Improving Soil Available Nutrient Estimation by Integrating Modified WOFOST Model and Time-Series Earth Observations
Bibliographic record
Abstract
Information on soil available nutrient (SAN) at key crop growth stages is critical to generate prescription maps for implementing variable rate fertilization (VRF). Our previous study showed that integrating time-series remote sensing (RS) data with the modified World Food Studies (WOFOST) crop model provides a useful approach (preliminary RS-WOFOST-based method) to acquiring information on field SAN; however, the estimation accuracy was low for VRF application. In this paper, three steps were proposed to further improve the SAN estimation accuracy. At the first step, the rapid-nutrient assimilation (RNA) method was used to optimize the crop growth simulation process. Compared with the ensemble Kalman filter (EnKF) method, the RNA method showed an improved performance in estimating soil available nitrogen (N), phosphorus (P), and potassium (K) content [EnKF: R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.48 (N), 0.37 (P), 0.15 (K); RNA: R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.59 (N), 0.46 (P), and 0.18 (K)]. The improved K estimation at the first step was clearly lower than that of N and P; hence, the K content estimation was further optimized at the second step and the accuracy was improved (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.27) by using the estimated N as an input variable during the K estimation. At the third step, an iteration algorithm was implemented based on the first two steps, and the final R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.71 (N), 0.58 (P), 0.49 (K); root-mean-square error = 14.35 (N), 3.70 (P), and 14.87 (K). In general, the optimized approach can overcome the limitations of the preliminary RS-WOFOST-based method and improve the SAN estimation accuracy.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".