Statistical and In-field Challenges Involved in Quantifying Crop Nitrogen Use Efficiency (NUE) and Spatial Soil Fertility in Central Alberta
Bibliographic record
Abstract
Modern agriculture faces the conundrum of a looming threat of food scarcity and heightened pressure on natural resources to address and sustain increasing food demand. Improving nutrient use efficiency is crucial to sustainable food production. It can be helpful in tackling this critical challenge while delivering the required benefits on social, environmental, and economic fronts. Given the limited availability of readily accessible available soil nitrogen (N) and the high cost of synthetic nitrogenous fertilizers, nitrogen use efficiency (NUE) becomes central to the effectiveness of any management practice aimed at sustainable agriculture. In this study, I evaluated the statistical challenges involved in defining NUE as a ratio of grain productivity to available soil nitrate (AN). Ratio analyses and different regression models were used to compare NUE. Measures of goodness of fit showed that quadratic regression (QR) models were comparatively more robust in estimating NUE. This finding elucidated a fundamental limitation in most analyses of NUE as a ratio matrix, as it negated the assumption of isometry crucial to validity of the derived conclusions. Nonetheless, results from QR analysis can be extrapolated to extract information of practical significance, such as the agronomically optimum N rate (AONR) and economic optimum N rate (EONR). Moreover, sample size calculations elucidated the need for a large number of plots to distinguish genotypes differing for NUE; therefore, imposing a logistic constraint to accurately assess differences in NUE. Strategies for improving nutrient management in croplands such as the 4R Nutrient Stewardship offer a promising avenue to address the seemingly contrasting goals of modern agriculture. In this study, I compared multiple linear regression, a non-geostatistical technique, to different geo-statistical techniques, including ordinary kriging (OK), ordinary cokriging (OCK), and regression kriging (RK) to decipher the spatial structure of soil fertility parameters. Based on cross-validation iii estimates, OK in most cases proved to be the model choice to predict soil nutrients, including available nitrogen, readily available phosphorus, and available potassium. In contrast, RK was the best performing method to estimate cation exchange capacity, pH, and organic matter. Landscape position did not show a strong spatial correlation with soil fertility parameters and grain productivity, as terrain attributes failed to substantively improve the corresponding predicted estimates.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".