Previous and Current Crop Effects on Early-Season Root Growth and Growing Season’s Soil Moisture Under Dryland Agriculture in Temperate Climate
Bibliographic record
Abstract
Understanding the root growth and changes in soil moisture content during the growing season for dryland agriculture crops can improve crop production. It was hypothesized that early-season root growth might be influenced by previous crop and current crops, and soil moisture content and depletion pattern during the growing season and residual soil moisture may be affected by the crop type. A study was conducted on the early-season root growth of canola (Brassica napus L.), wheat (Triticum aestivum L.), and flax (Linum usitatissimum L.) in 2015; and changes in soil water content during the 2013, 2014, and 2015 growing seasons under canola, flax, wheat, barley (Hordeum vulgare L.), and pea (Pisum sativum L.). Early-season root growth of the canola and flax crops was better on wheat than canola stubble, while for wheat it was similar on the stubbles of both wheat and canola. Soil moisture depletion started relatively earlier under the barley and wheat and later under the flax compared to the canola and pea crops. Flax continued to deplete soil moisture for a longer period than the other crops. With some exceptions, all crops could deplete soil moisture to a similar level (down to about 15% or somewhat lower) by the end of their growing seasons. Generally, almost equal amounts of residual soil moisture remained after the different crops.
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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 | 0.000 | 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) | 0.001 | 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".