Impact of Farm and Producer Characteristics on the Adoption of Best Management Practices among Popcorn Producers in the United States
Bibliographic record
Abstract
Certain agricultural practices can negatively impact the environment, and thus experts developed a set of Best Management Practices (BMPs) to address these environmental risks and challenges from agriculture. The adoption of these BMPs have been recognized to safeguard the environment while also yielding economic advantages for agricultural producers. This study employed primary data obtained from popcorn producers in the United States to investigate the level of adoption of these BMPs among this group of producers. Additionally, various farm and producer characteristics were assessed to identify the factors that impact producers’ adoption levels of these practices. The BMP adoption index was developed to measure the level of BMP adoption among respondents. Multinomial logistic and logit regression models were used to estimate the extent of how certain factors impact popcorn producers’ BMP adoption levels. The results indicates that majority of producers follow BMPs at a moderate level, while nearly a quarter do so at low levels despite the numerous advantages. Also, this study reveals producers’ income and experience levels have a significant impact on the adoption of BMPs in popcorn production. These results may be important to stakeholders in developing strategies and interventions to promote the widespread adoption of BMPs among popcorn producers. Thus, this study will inform policymakers, agricultural extension services, and other industry stakeholders to effectively promote the adoption of BMPs in the popcorn industry, benefiting individual producers and improving environmental sustainability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".