Effects of P,K and Ca deficiency on the root morphology and nutrient absorption of Poncirus trifoliata seedlings
Bibliographic record
Abstract
【Objectives】Root system is particularly important for the nutrient and water uptake of fruit trees. Few researches are reported on the root architecture and spatial distribution related to nutrition because of the hugeness and complex of root distribution. In this research,Epson digital scanner and plasma atomic emission spectroscopy were used to study the two-dimensional architecture and nutrient concentration of plant roots,to explore the effects of nutrient deficiency on root morphology and the relationship with plant nutrient uptake.【Methods】A sand culture pot experiment was conducted and the seedlings of Poncirus trifoliata were grown as test materials. Root samples were scanned with an Epson digital scanner( Expression 10000 XL 1. 0,Epson Inc. Japan) and the data wereanalyzed with the WinRhizo Pro( S) v. 2004 b software( Regent Instruments Inc.,Canada) to obtain the root length,root volume and root surface area. The concentrations of nutrients were determined by inductively coupled plasma atomic emission spectroscopy( Thermo Inc,IRIS Advan,United States).【Results】The total root length,surface areas and volumes are reduced significantly in the nutrient deficiency media. When the root system was classified into fine,middle and coarse roots according to their diameters,the effects of nutrient deficiencies on the three fractions were different with treatments. Compared with the control,the root length,surface area and volume of fine and coarse roots are reduced,but the surface area and volume of middle roots are increased remarkably under P deficiency; the root length,surface area and volume of fine and middle roots are reduced under K and Ca deficiency. The nutrient deficiencies induce significant decreases of shoot and root biomass,stronger inhibition effect on shoot than on root under the P deficiency,but opposite effect under K deficiency,least effect under Ca deficiency. The Zn concentrations of plants are significantly increased under three nutrient deficiency media,the Fe concentration increased under the Ca deficiency but decreased under the K deficiency,Ca concentrations increased under the P and K deficiencies but decreased under the Ca deficiency.【Conclusions】The nutrient deficiencies lead to worse root morphology,as a concequence,decreases the absorption of nutrients in different extents and the growth of plant root and shoot,and leads to the peculiar symptoms corresponding to the different nutrient deficiency environment.
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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".