Nitrous Oxide Emissions in Corn (Zea mays L) as Affected by Timing, Method of Application and Source of Dairy Manure
Bibliographic record
Abstract
Field trials were conducted during three years to evaluate the effect of timing, method of application and manure source on N2O emissions and corn grain yield at Elora, ON, Canada. A randomized block design was set up every year, evaluating two timings (fall vs. spring), three methods of manure application (surface broadcasting, incorporation and injection) and two manure sources (raw, RM vs. anaerobically digested, AD), using non steady state chambers. Three and two years of data were used to evaluate the effect of manure application timing and manure source respectively on N2O emission, considering also application methods in each experiment. A hybrid, decision tree-based flux calculation method (DTBM) was developed and chosen to calculate N2O emissions, given that it advantaged to other methods due to its ability to match each data type with the best model. Nitrous oxide emissions did not respond to timing of manure application; however, as the interaction year by manure application timing as well as application method significantly affected N2O emissions (p< 0.01 and p< 0.05, respectively). The effect of method on cumulative N2O emissions depended on manure source(p<0.01), since -1 surface broadcast AD had the highest emission (6.4 kg N2O-N ha ), and both injected AD and -1 incorporated RM had the lowest values (2.6 kg and 2.8 N2O-N ha , respectively). Manure source tended to affect cumulative N2O emissions (F=4.67, p<0.1), with the largest emissions for AD -1 (4.8 kg N2O-N ha ). Anaerobically digested manure was proven to reduce cumulative N2O emissions when it was fall injected to corn in cold climates; however, if AD is broadcasted or broadcasted and incorporated, it may result in greater N2O emissions than those produced by RM. Short (2-3 yrs.) and long term (26 yrs.) trends for cumulative N2O emissions were simulated with a process-based model (DNDC-CAN). Even though no difference between predicted application timings was found at short-term, spring application was detected to decrease N2O emissions in the long term. The inter-annual variability canceled the effects of method of application in the long term on predicted N2O emissions. Injection of AD showed to be a good technique to mitigate predicted N2O emissions in the long term.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".