Recent Trends in the Yield-Nutrient-Water Nexus in Morocco
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Climate change is impacting environmental systems including agriculture. In Morocco, declining precipitation and increasing temperatures are negatively impacting crop yields. Consequently, crop yields in Morocco are now dependent on nutrient and water management. Most studies have focused on experimentation through fertilizer application and irrigation without any attention to the intrinsic linear relationships that exist between crop yields, fertilizers, and agricultural water withdrawal. The time series agricultural water withdrawal data were collected from AQUASTAT for the period 1990-2022 while data on nitrogen, phosphorous, and potash fertilizers were collected from FAOSTAT. Yield data for maize, barley, sorghum, and wheat were also collected from FAOSTAT. The data were analyzed using two machine learning models fitted through multiple linear regression. The key results show that for the three fertilizers, phosphates tend to have the strongest impacts and cause changes in crop yield as seen in the context of wheat. When both fertilizers and agricultural water withdrawal are fitted against yield, agricultural water withdrawals tend to have a strong relationship with yields. This work has helped us to identify which crops and management options need to be valorized in terms of increased access to nutrients and water.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it