Raw Materials and Processing Effects on Chinese Organic Fertilizer Prices
Bibliographic record
Abstract
This study investigates the impact of raw material sources and processing methods on the pricing of organic fertilizers in China’s market. Using sales data from Cnhnb.com, a leading agricultural input sales platform for ordinary farmers, and JD.com, a major retail platform for gardening enthusiasts, we employ text analysis methods and a probability-weighted hedonic pricing model to estimate price premiums and discounts, which can be interpreted as marginal value of characteristics or consumer preferences in a highly competitive multi-brand organic fertilizer market. Our results indicate significant price discounts for organic fertilizers derived from high-risk raw materials like manure and kitchen waste compared to products with unspecified sources, while fertilizers made from plant, animal (e.g., processed animal products, bone meal, etc.), and humic acid sources command price premiums. We also find that convenience-enhancing processing methods, such as pelletization and water-soluble concentration, and nutrient adjustments tailored for specific crops are effective in improving consumer preferences and increasing price premiums for organic fertilizers. These findings offer valuable insights for policymakers and industry stakeholders to expand market demand for organic fertilizers, promote their substitution for synthetic fertilizers, and foster the sustainable development of China’s organic fertilizer industry in the context of green agricultural transition.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".