Precision Application of Manure and Promising Pollutant Mitigation Options
Bibliographic record
Abstract
The main objectives of the study are to assess mitigation potentials of nutrient losses (1) from fields managed under precision application of manure in comparison to flat rate manure and synthetic fertilizers, and (2) reduction of contaminants using biochar and gypsum. Nine micro -watersheds and three fertilizer management practices (i.e., precision variable rate application of solid cattle manure -precision manure, flat constant rate application of solid cattle manure -flat manure, flat constant rate application of anhydrous ammonia -flat synthetic) were applied at the contrasting micro-watersheds. Within each of the micro-watershed an East -Depression -West transect was established. In total, twenty-seven custom-made runoff collection frames (RCF) with the size of 1 m 2 were installed, and runoff (water and sediment) was funneled to containers. Three Teros-12 moisture sensors and associated ZL6 data loggers (Meter Environment, Pullman WA, U.S.A.) were mounted in the immediate vicinity of the RCF in three micro -watersheds. Arrow Gold GNSS GPS unit (EOS Positioning Systems, Terrebonne QC, Canada) were used for geo -referencing, and the surface slope and aspect were determined by a Brunton Transit Clinometer (Brunton GEO F-5010, Riverton WY, U.S.A.). In-season rainfall simulations using a portable Mini Rainfall Simulator (Eijkelkamp, Wilmington NC, U.S.A.) were conducted nearby the frames. Biochar was prepared from solid cattle manure and wheat straw mixed at a ratio of 3:1 ww, by the process of pyrolysis at 450 0 C, in the absence of oxygen. The final biochar product was ground, and dry sieved. Runoff, collected from the RCF of the precision manure and flat manure fields, was passed through amended soil columns contained in suspended polyvinyl chloride (PVC) tubes (i.e., control, soil + solid manure / straw derived biochar 10% ww , soil + gypsum 0.25% ww). Volumes of leachate were determined after 1, 5 and 24 hours and cumulated water samples were analysed for phosphorus (dissolved organic P, soluble reactive P, total P), available nitrogen (NH4-N, NO3-N), other micro and macro nutrients (K, SO4-S, Mg, Ca, Na), and trace elements. Preliminary results from 2021 showed that effect of the soil amendments was statistically significant (P < 0.001) but water source treatments were not. Biochar significantly (P < 0.001) reduced infiltration rate and cumulative leachate, however, gypsum treated columns were not significantly different (P = 0.094) from control. Biochar reduced Al, Co, Pb, Ni, Mo and Mn concentration in leachate and gypsum significantly reduced Se and V. In contrast, the addition of biochar and gypsum to soil increased the concentrations of NH4-N, SO4-S, Mg, Ca, and K. While the growing season of 2021 was exceptionally dry, near normal moisture conditions occurred for 2022. A summary of the two years (2021 and 2022) results will be presented.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".