Multistage Soybean Biomass Inversion Models and Spatiotemporal Analyses considering Microtopography at the Sub-Field Scale
Bibliographic record
Abstract
Crop biomass is an agricultural indicator of productivity, and knowledge of the temporal and spatial variation in biomass in different topography is critical for the application of precision management techniques. This study integrates microtopography with multitemporal remote sensing observations to reveal biomass- and yield-limiting variables. Three SPOT-6 high spatial resolution images and six topographic variables were combined to model the spatial variation in soybean biomass at multiple stages during the growing season. The results showed the following. (i) The multiple regression model for biomass estimation that combines topographic variables with vegetation indices can achieve higher accuracy than a vegetation index model. (ii) Biomass varied dramatically with topography during the growing season. (iii) Microtopographic variables, such as curvature and slope, had distinctive impacts on crop conditions over a growing season. Early in the growing season, sunny upper slopes produced more biomass than shady lower slopes, whereas this trend reversed over the season. Gentle concave slopes (–1.2 m−1 < curvature < 0 m−1) showed greater productivity later in the season, while slopes with high concavity (curvature < –1.2 m−1) or high convexity (curvature > 0.75 m−1) suppressed crop growth. These conclusions can be used directly to precisely manage nutrients and water applications.
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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".