Surface and Subsurface Transport of Nitrate Loss from the Selected Bioenergy Crop Fields: Systematic Review, Analysis and Future Directions
Bibliographic record
Abstract
Nitrate loss from bioenergy crop fields has attracted considerable attention during the last few years because of its potential negative impact on aquatic and human health. Both controllable and uncontrollable factors for nitrate loss have been the subject of several previous studies. Due to differences in climate, biophysical dissimilarities and land management characteristics in different parts of the world the factors affecting nitrate loss are often inconsistent and hence difficult to generalize. Therefore, reanalyzing the experimental field or plot scale studies to understand the nitrate loss factors in crop fields is useful and necessary in developing management strategies for reducing nitrate loss. This research synthesized and investigated 36 peer reviewed scientific journal articles related to selected bioenergy crop fields that included: continuous corn, corn in rotation with soybean, switchgrass and Miscanthus to conduct a meta-analysis of the available research. In this study, factors such as drain tile spacing, tillage practices, type and timing of the fertilization rate, irrigation and various other factors, which are challenging to represent in regression equations, were also systematically analyzed. In addition, various other agronomic characteristics that are attributed too nitrate loss are caused by perennially planted bio energized crops such as Miscanthus and switchgrass. Results indicated that 49% of nitrate loss through surface runoff from corn fields is directly related to the annual precipitation and fertilization rate. Multiple linear regression equations were developed to estimate the annual subsurface nitrate loss for the continuous corn fields with a R2 value of 0.65, 0.58 and 0.26 for sandy loam, silty loam and clay loam, respectively. Our analysis resulted in the conclusion that corn has a 2 to 3 times higher nitrate loss in surface runoff compared to switchgrass. Likewise, continuous corn and corn in rotation with soybean contributed more than 9 times the subsurface loss of nitrate compared to the established subsurface loss attributed to the Miscanthus and switchgrass.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".