Impact of organic fertilizers on crop yield, soil carbon stock and greenhouse gas fluxes from corn-soybean agroecosystems
Bibliographic record
Abstract
Municipal organic waste transformed into organic fertilizers can replace mineral fertilizers to sustain crop production and soil organic carbon (SOC) stock but may induce soil greenhouse gas (GHG) emissions, depending on the physicochemical properties of organic fertilizer.This project evaluated the effect of three organic fertilizers on corn (Zea mays L.) and soybean (Glycine max L.) yields, SOC stock (0 -20 cm) and soil GHG fluxes.Organic fertilizers were composted food waste (compost; 240 kg N/ha), LysteGro biosolid slurry (LysteGro; 215 kg N/ha) and liquid anaerobic digestate (digestate; 231 kg N/ha), plus a mineral fertilizer control (NPK; 170 kg N/ha).Fertilizers were applied once at the beginning of the corn-soybean rotation at the Emile A.Lods Agronomy Centre (Lods), Ste-Anne-de-Bellevue, Quebec (8.4 -14.2 g SOC /kg, sandy loam soil) and the Elora Research Station (Elora), Elora, Ontario (21.3 -30.8 g SOC /kg, silt loam soil).Corn and soybean yields were similar among fertilizer treatments and comparable to regional averages, indicating satisfactory agronomic performance of all organic fertilizers.The SOC stocks remained similar after one-time application of organic fertilizers.Transient effects (within one month of fertilizer application) on N2O fluxes did not lead to any significant difference in the cumulative growing-season N2O and CO2 emissions.Methane fluxes were close to zero in all site-years.In addition, the long-term effect of these organic fertilizers on crop yields, SOC stock and soil N2O emission under two future climate scenarios (RCP4.5 and RCP8.5) from 2018 -2070 was simulated using the DayCent model after calibrating the model with two seasons of field data.DayCent predicted that digestate application would produce the highest corn silage yield (25 -28 % higher than NPK on average) whereas compost would produce the highest soybean grain yield (2.8 -4.4% higher than NPK) at both sites.Compost application was predicted to accrue the most SOC and have the lowest greenhouse gas intensities
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".