Costs and benefits of insecticide and foliar nutrient applications to huanglongbing‐infected citrus trees
Bibliographic record
Abstract
Abstract BACKGROUND The Asian citrus psyllid (ACP), Diaphorina citri Kuwayama (Hemiptera: Psyllidae), vectors Candidatus Liberibacter asiaticus, which causes huanglongbing (HLB). In Florida, HLB incidence is approaching 100% statewide. Yields have decreased and production costs have increased since 2005. Despite this, some growers are maintaining a level of production and attribute this in part to aggressive psyllid control and foliar nutrition sprays. However, the value of these practices is debated. A replicated field study was initiated in 2008 in a commercial block of ‘Valencia’ sweet orange trees to evaluate individual and combined effects of foliar nutrition and ACP control. Results from 2012–2016 are presented. RESULTS Insecticides consistently reduced ACP populations. However, neither insecticide nor nutrition applications significantly influenced HLB incidence or PCR copy number in mature trees. In reset trees, infection continued to build and reached 100% in all treatments. Greatest yields (kg fruit ha−1) and production (kg solids ha−1) were obtained from trees receiving both insecticides and foliar nutrition. CONCLUSION All treatments resulted in production and financial gains relative to controls. However, material and application costs associated with the nutrition component offset these gains, resulting in lesser benefits than insecticides applied alone. © 2016 Society of Chemical Industry
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 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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".