Crop modelling with AquaCrop during climate change in the Ancash region of the Peruvian Andes
Bibliographic record
Abstract
Peruvian Andean rural farmers often have precarious livelihoods and already experience less predictable weather conditions than in recent decades. With the goal of investigating hydrological and agricultural resilience in a region with an uncertain climate future (with regard to both temperature and precipitation), we present here the results obtained from using the AquaCrop software to model both crop growth and the consequent harvest yields in the valleys of the Peruvian Andes, including the Rio Santa Valley in the Ancash region. The crop models are presented for 1970-2099 (the historical and the future during climate change), using RCP2.6 & RCP8.5 Regional Climate Models (RCMs) from CORDEX at a spatial resolution of 0.22 degrees. We chose the CORDEX RCM data that was dynamically downscaled from the CMIP5 GCMs instead of the CHELSA statistically-downscaled data, since the downscaling of the CORDEX RCM data produces more locally-heterogeneous climate averages, which are more consistent with the variable topography. The CORDEX RCM model data has subsequently been bias-corrected to monthly CHIRPS precipitation and monthly ECMWF ERA-Interim temperature extremes from 1981-2005 for locations in the Ancash region, including Yungay and Aija. For the various crops that we modelled (maize/corn, potatoes, dry beans, quinoa, wheat), we find significant interannual variability of the dry yields from crop harvest (without irrigation or fertilizers), particularly when the climate is transitioning to a warmer one for those crops that prefer warmer climates. Without the consideration of irrigation or fertilizers, the possibility of high yield interannual variability could make it difficult for the Peruvian Andean farmers to plan ahead, and maintaining a diversity of crops within the Rio Santa Valley and the wider Ancash region could be advantageous for these farmers.
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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 | 0.001 | 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".