The Impacts of Climate Variability on Crop Yields and Irrigation Water Demand in South Asia
Bibliographic record
Abstract
Accurate (spatio-temporal) estimation of the crop yield relation to climate variables is essential in the densely populated Indus, Ganges, and Brahmaputra (IGB) river basins of South Asia for devising appropriate adaptation strategies to ensure regional food and water security. This study examines wheat (Triticum aestivum) and rice (Oryza sativa) crop yields’ sensitivity to primary climate variables (i.e., temperature and precipitation) and related changes in irrigation water demand at different spatial (i.e., province/state, districts and grid cell) and temporal (i.e., seasonal and crop growth phase) scales. To estimate the climate driven variations in crop yields, observed and modelled data applying the Lund-Potsdam-Jena managed Land (LPJmL) model are used for six selected study sites in the IGB river basins over the period 1981–2010. Our statistical analysis underscores the importance of impacts assessments at higher spatio-temporal scales. Our grid cell (aggregated over study sites) scale analysis shows that 27–72% variations in wheat and 17–55% in rice crop yields are linked with temperature variations at a significance level of p < 0.001. In the absence of irrigation application, up to 39% variations in wheat and up to 75% variations in rice crop yields are associated with precipitation changes in all study sites. Whereas, observed crop yields show weak correlations with temperature at a coarser resolution, i.e., up to 4% at province and up to 31% at district scales. Crop yields also showed stronger sensitivity to climate variables at higher temporal scale (i.e., vegetative and reproductive phases) having statistically strong negative relationship with temperature and positive with precipitation during the reproductive phase. Similarly, crop phase-specific variations in climate variables have considerable impacts (i.e., quantity and timing) on irrigation water demand. For improved crop water planning, we suggest integrated climate impact assessments at higher spatio-temporal scales which can help to devise appropriate adaptation strategies for sustaining future food demand.
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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 | 0.000 | 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) | 0.000 | 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".