CROP SEQUENCE AND NITROGEN APPLICATION RATE EFFECT PRODUCTIVITY, NITROGEN USE EFFICIENCY, AND SOIL CARBON A SEMI-ARID POTATO PRODUCTION SYSTEM
Bibliographic record
Abstract
Potato is the most valuable vegetable crop grown in Canada, with production reaching 4.78 Mt—accounting for 29% of all vegetable crop receipts—in 2015. However, potato is a demanding crop that requires large rates of fertilizer, intensive use of tillage and is susceptible to water stress. An important consideration, therefore, is whether crops grown in rotation with potatoes can improve the overall nitrogen use efficiency of the cropping sequence and help counterbalance the small organic matter inputs provided by the potatoes. Through the quantification and comparison of multiple soil parameters, yield and N2O emissions from three different potato cropping sequences with different rates of N, this research aimed to find a combination of crops and nitrogen rates to improve the environmental sustainability of the potato production. The analysis of active carbon and soil microbial communities confirmed the negative impact of tillage on soil organic matter. Furthermore, the increase in N rates increased potato crop biomass but did not increase yield, indicating that N was not the limiting factor for yield. The use of faba beans as preceding crops is an interesting option to improve soil residual N (SRN) and reduce the need for N application, however the rate of mineralization is unpredictable. The results indicated the importance of soil sampling to evaluate the SRN and manage N rates accordingly, for all the crops that require N application. This would not only improve the cost of potato production but also reduce N2O emissions due to overapplication of N, as was observed in this study where N2O emissions increased with the increase of N rates. This research provides a look into the effect of three different cropping sequences on SOM, yield and N2O emissions and potential areas for mitigation.
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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 | 0.000 | 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".