Effects of Conservation Agriculture on Land and Water Productivity in Yellow River Basin, China
Bibliographic record
Abstract
In the dryland regions of North China, water is the limiting factor for rainfed crop production. Conservation agriculture (featuring reduced or zero tillage, mulching, crop rotations and cover crops) has been proposed to improve soil and water conservation and enhance yields in these areas. Conservation agriculture systems typically result in increased crop water availability and agro-ecosystem productivity, and reduced soil erosion. To evaluate the potential of conservation agriculture to improve soil water balance and agricultural productivity, the DSSAT crop model was calibrated using the data of a field experiment in Shouyang County in the semi-arid northeastern part of the Yellow River Basin. The average annual precipitation at the site is 472 mm, 75% of which falls during the growing season. The site had a maize-fallow-maize rotation. data from two crop seasons (2005 and 2006) and four treatments for calibration and analysis were used . The treatments were: conventional tillage (CT), no-till with straw mulching (NTSM), all-straw incorporated (ASRT) and one-third residue left on the surface with no-till (RRT). The calibration results gave satisfactory agreement between field observed and model predicted values for crop yield for all treatments except RRT treatment , and for soil water content of different layers in the 150 cm soil profile for all treatments. The difference between observed and predicted values was in the range of 3 % -25% for maize yield and RMSE was in the range of 0.03-0.06 cm 3 /cm 3 for soil water content measured periodically each cropping season. While these results are encouraging, more rigorous calibration and independent model evaluation are warranted prior to making recommendations based on model simulations. Medium-term simulations (1995-2004) were conducted for three of the treatments using the calibrated model. The NTSM and ASRT treatments had similar or higher yields (by up to 36%), higher crop water productivity by up to 28% and reduced runoff of up to 93 % or 43 mm compared to CT treatment . Keywords: tillage, conservative agriculture, soil and water conservation, mulch, residues, CERES model , DSSAT model DOI: 10.3965/j.issn.1934-6344.20 1 0.0 2 .0 05 -0 17 Citation: Vinay Nangia, Mobin-ud-Din Ahmad, Du Jiantao, Yan Changrong, Gerrit Hoogenboom, Mei Xurong, et al . Effects of Conservation Agriculture on Land and Water Productivity in Yellow River Basin, China. Int J Agric & Biol Eng, 20 1 0; 3 ( 2 ): 5 <span style=line-height: 130%; font-family:
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".