Recent Achievements and New Research Opportunities for Optimizing Macronutrient Availability, Acquisition, and Distribution for Perennial Fruit Crops
Bibliographic record
Abstract
Tree responses to fertilizer management are complex and are influenced by the interactions between the environment, other organisms, and the combined genetics of composite trees. Increased consumer awareness of the environmental impact of agriculture has stimulated research toward increasing nutrient-use efficiency, improving environmental sustainability, and maximizing quality. Here, we highlight recent advancements and identify knowledge gaps in nutrient dynamics across the soil–rhizosphere–tree continuum for fruit crops. Beneficial soil management practices can enhance nutrient uptake and there has been significant progress in the understanding of how roots, microorganisms, and soil interact to enhance nutrient acquisition in the rhizosphere. Characterizing root architecture, in situ, still remains one of the greatest research challenges in perennial fruit research. However, the last decade has advanced the characterization of root nutrient uptake and transport in plants but studies in tree fruit crops have been limited. Calcium, and its balance relative to other macronutrients, has been a primary focus for mineral nutrient research because of its important contributions to the development of physiological disorders. However, annual elemental redistribution makes these interactions complex. The development of new approaches for measuring nutrient movement in soil and plant systems will be critical for achieving sustainable production of high-quality fruit in the future.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".