Effects of Growth Medium and Water Stress on Soybean [Glycine max (L.) Merr.] Growth, Soil Water Extraction and Rooting Profiles by Depth in 1-m Rooting Columns
Bibliographic record
Abstract
The pattern of soil water availability in frequently watered small pots is different from field environments. In small pots, volumetric soil water content (VSWC) is relatively high throughout the rooting zone due to a lack of suction to remove water from large and midsize capillaries. This necessitates the use of growing media with large pore space to avoid anaerobic conditions and so prohibits the use of field soil (FS) in small pots. We hypothesized that in 1-m rooting columns, the 0.01-MPa gravitational potential difference between top and bottom may permit the use of lightly-amended FS as a growing medium and provide for realistic VSWC and rooting profiles by depth. This study aimed to investigate the effects of amending a typical sand-based potting mix with different proportions of FS on soybean growth [dry matter (DM) accumulation], water use, VSWC and rooting profiles by depth under control and water stress conditions, in 1-m rooting columns (polyvinyl chloride tubes having an inside diameter of 10 cm and length of 1 m). We tested three growth media (0, 50, and 67% FS mixes), watered daily to either 100% of the maximum soil water holding capacity (SWHC; control) or 75% SWHC (stress). VSWC was calculated from time-domain reflectometry measurements. Compared to all growth media, the 67% FS mix resulted in the highest DM accumulation, water use, water use efficiency (WUE), and also produced realistic VSWC and rooting profiles by depth similar to those reported in the literature under field conditions. Compared to the control, the water stress treatment reduced shoot DM by 24%, root DM by 13%, whole-plant DM by 22%, and water use by 25%, but increased root-to-shoot DM ratio by 18% and WUE by 6%. Of the three growth media tested, the 67% FS mix was the most suitable growth medium for controlled environment phenotyping studies of root functional traits affecting drought tolerance in soybean. This study provides novel phenotyping tools to select for root function and yield formation traits that could increase soybean yield under soil water deficit conditions.
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.
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) | 0.001 | 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 | 0.001 |
| 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".