Sensing of Crop Nitrogen Status: Opportunities, Tools, Limitations, and Supporting Information Requirements
Bibliographic record
Abstract
Diagnosing nitrogen (N) sufficiency in crops is used to help insure more effective management of N fertilizer application, and several indicators have been proposed to this end. The N nutrition index (NNI) offers a reliable measurement, but it is relatively difficult to determine. This index is based on the relationship between plant tissue N concentration and the biomass of the plant's aerial parts. However, a good estimate of the NNI should be obtained by nondestructive methods that can be carried out quickly. Although dependent on sites, chlorophyll meter (CM) measurements have been correlated with the NNI in corn ( Zea mays ). Since chlorophyll can be estimated through remote sensing, the possibility of quickly obtaining measurements for large surface areas points to practical applications for precision agriculture. When combined with the mapping of soil properties such as apparent electrical conductivity (EC), elevation and slope, such chlorophyll measurements make it possible to derive N fertilization recommendations by taking into account natural variations in the soil. Recently, an instrument called the Dualex (FORCE-A, Orsay, France) is marketed, which uses measurement methods based on the fluorescent properties of plant tissues. It is similar to the CM in terms of its operating principle but it measures polyphenolics (Phen), compounds that accumulate in the epidermis of leaves under N stress. Epidermal transmittance to ultraviolet light is assessed by the fluorescence excitation ratio F(ultraviolet)/F(REF), where F(ultraviolet) is the fluorescence excitation detected following ultraviolet excitation, and F(REF) is the fluorescence detected on excitation at a reference wavelength, not absorbed by the epidermis. Although the Dualex generally did not identify more differences among treatments than the CM in our studies on wheat ( Triticum aestivum ), corn, and broccoli ( Brassica oleracea ssp. italica ), combining the two measurements in a chlorophyll/Phen ratio improved the relationships with crop N nutrition status appreciably. This ratio can also be estimated by remote sensing techniques. The NNI on its own does not constitute an economically optimal recommendation for N fertilizer [economically optimal N rate (EONR)]. The EONR is the N rate at which profit is greatest. Work is currently being done to use overfertilized reference plots for this purpose and to permit an improved correlation between the indicator (NNI or chlorophyll) and EONR.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".