An Evaluation of Minimum Tillage in the Corn-wheat Cropping System in Hebei Province, China: Wheat productivity and water conservation
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Bibliographic record
Abstract
In North China where the main crops are winter wheat and summer corn, current agricultural practices involve minimum tillage for corn and full tillage for corn or wheat, and require large amounts of irrigation water, especially during the wheat growing season. Conservation tillage (CT) is a promising method of water conservation, but local farmers still question whether it will affect the yield of winter wheat. We conducted fieldwork during 2011-2014 in Xushui, Hebei, China, in order to compare the effects of various methods of tillage, mulching, and irrigation on the yield, soil moisture, and soil temperature under a summer corn/winter wheat double cropping system. Wheat grain yield in 2012-2013 did not differ significantly because of tillage, residue, and irrigation treatments. This means that reduced irrigation did not affect grain yield for all the treatments. However, in 2013-2014, the yield for minimum tillage with residue mulch (MTm) was significantly higher (19.5%) than that for full tillage with residue removal (FTr). Yields for MTm with reduced irrigation were 10.2% significantly higher than FTi with reduced irrigation. The positive crop response to MTm may have been due to relatively higher topsoil moisture and soil temperature under MTm than under FTi during the winter period. Minimum soil temperature for the inter-row at the 5-cm depth under MTm remained slightly higher than that under FTi during the winter of 2012–2013, with colder weather than in 2013-2014. Hence, after our two-year field experiment, we concluded that MTm resulted in higher grain yields as compared with FTr probably due to higher topsoil water content; MTm with reduced irrigation maintained high yields despite eliminating one round of irrigation. Therefore, MTm with reduced irrigation was more beneficial for winter wheat crop production in North China.
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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 | 0.004 | 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 | 0.001 |
| 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 it