An Evaluation of Minimum Tillage in the Corn-wheat Cropping System in Hebei Province, China: Wheat productivity and water conservation
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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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".