A Systematic Review of Durum Wheat: Enhancing Production Systems by Exploring Genotype, Environment, and Management (G × E × M) Synergies
Bibliographic record
Abstract
According to the UN-FAO, agricultural production must increase by 50% by 2050 to meet global demand for food. This goal can be accomplished, in part, by the development of improved cultivars coupled with modern best management practices. Overall, wheat production on farms will have to increase significantly to meet future demand, and in the face of a changing climate the poses risk to even current rates of production. Durum wheat [Triticum turgidum L. ssp. durum (Desf.)] is used largely for pasta, couscous and bulgur production. Durum producers face a range of factors spanning abiotic (frost damage, drought and sprouting) and biotic (weed, disease and insect pests) stresses that impact yields and quality specifications desired by export market end-users. Serious biotic threats include Fusarium head blight (FHB) and weed pest pressures, which have increased as a result of herbicide resistance. While genetic progress for yield and quality is on pace with common wheat [Triticum aestivum L.], development of resistant durum cultivars to FHB is still lagging. Thus, successful biotic and abiotic threat mitigation are ideal case studies in Genotype (G)×Environment (E)×Management (M) interactions where superior cultivars (G) are grown in at-risk regions (E) and require unique approaches to management (M) for sustainable durum production. Transformational approaches to research are needed in order for agronomists, breeders and durum producers to overcome production constraints. Designing robust agronomic systems for durum demands scientific creativity and foresight based on a deep understanding of constitutive components and their innumerable interactions with each other and the environment. This encompasses development of durum production systems that suit specific agro-ecozones and close the yield gap between genetic potential and on-farm achieved yield. Advances in individual technologies (e.g., genetic improvements, new pesticides, seeding technologies) are of little benefit until they are melded into resilient G×E×M systems that will flourish in the field under unpredictable conditions of prairie farmlands. We explore how recent genetic progress and selected management innovations can lead to a resilient and transformative durum production system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".