THE INFLUENCE OF FIELD PEA ON CARBON AND NITROGEN DYNAMICS AND GREENHOUSE GAS EMISSIONS
Bibliographic record
Abstract
Pulse crops have been long associated with biological dinitrogen fixation and therefore improve the sustainability of cropping systems when included in rotation.However, studies indicate there may be additional benefits of including pulse crops in rotation.To quantify these potential benefits, soil processes and properties related to nitrogen (N) and carbon (C) cycling were examined in five crop rotations with and without field pea (Pisum sativum L.) in Scott, Saskatchewan.Gross mineralization and nitrification rates were determined using the 15 N isotope dilution technique in intact soil cores.To estimate the proportion of nitrous oxide (N 2 O) emissions derived from nitrification related processes rather than denitrification processes tracer techniques using 15 N were used.Field incubations were performed in 2008 at seeding (May 13), anthesis (July 8) and just after harvest (October 8).Mean mineralization and nitrification rates were not significantly different among rotations on any date and there was no significant difference in mean N 2 O emissions among rotations.From labeled 15 NO 3 -cores, it was determined that nitrification-related processes were the major contributors to N 2 O emissions.There was no difference among the rotations in microbial biomass carbon (MB-C) or microbial biomass N (MB-N) with the exception of MB-C in the continuous field pea (FP) and the canola (Brassica napus L.)-wheat (Triticum aestivum L.)-field pea (CNL-W-FP) rotation at anthesis.There was no effect of rotation on dissolved organic carbon (DOC) and only seasonal differences were observed with DOC levels being lower before seeding than at anthesis and post-harvest.Based on the results obtained from a single growing season, our results show that N benefits of including field pea in rotation, beyond dinitrigen fixation, were not detectable and that the immediate N benefit of including field pea in rotation may be due simply to the direct effects of biological dinitrogen (N 2 ) fixation.However, there have been reports of pulse crop benefits to succeeding crops in rotation.As a result, we investigated both the quantity and quality of crop residues, which can have an impact on soil properties and processes.Plants enriched with isotopic tracers can be used to trace crop residue decomposition to various C pools iii but only if the tracer is homogeneously distributed throughout the plant.In order to determine if repeat-pulse labeling could be used to trace crop residue decomposition, this method was followed using 13 CO 2 to enrich plant material of field pea and canola plants in a controlled environment.The distribution of 13 C throughout the plant parts (roots, stem, leaves, and pod) and biochemical fractions [acid detergent fiber (ADF) and acid detergent lignin (ADL)] were determined.It was found that 13 C was not homogeneously distributed throughout the plant parts or biochemical fractions.The pod fraction in particular was much less enriched in comparison to the other fractions.The ADL fraction was less enriched than the ADF fraction.Because of the heterogeneity of the label throughout the plant, modifications of the method are needed and 13 C distribution through out the plant needs to be assessed before the repeat-pulse method can be used to trace C residue through various C pools.Nevertheless, root contributions to below-ground C were successfully determined from the enriched root material and the resulting enriched soil.It was found that canola contributed more above-and below-ground residues than field pea, however canola was also higher in ADF and ADL fractions indicating a more recalcitrant residue.Research should continue to better define the impact of pulse crop residues on C and N cycling and subsequent crops in rotation.To all of you that have made this discovery interesting, challenging, and even fun, thank you so much.I have appreciated your insight, ambition, dedication and support.Specifically, I'd like to thank my supervisor, Dr. Angela Bedard-Haughn for guiding me through this journey with enthusiasm, encouragement, and patience, all the while, doing so with a sense of fun and much laughter.
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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 | 0.000 |
| 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) | 0.001 | 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".