An Inverse Correlation between Corn Temperature and Nitrogen Stress: A Field Case Study
Bibliographic record
Abstract
Nitrogen is one of the most important yield‐limiting nutrients for corn (Zea mays). The ability of thermal remote sensing to detect nitrogen deficiency in corn may enable precision agriculture to modify nitrogen rates according to field conditions. This study applies the exergy destruction principle as a theory to explain the inverse relationship between surface temperature and nitrogen rate. Two hypotheses were developed. First, it was hypothesized that agricultural crops experiencing greater growth and providing greater yield will have lower surface temperature. The second hypothesis was that corn grown under optimum levels of nitrogen will have lower surface temperatures compared to corn grown under nitrogen stressed conditions. Field studies were conducted during two summer seasons (2016 and 2017) on an established long‐term field trial of corn yield response to varying rates of nitrogen. It was found that corn surface temperature decreased as the rate of nitrogen increased. A shallow but statistically significant (P < 0.05) negative slope was observed consistently with increasing rates of nitrogen. Surface temperature measurements, however, were variable. This variability was the result of external and weather dependent variables that influenced leaf surface temperature. Despite this variability, the exergy destruction principle provides a theory from which thermal remote sensing can be applied through the use of surface temperature measurements to detect physiological stress in crop plants. Core Ideas Thermal remote sensing was proposed to detect nitrogen stress in corn plants. Nitrogen stressed plants had higher surface temperatures than less stressed plants. Temperature trends were consistent with the exergy destruction principle. Nitrogen‐temperature correlations were statistically significant at the 0.05 significance level. Corn yield increases with nitrogen rate increase and surface temperature decrease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".