Effect of conservation and conventional tillage on soil water storage, water use efficiency and productivity of corn and soybean in Northeast China
Bibliographic record
Abstract
The dominant factors affecting crop production in Northeast China are the low amount of rainfall in spring and high loss of soil water through evaporation during summer, both of which contribute to the lower soil water use efficiency (WUE). The objective of this study was to evaluate the long-term effects of no-till (NT), reduced tillage (RT) and conventional tillage (CT) on soil water storage (SWS), WUE, and soybean and corn yields in Northeast China from 2009 to 2011. The soil water contents under NT were higher than CT, especially in the 0–30 cm soil. SWS was lower in spring and autumn but higher in summer and it was influenced by both rainfall and tillage practices. NT had the greatest SWS and CT the least, with RT having intermediate values in the 30-cm surface. Leaf area index was higher for CT compared with the RT and NT in corn, but it was higher for RT than CT and NT in soybean. The evapotranspiration in the crop growing seasons was higher for NT and RT than for CT in the two corn years (2009, 2011), but no differences were found in the soybean year (2010). The WUE was greatly affected by weather conditions, (i.e., 64% lower in the wet year), and also affected by tillage; that is, CT and RT were 36 and 15% higher than NT in 2009, but no differences in other years. We conclude that RT was a good compromise for both soybean and corn crops, as it was associated with higher economic returns for farmers. Further research is required to compare the effect of the conservation tillage on the WUE and economic returns for other locations in Northeast China.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".