Effect of Climate Change on the Yield of Cereal Crops: A 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
By the end of this century, the average global temperature is predicted to rise due to the increasing release of greenhouse gases (GHGs) into the atmosphere. This change in climate can reduce agricultural yields, resulting in food insecurity. However, agricultural activities are one of the major contributors of GHGs and lower yields can trigger increased activity to meet the demand for food, resulting in higher quantities of GHGs released into the atmosphere. In this paper, we discuss the growth requirements and greenhouse gas release potential of staple cereal crops and assess the impact of climate change on their yields. Potential solutions for minimizing the influence of climate change on crop productivity are discussed. These include breeding to obtain cereals that are more tolerant to conditions caused by climate change, increased production of these new cultivars, improved irrigation, and more effective use of fertilizers. Furthermore, different predictive models inferred that climate change would reduce production of major cereal crops, except for millets due to their ability to grow in variable climatic conditions, and in dry areas due to a strong root system. Moreover, millets are not resource-intensive crops and release fewer greenhouse gases compared to other cereals. Therefore, in addition to addressing food security, millets have an enormous potential use for reducing the impact of agriculture on global warming and should be grown on a global scale as an alternative to major cereals and grains.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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