Adoption of Certified Seed and Its Effect on Technical Efficiency: Insights From Northern Kazakhstan
Bibliographic record
Abstract
Despite the economic and food security importance of the Kazakh wheat sector, current statistics suggest a yield gap between actual and potential yields. In view of this, farmers, stakeholders and the government are looking for agricultural technologies to increase the output. To this end, adoption of certified seeds is being promoted. The reasoning is that certified seed is produced from seed of known genetic origin and genetic purity, in a controlled and tested manner, processed and declared in accordance with the Law on Seeds and thus, could aid in producing maximum obtainable output. Unfortunately, little is known on whether this could affect wheat production and technical efficiency more than the conventional seed as such a subject has never benefitted from empirical analysis. To begin to fill this research gap, data from smallholder farms in Kazakhstan is used to evaluate the impact of adoption on technical efficiency by applying the stochastic production frontier. Results indicate that adoption of certified seed has productivity effects. Precisely, adopters are 20% more efficient than their counterparts. To a large extent, this is attributable to the quality of seeds used. Therefore, our study demonstrates the importance of certified seed adoption and accentuates the role governments can play in ensuring seed quality for enhanced technical efficiency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".