Modelling Soil Water Dynamics under Rainfed Agriculture to Mitigate Climate Change
Bibliographic record
Abstract
The model performance to simulate soil water dynamics was evaluated by comparing the predicted soil water content values with calculated soil water at different phenological stages of wheat and total soil available water using neutron probe. The pre-sowing soil water (mm) in this study varied from 40 to 50 mm in loam and sandy clay soil of Islamabad and Chakwal, respectively. When soil water is >50 mm, its effect on crop establishment is dependent on amount and temporal distribution of rainfall. Plant available soil water seems to be the most important factor if rainfall occurs between sowing and floral initiation period as happened during 2008-09 in the present study. The dynamics of soil water from emergence to maturity represented here as total soil water that remained maximum at earlier plant stages especially when crop roots were established and utilized soil water effectively that resulted in the lowest level of water at maturity which can be due to the evapotranspiration. The results depicted that the soil water distribution pattern mainly depends on soil properties and if sowing methodologies are resilient with available soil water then crop stand will be good and crop uses water much effectively. The results also depicted that when soil water is at drain upper limit (DUL) and the crop sown at proper time along with least soil evaporation, the soil water can be more easily taken up by the plant roots. Furthermore, the simulated soil water by the model was in close agreement with actual data. The validation skill scores like R2 confirmed the actuality of the model, therefore, dynamic model like Agricultural Production System Simulator (APSIM) could be used to describe the distribution of rainwater into different components like infiltration, runoff and drainage, and it can be used as a decision support tool for accurate management of different cultural operations for sustainable atmosphere-soil-plant (ASP) system.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".