Opportunities for improving phosphorus‐use efficiency in crop plants
Why this work is in the frame
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Bibliographic record
Abstract
Summary Limitation of grain crop productivity by phosphorus (P) is widespread and will probably increase in the future. Enhanced P efficiency can be achieved by improved uptake of phosphate from soil (P‐acquisition efficiency) and by improved productivity per unit P taken up (P‐use efficiency). This review focuses on improved P‐use efficiency, which can be achieved by plants that have overall lower P concentrations, and by optimal distribution and redistribution of P in the plant allowing maximum growth and biomass allocation to harvestable plant parts. Significant decreases in plant P pools may be possible, for example, through reductions of superfluous ribosomal RNA and replacement of phospholipids by sulfolipids and galactolipids. Improvements in P distribution within the plant may be possible by increased remobilization from tissues that no longer need it (e.g. senescing leaves) and reduced partitioning of P to developing grains. Such changes would prolong and enhance the productive use of P in photosynthesis and have nutritional and environmental benefits. Research considering physiological, metabolic, molecular biological, genetic and phylogenetic aspects of P‐use efficiency is urgently needed to allow significant progress to be made in our understanding of this complex trait. Contents Summary 306 I. The need to use phosphorus efficiently 307 II. P‐use efficiency and P dynamics in a growing crop 307 III. P pools in plants 307 IV. Phosphorus pools and growth rates 310 V. Are crops different from other plants in their P concentration? 310 VI. Phosphorus use and photosynthesis 311 VII. Crop development and canopy P distribution 312 VIII. Internal redistribution of P in a growing vegetative plant 313 IX. Allocation of P to reproductive structures 314 X. Constraints to P remobilisation 315 XI. Do physiological or phylogenetic trade‐offs constrain traits that could improve PUE? 316 XII. Identifying genetic loci associated with PUE 316 XIII. Conclusions 317 Acknowledgements 317 References 317
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it