Exploring the key drivers of crop yields in Morocco – a systematic review
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
Introduction Morocco's agricultural sector faces significant socio-environmental challenges that threaten food security and economic development. A comprehensive assessment of these challenges is crucial for informed decision-making at both national and farm scales. This study aims to identify and analyze key drivers influencing crop yields in Morocco, with a focus on grain crops, by integrating climatic, socio-economic, and biophysical factors. Methods A systematic review of 135 peer-reviewed and grey literature sources published between 1990 and 2024 was conducted. The review examines both climatic and non-climatic factors affecting crop yields, particularly for wheat, a staple in Morocco’s food system. Results Precipitation emerged as the primary driver of crop yields, with approximately 15.6% of the literature analyzed emphasizing its impact. Other significant factors include irrigation, fertilization, water stress, temperature, technical efficiency, soil properties, conservation agriculture, insects and pests, sowing date, drought, crop varieties and genetics, diseases, herbicides, and extreme climatic events. These drivers interact in complex ways, with precipitation and irrigation playing pivotal roles in mitigating water stress and enhancing crop productivity. Discussion The findings highlight the intricate dependencies between climatic and agronomic factors affecting Morocco's grain production. Understanding these interactions is essential for policymakers and farmers to develop strategies that enhance agricultural sustainability and resilience. This study provides a foundation for impact-based analysis and evidence-based decision-making to improve productivity and ensure food security in Morocco.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| 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 it