Variations in Corn Yield and Nitrogen Uptake in Relation to Soil Attributes and Nitrogen Availability Indices
Bibliographic record
Abstract
Identification of soil attributes most determinant to crop yield is still a matter of debate. The main objective of the present study was to relate the variations in corn ( Zea mays L.) yield and N uptake to 16 soil attributes. Samples were collected in 2005 and 2006 from a long‐term experiment. Soil organic C (SOC), total N (TN), potential mineralizable N (PMN), NO 3 extractable with KCl (NO 3 –KCl) and CaCl 2 (NO 3 –CaCl 2 ), NO 3 adsorbed on anion exchange membranes (NO 3 –AEM), N extracted with NaHCO 3 read at 205 nm (N‐NaHCO 3 −205) and 220 nm (N‐NaHCO 3 −220), N present in fulvic acid (FA‐N), humic acid (HA‐N) and non‐humified fractions (NHF‐N), mean weight diameter of aggregates (MWD), total, macro‐ and microporosity, and bulk density (D b ) were measured. Principal component analysis (PCA) was conducted with the measured soil attributes, and the principal components (PCs) were used in a stepwise regression with corn yield and N uptake. In both years, a maximum of 88% of the total variance was explained. The stepwise regression analysis indicated that the first two PCs explained 78 to 91% of the variability in corn yield and N uptake. Based on the PCA, TN, HA‐N, NO 3 –KCl, NO 3 –CaCl 2, NO 3 –AEM, and PMN appeared as primary indicators of corn yield and N uptake, whereas MWD, FA‐N, and NHF‐N appeared as secondary indicators. When the variability in corn yield and N uptake explained by each N availability index was assessed, NO 3 –KCl and NO 3 –CaCl 2 appeared as the best predictors of corn yield because of their ease of measurement and reliability across years.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".