Integrated assessment of climate change impact on surface runoff contamination by pesticides
Bibliographic record
Abstract
Abstract Pesticide transport by surface runoff depends on climate, agricultural practices, topography, soil characteristics, crop type, and pest phenology. To accurately assess the impact of climate change, these factors must be accounted for in a single framework by integrating their interaction and uncertainty. This article presents the development and application of a framework to assess the impact of climate change on pesticide transport by surface runoff in southern Québec (Canada) for the 1981–2040 period. The crop enemies investigated were: weeds for corn (Zea mays); and for apple orchard (Malus pumila), 3 insect pests (codling moth [Cydia pomonella], plum curculio [Conotrachelus nenuphar], and apple maggot [Rhagoletis pomonella]), 2 diseases (apple scab [Venturia inaequalis], and fire blight [Erwinia amylovora]). A total of 23 climate simulations, 19 sites, and 11 active ingredients were considered. The relationship between climate and phenology was accounted for by bioclimatic models of the Computer Centre for Agricultural Pest Forecasting (CIPRA) software. Exported loads of pesticides were evaluated at the edge-of-field scale using the Pesticide Root Zone Model (PRZM), simulating both hydrology and chemical transport. A stochastic model was developed to account for PRZM parameter uncertainty. Results of this study indicate that for the 2011–2040 period, application dates would be advanced from 3 to 7 days on average with respect to the 1981–2010 period. However, the impact of climate change on maximum daily rainfall during the application window is not statistically significant, mainly due to the high variability of extreme rainfall events. Hence, for the studied sites and crop enemies considered, climate change impact on pesticide transported in surface runoff is not statistically significant throughout the 2011-2040 period. Integr Environ Assess Managem 2016;12:559–571. © Her Majesty the Queen in Right of Canada 2015; Published 2015 SETAC Key Points Integration of climate model data, bioclimatic model data, agricultural management scenarios, a pesticide transport model, and a stochastic model in a single framework to assess the climate change impact on exported pesticide loads in surface runoff. Assessment of climate change impacts on both pesticide applications and losses by surface runoff. Assessment of climate change impacts of major crop enemies in Québec (Canada) in a relatively near horizon (1981–2040). For the studied sites and crop enemies considered, climate change impact on pesticide transported in surface runoff is not statistically significant, mainly due to the high variability of intense rainfall events.
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 | 0.000 | 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) | 0.000 | 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".