Assessment of climate change impact on crop yield and irrigation water requirement of two major cereal crops (rice and wheat) in Bhaktapur district, Nepal
Bibliographic record
Abstract
Rice and wheat are major cereal crops in Nepal. Climate change impacts are widespread and farmers in developing countries like Nepal are among the most vulnerable. A study was carried out to assess the impact of climate change on yield and irrigation water requirement of these cereal crops in Bhaktapur, Nepal. Laboratory and soil-plant-air-water analysis showed silt-loam being the most dominant soil type in the study area. A yield simulation model, AquaCrop, was able to simulate the crop yield with reasonable accuracy. Future (2030–2060) crop yield simulations, on forcing the Providing Regional Climates for Impacts Studies (PRECIS) based on regional circulation model simulation indicated decreased (based on HadCM3Q0 projection) and increased (based on ECHAM5 projection) yield of monsoon rice for A1B scenario, and rather stable yield (for both projection) of winter wheat. Simulation results for management strategies indicated that the crop yield was mainly constrained by water scarcity and fertility stress emphasizing the need for proper water management and fertilizer application. Similarly, a proper deficit irrigation strategy was found to be suitable to stabilize the wheat yield in the dry season. Furthermore, an increase in fertilizer application dose was more effective in fully irrigated conditions than in rainfed conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".