Do Farmers’ Asset Values Correlate with Land Quality?
Bibliographic record
Abstract
This paper investigates if farmers’ asset values have a predictive power to asses land quality. A rich sustainable livelihood literature describes small farmers’ biophysical and socio-economic environment through asset values, which closely adheres to the required information for an integrated quality appraisal of the natural resource base. For our analysis we use an in-depth survey held among 50 famers’ households in three rural areas of Senegal. Farmers gave scores for their livelihood assets (human, physical, natural, financial and social) and judgments on the state and trend of the quality of their natural resource base (crop land, rangeland, forest and water resources). As our observational data are dominated by unobserved heterogeneity, we refrain from causal statistical analysis and seek associative patterns between asset values and state and trend of natural resource quality using data visualization techniques and descriptive statistics. We compare categorical data on state and trend of land qualities with asset value classes in a frequency distributions evaluation (Chi-square) and with continuous asset value scores in an analysis of variance (ANOVA). For state of forest we found consistent but counterintuitive differences for various asset values with higher asset values for ‘degraded’ classes and lower values for ‘good’ quality of the forests. There is some evidence that trend of forest quality can be derived from asset value scores which were in agreement with our premise of lower scores for low quality and higher scores for better quality. Yet, overall we have to conclude that asset values do not correlate straightforward and unequivocally with state and trend of natural resource quality.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".