Influence of the Soil Solarization Management and Fertilizer on Soil Temperature under Different Soil Tillage Systems
Bibliographic record
Abstract
The soil tillage system can notably influence soil solarization system under dry land farming in semiarid areas. Field experiments were conducted from 6/6/2013 to 1/9/2013, to evaluate the effects of soil tillage system, soil solarization system and fertilizer type on soil temperature at three depths as well as influence at gap between the mulch and soil surface. The experiment included three experimental factors. The first factor, soil tillage systems (moldboard plowing followed by spring disking {MP}, Rotary plough {RP}), the second factor represent the soil mulching systems in three levels (transparent mulch, black mulch and without mulch {control}), the third factor includes three fertilizers types (compose fertilizer; in compose fertilizer and chemical fertilizer). The results showed that the experimental factors effects on soil temperature, when used soil tillage system (MP), the higher soil temperature recorded at 10 cm depth ,its value 70 oC ( time 11 am) when mean solar radiation 1146 w/m2 in soil transparent mulching system and chemical fertilizer, as well as for treatment soil black mulch and compose fertilizer, but the lower soil temperature recorded at 30 cm depth, its value 35.8 oC (time 1 pm) when mean solar radiation 1147 w/m2 in soil transparent mulch and chemical fertilizer treatment. The highest soil temperature recorded at 10 cm depth, when used soil tillage system (RP), its value 78.3 oC (time 11 am) when mean solar radiation 1125 w/m2 in soil black mulch and chemical fertilizer, but the low soil temperature recorded at 30 cm depth, its value 41 oC (time 3 pm) when mean solar radiation 900 w/m2 in soil transparent mulch and compose fertilizer treatment. Moreover, the results showed the maximum temperature at gap between the mulch and the soil surface occurs at noon. Addition the obtained results demonstrated that all soil mulching system raised soil temperature substantially compared with without mulched.
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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".