Crop response to water and fertilizers used in soil modified with hydrogels
Bibliographic record
Abstract
Increasing water and nitrogen use efficiency is essential to increase crop production and to reduce environmental degradation. Cellulosic hydrogels derived from paper waste, have the ability to retain and gradually release water and nitrogen for optimum plant growth. This study assessed crop response to water and NO3 − N with hydrogels amended in the soil. Tomato was used as the crop for this study as it is the most widely cultivated vegetable crop with a sensitive response to water and nitrogen. An experiment was conducted during 2020-2021 in the research greenhouse at Macdonald Campus of McGill University, comprising the following treatments: freeze-dried hydrogels (FDH), oven-dried hydrogels (ODH), control (without hydrogels) as well as two irrigation treatments (95% and 75% available water content (AWC)). Equivalent beads (32.10 of FDH and 35.96 g of ODH) corresponding to 4.6 g of 20-20-20 N-P-K fertilizer were applied before transplanting, at a depth of 0.15m from the soil surface. The treatments were replicated three times using a factorial design. The results indicated that FDH- 95% AWC treatment produced the highest average crop yield of 0.88 kg plant-1, compared to the ODH (0.32 kg plant-1) and control treatments (0.40 kg plant-1). The hydrogel and AWC combinations did not significantly (p > 0.05) impact plant height and stem diameter, while these treatment combinations enhanced and significantly affected crop yield, leaf area index and plant biomass (p < 0.05). FDH and ODH produced a substantially higher yield and saved 15 % and 20% of irrigation water (225mm) as compared to the control treatment. Furthermore, there was a noticeably higher water use efficiency in the FDH-95 (3.911 kg m-1 plant-1) treatment as compared to the ODH-95 (1.467 kg m-1 plant-1) and control (1.509 kg m-1 plan-1) treatments. With soil only, and no crop, FDH was most effective in releasing fertilizer to the plants. The FDH gradually increased NO3 − N concentration from 20 to 65 mg kg-1 over a month. The results indicate that under FDH and ODH treatments, excess nitrate was stored in the soil vacuoles, and was remobilized for uptake by the plant roots. The overall performance of both hydrogels was comparatively better than the control, with the FDH-95% AWC giving the highest marketable yield, and best water and nitrogen saving potential. This study showed that cellulose -paper-based hydrogels, which is a waste product from the pulp and paper industry, can be used to improve crop production
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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".