Agronomic and physiological aspects of nitrogen use efficiency in conventional and organic cereal-based production systems
Bibliographic record
Abstract
Abstract Better management of synthetic nitrogen (N) fertilizers in conventional agricultural systems laid the foundation for feeding the increasing world's population since the Green Revolution. However, excessive reliance on inorganic fertilizer has resulted in environmental degradation issues. Difficulties in soil nutrition management in organic cropping systems often results in lower and variable yields, also raising questions of sustainability. Improving nitrogen use efficiency (NUE) is thus of key importance to overcome environmental concerns in conventional systems and production limitations in organic systems. The differences in the two farming systems have impacts on crop traits and N cycles, making it difficult to enhance NUE with a single strategy. Different approaches need to be adopted to improve NUE in each system. Extensive efforts have been made to better understand mechanisms to potentially improve NUE in cereal crops under both systems. This review suggests that NUE may be improved through a combination of management practices and breeding strategies specific to the management system. Diversified crop rotations with legumes are effective practices to optimize the N cycle in both conventional and organic systems. Best Management Practices coupled with nitrification inhibitors, controlled release products and split-application practices can reduce N loss in conventional systems. In organic systems, we need to take advantage of available N sources and adapt practices such as no-tillage, cover crops, and catch crops. Utilization of beneficial soil microorganisms is fundamental to optimizing availability of soil N. Estimation of soil organic matter mineralization using prediction models may be useful to enhance NUE if models are calibrated for target environments. Cereal crops are often bred under optimum N conditions and may not perform well under low N conditions. Thus, breeders can integrate genetic and phenotypic information to develop cultivars adapted to specific environments and cultivation practices. The proper choice and integration of strategies can synchronize N demand and supply within a system, resulting in reduced risk of N loss while improving NUE in both conventional and organic systems.
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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 | 0.000 | 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) | 0.000 | 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".