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Record W2469789779 · doi:10.25165/ijabe.v3i2.212

Effects of Conservation Agriculture on Land and Water Productivity in Yellow River Basin, China

2010· article· en· W2469789779 on OpenAlex
Vinay Nangia, Mobin‐ud‐Din Ahmad, Du Jiantao, Yan Chang-rong, Gerrit Hoogenboom, Mei Xurong, Wenqing He, Shuang Liu, Qin Liu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational journal of agricultural and biological engineering · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Agricultural Sciences
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsEnvironmental scienceConservation agricultureAgronomyTillageDryland farmingDSSATWater balanceSoil conservationConventional tillageMulchNo-till farmingCrop yieldCrop residueGrowing seasonCrop rotationAgricultureSoil waterCropGeographySoil fertilitySoil scienceBiology

Abstract

fetched live from OpenAlex

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:

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.775
Threshold uncertainty score0.122

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.002
GPT teacher head0.154
Teacher spread0.152 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it