National agroforestry program in Mexico faces trade-offs between reducing poverty, protecting biodiversity and targeting forest loss
Bibliographic record
Abstract
Abstract National reforestation initiatives with ambitious targets and multiple objectives are becoming the norm across the Global South. The extent to which these large-scale initiatives are actually achieving their multiple and potentially conflicting objectives, however, is largely unknown. Sembrando Vida, a national initiative in Mexico implemented in 2019, pays smallholder farmers to plant agroforests in order to reduce poverty and forest loss, and protect biodiversity. We assessed to what degree program recruitment met its stated objectives via its selection of participating municipalities and households. Because program data are not publicly available, we consolidated and harmonized >14 million policy payments (totaling ∼$4 billion USD) to smallholder farmers, thus creating the first spatiotemporal dataset of program outcomes. We found that ∼450k rural households in ∼1000 municipalities across the country participated in the program consistently from 2019 to 2022. The program was reasonably well targeted to achieve its poverty reduction objectives. Significantly more households (ANOVA, p < 0.001) were enrolled in high-poverty (10.4%) than low-poverty (4.9%) municipalities, despite more money being transferred in absolute terms to low-poverty municipalities. The program did not reach some regions that best fit its three goals. Using a zero-inflated negative binomial model, we showed that the distribution of participating households was more likely to address poverty (coefficient = 0.51, p < 0.001 at household level) and forest cover loss (0.1, p = 0.01) than to restore areas important for biodiversity (−0.08, p = 0.02). Finally, we conducted a spatial analysis showing that there is technically sufficient rural land (4.29 Mha) and households (491k) to maximize the potential of all policy objectives simultaneously, but this would require that the program operate in only 83 municipalities across 10 states. Our results highlight the challenges in reaching high poverty regions while meeting multiple other objectives when scaling up forest landscape restoration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".