Estimating resource acquisition and water-use traits in wine grapes using reflectance spectroscopy
Bibliographic record
Abstract
Abstract In agroecosystems, the variable expression of crop functional traits is expected to play a role in key processes, including plant nutrient cycling and water acquisition, that confer ecosystem resistance and/ or resilience to environmental change. The ability to estimate crop trait data is therefore critical to predict crop responses to environmental change, enabling more informed diagnosis of crop performance and on-farm management strategies. Yet, many traditional methods for quantifying plant traits are time-consuming and resource-intensive, limiting sample sizes and study durations. In response, high-throughput phenotyping— specifically reflectance spectroscopy— has emerged as a key element of plant trait research, capable of estimating plant traits more rapidly. However, little is known about whether or not reflectance spectroscopy can detect within-species variation in resource acquisition and plant-water traits. Using wine grapes ( V. vinifera subsp. vinifera ) as a focal crop, this study aimed to assess the ability of reflectance spectroscopy and the subsequent partial least squares regression modelling approach to quantify intraspecific variation in 12 functional traits across 12 different cultivars. Results showed significant differences in traits, especially in the photosynthetic and hydraulic traits, among closely related cultivars, falling along a resource-conservative to resource-acquisitive axis of variation. We also found that reflectance differentiated this fine-scale trait variation, specifically in leaf chemical and morphological traits, contributing to higher accuracy, and indicating that this HTP approach is viable for detailed trait estimation in diverse agroecosystems.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 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".