Enhancing environmental sustainability in eastern Canada's corn agroecosystem with controlled drainage and subsurface irrigation
Bibliographic record
Abstract
Abstract Water table management through controlled tile drainage and subsurface irrigation (CDSI), retrofitting to conventional tile drainage, has been developed to abate the environmental impacts of irrigation and drainage meanwhile supporting agroecosystems and crop productivity. Since the environmental profile of new technologies is a prerequisite to understanding their socio-economic benefits, a life cycle assessment was conducted to assess the environmental impacts of CDSI on corn production for the 2014 and 2015 growing seasons at St-Emmanuel, south-western Quebec in eastern Canada, compared to the free drainage (FD). Inventory flows of corn production with CDSI and FD were developed using biophysical data from field experiments and public databases. Then, environmental impacts were compared for corn production with CDSI and FD, including climate change, eutrophication potential, acidification potential, and toxicity. The assessment results show the environmental benefits of implementing CDSI, particularly in improving water quality. However, potential synergy and trade-offs of climate change, eutrophication, and acidification impacts from the implementation of CDSI, especially under different climatic conditions, should be further monitored to improve the performance of the technology. Nevertheless, CDSI and associated practices can be adopted as adaptation measures in agricultural water management to support agroecosystems and address the challenges posed by environmental impacts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".