies in rural dryland areas.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".