Bibliographic record
Abstract
In the Ethiopia crop agriculture, pillar in balancing the food diet of the majority people, as a whole and the cereals specifically is facing serious and vast challenges which in turn affect the supply of food grains. Various research institutes in Ethiopia have revealed several factors which affect crop production over the past three decades, but their findings are not synthesized into a whole and difficult to access as such. This study attempts to review various existing research findings and present them holistically. The most common factors are lack of more recently introduced improved seeds, initial capital for investment, loss of cropland, labor, pesticides, invasive alien species, farm storage techniques, methods of small scale irrigation, and religious and cultural challenges. Thorough understanding these constraints, researchers and policy makers can make useful recommendations to crop growers’ on farm improvement system and not on how to devastate well designed sustainable crop production systems. Succinct and collated scientific information would help to shade light on the best standards to overcome most factors affecting crop production and enhance the productivity and quality of crops. Moreover, this paper offers more implications and recommendations for various stakeholders in Ethiopian and similar contexts.
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.
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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".