Agricultural Biosolids Application: Greenhouse Gas Emissions, Nitrogen Dynamics, and Crop Productivity
Bibliographic record
Abstract
Agricultural activities contribute greatly to greenhouse gas (GHG) emissions, accounting for 14% of the total anthropogenic emissions of GHG such as nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4). Of these GHG, N2O is the most concerning gas because of its high global warming potential (GWP), 298 times higher than that of CO2, and its capacity for depleting stratospheric ozone. The increase of nitrogen fertilizer additions over the years has made agricultural soils responsible for around 60% of global anthropogenic N2O emissions. Biosolids are by-products from sewage treatment processes that can be land applied to agricultural soils in order to recycle their nutrients, improve soil properties and decrease the dependence on commercial fertilizers; however, GHG can be released from this practice. Thus, there is a need to understand the effects of biosolid additions on N dynamics, nutrient use efficiency, crop productivity, and the amount of GHG emissions released by this practice. In a field study, we evaluated the fluxes of N2O, CH4 and CO2, soil available N, barley (Hordeum vulgare L.) biomass productivity, and nitrogen use efficiency (NUE) in croplands receiving three types of biosolids (mesophilic anaerobic digested [BM], composted [BC], and alkaline-stabilized [BA]) and granular urea in a Black Chernozem soil in Central Alberta, Canada, over three experimental site-years. The combinations of each biosolid with urea was also evaluated. All N source treatments were assessed in two placements: surface (S) and incorporation (I) to 15 cm soil depth. Nitrous oxide emissions were triggered by concurrent increases of soil moisture and available N, and incorporation of the N source increased N2O emissions compared to surface-applied N. Annual N2O emission factor (EFarea) from urea-amended soils (0.62 ± 0.14%) were fivefold higher than those from soils receiving only BA or BC (0.12 ± 0.04% or 0.12 ± 0.03%, respectively, P < 0.05), but EFarea from soils amended with only BM (1.33 ± 034%) was more than double the EFarea from urea-amended soils (P > 0.05). Carbon dioxide (CO2) fluxes generally followed similar patterns as the N2O fluxes, while CH4 fluxes were minimal. Overall, the mesophilic anaerobic digested under incorporation treatment (BMI) showed the highest GHG emissions. Results of a partial GHG balance showed that N2O emissions were the main contributor (up to 96%), while urea manufacturing contribution to the GHG balance was up to 52%. This offset the comparatively low field N2O emissions from the urea-amended fields, leading to even higher CO2 equivalents than the BA- and BC-amended fields. Incorporating the N sources enhanced barley biomass, and in certain cases, the combinations of biosolids and urea (e.g., BMURI, BMURS, BCURS) showed even higher biomass and NUE, as well as lower N2O emissions than biosolids-amended soils. Moreover, in an incubation study, we examined the effect of moisture (i.e., 28, 40, 52, and 64% WFPS) and the different types of biosolids aforementioned (i.e., BM, BA, and BC) on N2O production in the referred Black Chernozem soil. We found how the different biosolids properties and soil water contents influenced soil available N dynamics to produce N2O emissions. BM- and BC-amended soils were the higher N2O emitters, and emissions increased with soil moisture. These biosolids-amended soils also showed higher nitrification rates than BA-amended soils and the controls. The NO3−–N concentration by the end of the experiment was well correlated with the total N2O production (r = 0.91). In addition, we examined the sources and priming of N2O production as a function of 15N-labelled urea addition and multiple moisture contents (28, 40, 52, and 64% WFPS) in a Black Chernozem soil (high SOM: 55 g organic C kg−1). More N2O was sourced from SOM than added urea, with 59 ± 2% N2O originating from SOM, and SOM-derived N2O under urea was larger than that of the control, revealing a positive N2O priming triggered by urea addition (19 ± 2% of the total N2O from urea-amended soils). In summary, our findings will help to improve prediction ability and mitigation strategies for GHG emissions, particularly for N2O, from agricultural soils receiving biosolids additions.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".