Impacts on Productivity through Sustainable Fertilization of Nopal (Opuntia Ficus-Indica) Crops Using Organic Compost
Bibliographic record
Abstract
This paper shows the results obtained when evaluating current practices in cultivation processes of nopal. Production of nopal in the borough of Milpa Alta in Mexico City has been based for more than 40 years on the use of high doses of fresh cow manure (up to 600 t ha-1). It is necessary to consider the effects that this type of fertilization could have on the environment. In order to compare the effect of different fertilization methods on the production, quality and shelf-life of cladodes, three-year-old cactus plants were fertilized with compost, compost leachate, fresh manure cow and synthetic fertilizer; plants treated with water served as a control. The plants fertilized with compost (leached or solid) tended to a higher yield (g) per plant, although there were no significant statistical differences between treatments. Cladodes produced with solid compost or fresh manure showed a lower pH (4.7) than those produced with water to the soil. Cladodes produced with synthetic fertilizers showed higher shear strength than those produced with manure. Cladodes produced with synthetic fertilizer and compost leachate took more days to show shelf darkening (oxidation) than those produced with soil water. In addition, the use of compost showed a significant impact on cost reduction during the production nopal given a lower cost against manure and synthetic fertilizer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".