Effects of fertilization and other agronomic measures on nutritional quality of crops
Bibliographic record
Abstract
Abstract Crops, as the basic source of essential substances and nutrients, do not always contain sufficient amounts of these essential nutrients to meet dietary requirements. In this review paper, we discussed the effects of fertilization and other agronomic measures on the nutritional quality of cereal, oilseed and protein crops, tuber plants and vegetables. Research indicates that application of N, P, K and S fertilizers generally increases crop yield as well as nutritional quality. For example, fertilizer increased protein concentration in cereals and pulses, oil concentration in oilseed crops, starch concentration in tubers, and concentration of essential amino acids and vitamins in vegetables. However, excessive fertilizer application, especially N fertilizer, can result in undesirable changes such as increases in nitrate, titratable acidity and acid to sugar ratio, while decreasing the concentration of vitamin C, soluble sugar, soluble solids, and Mg and Ca in some crops. Other agronomic measures, such as tillage and crop rotation, organic farming, soil moisture management, and crop breeding and genetic engineering can also have a large effect on food crop quality, though the potential benefits of these measures for improving crop quality has not been fully exploited. Research literature on this subject suggests that more information is needed in order to achieve an increase in the concentration of essential microelements, prevent accumulation of toxic levels of elements such as Cu, Mo, Zn, Ni, Se and nitrate, and other dangerous or toxic substances and elements in crops. Copyright © 2007 Society of Chemical Industry
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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 | 0.000 | 0.001 |
| 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".