Improving Photosynthesis Efficiency in Potato: A Review of Genetic and Agronomic Approaches
Bibliographic record
Abstract
Photosynthetic efficiency is the core physiological basis for the formation of crop productivity. The study of its regulatory mechanism has important theoretical value and practical significance for staple crops such as potato ( Solanum tuberosum L.) that are related to global food security. This study systematically explains the genetic improvement path and agronomic regulation system for improving potato photosynthetic efficiency: (1) Based on the perspective of photosynthetic physiological ecology, key limiting factors such as source-sink imbalance, photoinhibition and abiotic stress were analyzed; (2) From the perspective of molecular design breeding, CRISPR/Cas9-mediated photosynthetic gene editing technology and cross-species transfer strategies of key enzyme genes in C4 and CAM photosynthetic pathways were reviewed; (3) Through the agronomic regulation level, an efficiency-enhancing technology system with dynamic rationing of mineral nutrients, precise water regulation and coordinated application of plant growth regulators as the core was established. Combined with crop physiological experimental data, the role of chloroplast targeted modification in promoting the stability of photosystem II and the efficiency of the Calvin cycle was verified. The study further explored the application prospects of interdisciplinary technologies such as multi-omics integrated analysis, hyperspectral remote sensing monitoring and machine learning algorithms in whole genome association analysis and phenotypic omics research. Based on the scientific problems existing in existing research, such as the unclear regulation mechanism of metabolic networks and insufficient quantification of the interaction effect between genotype and environment, this study proposed the development direction of establishing a genetic-physiological-environmental multiscale coupling model to provide a theoretical framework for the directional improvement of potato photosynthetic performance and sustainable intensive production.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".