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Record W7162003988 · doi:10.82308/30350

Crop response to water and fertilizers used in soil modified with hydrogels

2022· dissertation· en· W7162003988 on OpenAlex

Why this work is in the frame

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aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicPolymer-Based Agricultural Enhancements
Canadian institutionsnot available
Fundersnot available
KeywordsSelf-healing hydrogelsIrrigationCropGreenhouseCrop yieldFertilizerSoil waterBiomass (ecology)

Abstract

fetched live from OpenAlex

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

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.221
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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