Impacts of the joint adoption of improved varieties and chemical fertilizers on rice productivity in Bolivia: implications for Global Food Systems
Bibliographic record
Abstract
Rice research and technology development in Latin America has increased yields and offered the opportunity for several countries to contribute to global food security by becoming net exporters of this cereal. In spite of the broad availability of rice technologies in the region, rice yields remain substantially low in countries like Bolivia. This study examines how Bolivian rice growers make simultaneous decisions about adopting improved varieties and chemical fertilizers and how this joint decision influences the productivity of this crop. By exploiting a nationally representative survey of rice producers, we use a multinomial logit model and an optimal instrumental variable approach to study both the correlates of technology adoption and the impacts of this adoption on rice yields. Our findings suggest that partial adoption of rice varieties or fertilizers does not affect yields, but the joint adoption of these technologies can almost double rice productivity. Promoting packages of agricultural technologies—instead of single technologies within efforts to make these technologies available for small farmers—would exploit the complementarities of different technologies and boost rice yields in Bolivia. The implications would not only be to achieve the desired self-sufficiency in rice production but also to follow similar pathways of other countries in the region that have become net exporters of rice and are contributing to Global Food Systems.
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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 | 0.002 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".