Understanding the Trade-Offs Between Environmental Service Provision through Improved Fallows and Private Welfare Using Stated Preference Approach: A Case Study in Chongwe - Zambia
Bibliographic record
Abstract
<p>The trade-offs between environmental service (ES) provision through the uptake of improved fallows and private farmer welfare losses have rarely been evaluated. Unlike inorganic fertiliser, improved fallows provide ES in addition to improving the soil fertility. This study used contingent valuation methodologies to evaluate willingness to provide ES through improved fallows among 324 farmers in Chongwe district of Zambia. Given scenarios that improved fallows, unlike inorganic fertiliser, help in mitigating soil erosion and water pollution, more than 70% of the farmers were willing to supply these services through the technology. The willingness to be pro-fertiliser oriented was positively associated with cropped land sizes and soil fertility challenges and negatively associated with total farm size. In addition, for users of improved fallows, increases in per capita income increased the probability of willingness to embrace fertiliser. Group membership decreased the probability for the users’ willingness to embrace fertiliser. For the non-users, the probability of joining the association that would ensure blockage of an improved fallow policy decreased with maize productivity. For the few farmers, there was no significant difference in the willingness to pay (WTP) (<em>t = 1.546, p = 0.136</em>) to ensure availability of fertiliser or blocking a policy compelling uptake of improved fallows between the users (WTP = K1, 050,000, US$1 = K5, 000) and non-users (WTP= K1, 380,000) of the technology. The trade-off between ES provisions through improved fallows and loss in immediate private welfare by not embracing fertiliser was similar across the technology’ users divide. Therefore a payment for environmental services policy could target the farmers as a homogenous group.<strong></strong></p>
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".