The role of fungicides for effective disease management in cereal crops
Bibliographic record
Abstract
Fungicides are the last line of defence in the armoury of an integrated disease management (IDM) approach. They do not create yield, but protect an inherent yield potential that the grower may realize in the absence of disease. In the field, securing effective disease control from fungicide applications is dependent upon the disease pressure and the effectiveness of the fungicide to control that disease. Globally, the same fungicide active ingredients are used against a similar range of fungal pathogens. However, in the presence of the pathogen, the level of economic response to fungicide applications is primarily driven by the prevailing environmental conditions and their interactions with crop development and the pathogen. Fungicides are canopy management tools that influence the size and duration of the green leaf area (GLA) of the crop. The total number of fungicide applications links to the length of the growing season and the disease risk in that period. For example, the top three leaves of a wheat crop canopy might warrant protection for approximately 120 days in an irrigated wheat crop on the Canterbury Plains of New Zealand, but only 60 days in the dry land wheat crops of the Victorian Mallee in Australia. Combining our knowledge of fungicide effect on the crop canopy with soil water and nutrient availability enables better matching of fungicide product, dose and timing to a specific disease risk. It also enables better use of crop physiology models, such as APSIM (Agricultural Production Systems Simulator), to assist with in-crop fungicide decisions. This paper reviews the role of fungicides, principally the triazoles (FRAC Group 3), strobilurins (Group 11) and SDHI’s succinate dehydrogenase inhibitors (Group 7), in cereal disease management. It explains (i) why applying foliar fungicide by plant development stage (as well as disease threshold) confers advantages when fungicide mode of action and on-farm logistics are taken into consideration; (ii) gives examples of how fungicide management strategies are adjusted in Australia and New Zealand to take account of environmental conditions; and (iii) explains the importance of green leaf retention (GLR) in the realization of an economic response from fungicides.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".