Development of a Comprehensive Nitrogen Budget to Increase Nitrogen Use Efficiency and Reduce Nitrogen Losses in Semi-Arid Southern Alberta
Bibliographic record
Abstract
Synthetic nitrogen (N) fertilizer has increased crop yields, but crop nitrogen use efficiency (NUE) is low. The N fertilizer not taken up by the crop is subject to nitrate leaching, ammonia volatilization, and denitrification losses, contributing to declining air and water quality, ozone layer depletion, and N2O emissions. Nitrogen budgets, which account for N inputs, N outputs, and changes in soil N stocks, can be used to assess the fate of N in the agroecosystem and to develop effective N management practices that increase NUE and reduce N losses. Process-based ecosystem models such as ecosys, which simulate biogeochemical cycling and feedback processes, may be used to generate low-cost and time-efficient estimates of the fate of N in the agroecosystem at variable spatial and temporal resolutions. To identify effective N fertilizer management practices, a comprehensive N budget was developed using the process-based model ecosys to assess the effects of N rate (0 – 120 kg N ha-1), N source (Urea vs ESN), irrigation vs dryland, and interannual climatic variability (2008 – 2011) on modelled crop yields, grain N, NUE, N losses, and soil N stocks at a cool, semi-arid site in Southern Alberta. Cool soil temperatures early in the growing season slowed modelled N release from ESN such that N availability from ESN did not better match early season crop N demand compared to conventional urea fertilizer, and ESN did not increase yields, or NUE, or reduce N losses. Nitrogen rate had a greater impact on the N budget than the N source, indicating the importance of optimal N rate applications in effective N fertilizer management. As modelled yield gains diminished (<3%) at N rates >90 kg N ha-1, and modelled N2O emissions increased linearly with N rate, reducing N fertilizer rate applications from the maximum N rate (120 kg N ha-1) in this study to economically optimum N rates (71 – 79 kg N ha-1) would result in N2O emission reductions of 18 – 22%, with only minimal yield reductions of 2.7 – 3.6%. Nitrogen fertilizer rate applications > 90 kg N ha-1 greatly increased modelled residual nitrate-N (15 – 51 kg N ha-1) compared to lower N rates, which was subject to downward nitrate-N movement beyond the crop rooting depth and N leaching. Irrigation and interannual climatic variability affected the magnitude of modelled NH3 and subsurface N losses, with dry years (e.g., 2009) and dryland sites having greater modelled volatilization losses and wet years (e.g., 2010) and irrigated sites having greater modelled subsurface N losses. When indirect N2O emissions from modelled volatilization, subsurface and surface N losses were included in N2O emissions accounting, on average, area-based emission factors increased by 0.06% (+24%), indicating the opportunity for N2O mitigation by reducing indirect N2O losses. The results from this thesis could provide a methodology for developing effective N management strategies that balance agronomic benefits with environmental impacts for policymakers and producers.
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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 | 0.001 | 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".