Effects of Processing Methods on Phytochemical Compositions of Selected Plant Materials with Animal Nutrition Potentials
Bibliographic record
Abstract
The expansion of the salinization of agricultural areas limits the production of crops of high economic importance, such as soybeans. To attenuate the effects of salts on the plants and improve photosynthetic performance under salinity conditions, the application of a biostimulant based on seaweed extract (Ascophyllum nodosum (L.)) and fulvic acids as a physiological enhancer was adopted. In the present study, we evaluated whether applications of the biostimulant at different phenological stages can reduce damage to the photosynthetic apparatus in soybean plants under salt stress. The experiment was conducted in a greenhouse, and the design adopted was completely randomized in a double factorial scheme consisting of three applications (V3, V3/R1, and V3/R1/R4) and two levels of salinity of the irrigation water (S0—absence of salt and S1—saline solution at 5.0 dS m−1 prepared with the salts NaCl, CaCl2·2H2O, and MgCl2·6H2O in the ratio 7:2:1). There were two additional controls without the application of the biostimulant (with and without stress) and five repetitions. Soybean plants were irrigated daily with the solutions, and a weekly depth of 25% higher than the demand of the culture was applied. The evaluations were carried out 49 days after sowing, evaluating the potential quantum efficiency of photosystem II (Fv/Fm), the effective quantum yield of PSII (ΦFSII), the photochemical (qP) and non-photochemical (qN) quenching, and the rate of electron transport (ETR). Plants subjected to irrigation with saline water showed reductions in the evaluated parameters, suggesting that the salts caused damage to the photosynthetic apparatus in the photochemical stage. The application of the biostimulant was effective in reducing damage to the photosynthetic apparatus, providing greater efficiency in dissipating excess energy and less reduction in ETR. The application that provided the best results was V3/R1.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".