Évaluation des pertes de phosphore agricole par ruissellement, drainage et lessivage dans un contexte du Québec : application de l'outil LoPhos
Bibliographic record
Abstract
Longtemps négligé dans le bilan environnemental des activités agricoles, le phosphore apparaît de plus en plus comme un contaminant potentiel d`importance, tant pour les eaux de surface que pour les eaux souterraines. Réputé s`adsorber facilement et rapidement sur les particules argileuses, le phosphore était en effet peu retrouvé dans les environnements hydriques malgré des doses d`application parfois massives, comme dans le cas des épandages de lisier d`élevage. Cependant, du fait des apports massifs soutenus sur des surfaces agricoles parfois limitées, une saturation en phosphore de certains sols peut survenir et conduire alors à des pertes aussi importantes que les apports. Confronté à cette problématique depuis de nombreuses années, le Québec a développé une approche novatrice dans la quantification et le suivi de la saturation des sols en phosphore. Parmi les approches développées au Québec, le logiciel LoPhos permet d`évaluer les pertes en phosphore sur une exploitation agricole à partir de données facilement disponibles. Une application de cet outil sur un bassin agricole du Québec est présentée dans cet article, mettant en évidence l`importance de la saturation du sol en phosphore et l`impact de l`amélioration des pratiques agricoles. La transposition des connaissances acquises au Québec et d`outils comme LoPhos offre des perspectives intéressantes pour la France, où la problématique relative aux épandages d`effluents d`élevage émerge actuellement pour le paramètre phosphore. / Often neglected until now in the environmental budget of agricultural activities, phosphorus more and more appears as a potential important contaminant as well for the surface water as for ground water. Easily and rapidly adsorbed by clay particles, phosphorus wasn`t very found in water resources despite application rates sometimes excessive, such as with liquid manure spreading. However, because a continuous and significant spreading on sometimes limited agricultural areas, a saturation of soil can be reached and losses of phosphorus may then be as large as the application rate. For that, Quebec has developed innovative techniques to quantify and survey the phosphorus soil saturation. Among them, LoPhos is a tool that evaluates the phosphorus losses over an agricultural farm from easily available data. An application of this tool on a watershed of Quebec is presented in this paper showing the importance of the soil saturation and the impact of best management practices. The transposition in France of the knowledge developed in Quebec and such tool like LoPhos offer interesting perspectives when appears at present a phosphorus problematic related to the spreading of farming effluents.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".