Predicting Soil Organic Carbon and Total Nitrogen at the Farm Scale Using Quantitative Color Sensor Measurements
Bibliographic record
Abstract
Sensor technology can be a reliable and inexpensive means of gathering soils data for soil health assessment at the farm scale. This study demonstrates the use of color system readings from the Nix ProTM color sensor (Nix Sensor Ltd., Hamilton, ON, Canada) to predict soil organic carbon (SOC) as well as total nitrogen (TN) in variable, glacial till soils at the 147 ha Cornell University Willsboro Research Farm, located in Upstate New York, USA. Regression analysis was conducted using the natural log of SOC (lnSOC) and the natural log of TN (lnTN) as dependent variables, and sample depth and color data were used as predictors for 155 air dried soil samples. Analysis was conducted for combined samples, Alfisols, and Entisols as separate sample sets and separate models were developed using depth and color variables, and color variables only. Depth and L* were significant predictors of lnSOC and lnTN for all sample sets. The color variable b* was not a significant predictor of lnSOC for any soil sample set, but it was for lnTN for all sample sets. The lnSOC prediction model for Alfisols, which included depth, had the highest R2 value (0.81, p-value < 0.001). The lnSOC model for Entisols, which contained only color variables, had the lowest R2 (0.62, p-value < 0.001). The results suggest that the Nix ProTM color sensor is an effective tool for the rapid assessment of SOC and TN content for these soils. With the accuracy and low cost of this sensor technology, it will be possible to greatly increase the spatial and temporal density of SOC and TN estimates, which is critical for soil management.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".