Effects of Different Fertilization and Mulching Cultivation Methods on Yield and Soil Water Use of Winter Wheat on Weibei Dryland
Bibliographic record
Abstract
【Objective】 Research on effects of fertilization and mulching cultivation patterns on yield and water use of winter wheat is recognized to be of great significance in increasing crop yield and water and nutrient efficiency on Weibei dryland.【Method】Field experiments were carried out to study the effects of recommended N fertilization based on soil test,topdressing,ridge-mulching and furrow seeding,and covering soil surface by retention of wheat straw during summer fallow on winter wheat yield,biomass,harvesting index,water use efficiency and annual changes of soil water.【Result】 Results showed that compared to local farmers' cultivation mode(apply all the fertilizers to soil once before seeding),topdressing of partial N fertilizer(25% of total) in dry-land region increased winter wheat yield by 6%-14% and WUE by 7%-10%,with WUE reaching 12.2 kg·hm-2·mm-1 to 13.6 kg·hm-2·mm-1;ridge-mulching and furrow seeding combined with N reduced topdressing increased winter wheat yield by 15%-41% and WUE by 10%-30%,and the WUE were as high as 12.2 kg·hm-2·mm-1 to 16.5 kg·hm-2·mm-1.Optimizing nitrogen fertilizer application by 1/4 nitrogen fertilizer being topdressed and at the same time using the ridge-mulching and furrow seeding was found to be able to enhance deep soil water usage by winter wheat,increase water content of winter wheat at heading stage and then enhance the biomass and harvesting index at harvest.This method led to a higher soil water consumption over the whole growing season,but the water use efficiency was also increased due to much more increase of grain yield.However,only reducing nitrogen input did not increase the water use efficiency.Nitrogen reduced topdressing combined with ridge-mulching and furrow seeding also increased soil storage of rainfall water during summer fallow and the summer fallow efficiency,by covering furrow soil surface by retention of wheat straw and keeping the ridge mulched plastic film during summer fallow.This method achieved annual soil water balance and was proved to be able to sustainablely increase winter wheat yield.【Conclusion】Optimizing nitrogen application combined with ridge-mulching and furrow seeding was showed to be the cultivation and fertilization method to increase yield and water use efficiency for winter wheat growing in Weibei dryland area on the Loess Plateau.
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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".