Field-scale validation of an automated soil nitrate extraction and measurement system
Bibliographic record
Abstract
One of the many gaps that needs to be solved by precision agriculture technologies is the availability of an economic, automated, on-the-go mapping system that can be used to obtain intensive and accurate ‘real-time’ data on the levels of nitrate nitrogen (NO3–N) in the soil. A soil nitrate mapping system (SNMS) has been developed to provide a way to collect such data. This study was done to provide extensive field-scale validation testing of the system’s nitrate extraction and measurement sub-unit (NEMS) in two crop (wheat and carrot) production systems. Field conditions included conventional tillage (CT) versus no tillage (NT), inorganic versus organic fertilizer application, four soil groups and three points in time throughout the season. Detailed data analysis showed that: (i) the level of agreement, as measured by root mean squared error (RMSE), mean absolute error (MAE) and coefficient of efficiency (CE), between NEMS soil NO3–N and standard laboratory soil NO3–N measurements was excellent; (ii) at the field-scale, there was little practical difference when using either integer or real number data processing; (iii) regression equations can be used to enable field measurements of soil NO3–N using the NEMS to be obtained with laboratory accuracy; (iv) future designs of the SNMS’s control system can continue to use cheaper integer chip technology for processing the nitrate ion-selective electrode (NO3 −–ISE) readings; and (v) future designs of the SNMS would not need a soil moisture sensor, ultimately saving on manufacturing costs of a more simple system.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".