Interspecific root interactions enhance photosynthesis and biomass of intercropped millet and peanut plants
Bibliographic record
Abstract
Intercropping is commonly practiced worldwide because of its benefits to plant productivity and resource-use efficiency. Belowground interactions in these species-diverse agro-ecosystems can greatly contribute to enhancing crop yields; however, our understanding remains quite limited of how plant roots might interact to influence crop biomass, photosynthetic rates, and the regulation of different proteins involved in CO2 fixation and photosynthesis. We address this research gap by using a pot experiment that included three root-barrier treatments with full, partial and no root interactions between foxtail millet (Setaria italica (L.) P.Beauv.) and peanut (Arachis hypogaea L.) across two growing seasons. Biomass of millet and peanut plants in the treatment with full root interaction was 3.4 and 3.0 times higher, respectively, than in the treatment with no root interaction. Net photosynthetic rates also significantly increased by 112–127% and 275–306% in millet and peanut, respectively, with full root interaction compared with no root interaction. Root interactions (without barriers) contributed to the upregulation of key proteins in millet plants (i.e. ribulose 1,5-biphosphate carboxylase; chloroplast ß-carbonic anhydrase; phosphoglucomutase, cytoplasmic 2; and phosphoenolpyruvate carboxylase) and in peanut plants (i.e. ribulose 1,5-biphosphate carboxylase; glyceraldehyde-3-phosphate dehydrogenase; and phosphoglycerate kinase). Our results provide experimental evidence of a molecular basis that interspecific facilitation driven by positive root interactions can contribute to enhancing plant productivity and photosynthesis.
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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) | 0.001 | 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 | 0.001 |
| 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".