Controlled root targeted delivery of fertilizer using an ionically crosslinked carboxymethyl cellulose hydrogel matrix
Bibliographic record
Abstract
AIMS: The recent increases in food prices caused by the corresponding increases in fertilizer costs have highlighted the demand for reducing the overuse of fertilizers in industrial agriculture. There has been increasing interest in developing plant root-targeted delivery (RTD) of fertilizers in order to address the problem of inefficient fertilizer use. The aim of this study is to develop a low cost controlled release device to deliver fertilizers to plant roots and thereby increase fertilizer use efficiency. METHODS: The Root Targeted Delivery Vehicle (RTDV) is formed by dissolving Carboxymethyl Cellulose (CMC) chains in water, mixing it with liquid fertilizer and crosslinking using iron and calcium salts. Basic measurements quantifying nutrient release and green house growth trials were carried out to evaluate fertilizer use efficiency on wheat growing in nutrient depleted soil media. RESULTS: Growing wheat in nutrient depleted media showed that the RTDV permits a 78% reduction in the amount of fertilizer needed to achieve similar levels of plant yield in these conditions. Quantifying the losses associated with the RTDV synthesis showed that optimizing manufacturing could possibly increase this value as high as 94%. Furthermore, the delivery device showed a similar lifetime in soil to the plant's growth cycle, delivering fertilizer over the course of the plant's growth before removal from soil by degradation. CONCLUSIONS: These results illustrate the importance of fertilizer delivery in facilitating absorption and may have potential to vastly increase the use efficiency of fertilizers in soil, resulting in a significant reduction of costs and environmental damage. With more in depth study to quantify the fertilizer release and refine the device, there is great potential for the use of the RTDV as an effective means to increase fertilizer use efficiency in agriculture.
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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 | 0.000 |
| 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".