Fertilizer Use for Horticultural Crops in the U.S. during the 20th Century
Bibliographic record
Abstract
Tremendous changes have occurred during the past century in the sources and methods for supplying nutrients for horticultural crops. Reliance on animal manure, cover crops, and animal tankage was insufficient to meet the crop nutrient demand for a rapidly expanding population. The Haber-Bosch process for ammonia synthesis (1910s) revolutionized the availability and affordability of nitrogen (N) fertilizer. Discovery of large-scale deposits of rock phosphate in South Carolina (1860s) and Florida (1880s) alleviated widespread nutrient deficiencies. Acidification of rock phosphate and bone material significantly improved phosphorus (P) availability for plants. Discovery of potassium (K)-bearing minerals in New Mexico (1920s) and later in Canada (1960s) now provide a long-term nutrient source. Modern fertilizer technology allows nutrients to be applied in the correct ratio and amount to meet crop needs. Advances in understanding plant nutrition, coupled with slow-release fertilizers, foliar fertilization, soluble nutrients, and the development of soil and tissue testing have all improved the yield and quality of horticultural crops. Future developments will likely focus on fertilization in an increasingly competitive global economy, while requiring sophisticated management to minimize environmental impacts.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 0.001 |
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".