Economic Optimum Nitrogen Fertilizer Rate and Residual Soil Nitrate as Influenced by Soil Texture in Corn Production
Bibliographic record
Abstract
Core Ideas A 12 site‐year study representing four soil surface textural group was conducted. Soil texture significantly influenced the economic optimum N rate (EONR). Overall, clay and loam soils showed lower EONR and higher optimum corn yield. Residual soil NO3–N (RSN) at EONR was higher in sandy soils. At ∆EONR higher than zero, RSN was lower in clay and loam soils. Soil texture has been reported to be a significant factor influencing economic optimum N rate (EONR) and residual soil nitrate (RSN). Therefore, this study aimed to (i) determine the impact of soil texture on EONR and (ii) investigate the interactive impact of N rate and soil texture on RSN in corn (Zea mays L.) production. The study was conducted over 12 site‐years in Quebec, Canada, and included six N rates (0–250 kg N ha−1) and four soil surface textural groups (clay, loam, sandy belonging to the gleysolic soil order [Sg] and sandy belonging to the podzolic soil order [Sp]). The quadratic plus plateau model, best described corn grain yield response and was used for predicting EONR. The EONR was greatest in the Sg soil (173 kg N ha−1) and lowest in the Sp soil (123 kg N ha−1), with the Sp grain yield being nearly 60% less than that predicted at other soil textural groups. The EONR in clay and loam soils was 144 and 164 kg N ha−1 with estimated grain yield of 12.7 and 12.0 Mg ha−1, respectively. The RSN content was greatest in Sg and Sp soils. The estimated RSN at EONR in Sp soil was lower than measured, indicating possible N losses in this soil. This study clearly demonstrated that soil texture should be major criteria on which static N rate recommendations are made to optimize corn grain yield and avoid RSN accumulation in the soil profile, particularly under wet conditions of Quebec.
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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 | 0.001 |
| 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".