Remote sensing-based spatiotemporal assessment of agricultural drought and its impact on crop yields in Punjab, Pakistan
Bibliographic record
Abstract
Long-term meteorological droughts disrupt hydrological balances and lead to agricultural droughts that affect crop yield. This study uses remote sensing techniques to analyze agricultural droughts in Punjab, Pakistan, over two decades. From MODIS satellite data, three drought indices, such as vegetation condition index (VCI), temperature condition index (TCI), and vegetation health index (VHI), were generated to identify drought years and assess agricultural impacts during the rabi and kharif cropping seasons from 2001 to 2020. Standardized Yield Residual Series (SYRS) and Standardized Drought Residual Series (SDRS) were used to evaluate the impact of agriculture droughts on rabi crops (wheat, barley, gram) and kharif crops (sugarcane, rice, maize, cotton) and to compute Crop Drought Resilience (CDR). Results showed that Punjab experienced extreme to mild droughts from 2001 to 2018, notably in 2002 and 2008, with yield losses of 39% for rice, 34% for sugarcane, and 25% for wheat. The Mann-Kendall (MK) test indicated a significant ( p < 0.001) upward trend in VHI for both cropping seasons, with trend breakpoints in 2009 and 2010. Stepwise linear regression found VHI was most predictive for gram yield (R 2 = 0.49), while VCI was most predictive for sugarcane (R 2 = 0.56) and rice (R 2 = 0.29). Polynomial regression demonstrated that SYRS gram is highly influenced by all drought indices, especially SDRS VHI (R 2 = 0.49), followed by SDRS VCI (R 2 = 0.44) and SDRS TCI (R 2 = 0.28). SYRS sugarcane and SYRS rice crops were primarily affected by SDRS VCI , with correlation coefficients of R 2 = 0.62 for sugarcane and R 2 = 0.33 for rice. This study concludes that gram, sugarcane, and wheat exhibit high to moderate non-resilience under extreme drought conditions, highlighting the vulnerability of these crops to climate variability. These findings are essential for developing targeted adaptation strategies to mitigate yield loss and ensure sustainable agriculture.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".