Modeling Barley Yield in a Dark Brown Chernozem after Discontinuation of Long‐term Manure Application
Bibliographic record
Abstract
Core Ideas Soil properties at the discontinuation of manuring adequately explained subsequent grain yield. Soil TOC, TP, TN, and NO 3 –N were most influential and consistent in explaining yield variability. No evidence of convergence in grain yield among the discontinued manure treatments after 7 yr. Fertilizer application on soils that have received long‐term manure application is often not necessary and typically not advised for environmental reasons. Soil properties at the discontinuation of manure application may assist in modeling crop yield trends on such soils in subsequent years. The data used in this study were from a long‐term field experiment initiated in 1973 at the Agriculture and Agri‐Food Canada Research and Development Centre in Lethbridge, Alberta. Soil and plant samples were collected annually from 1973 through 2010 from irrigated plots that were cropped to barley ( Hordeum vulgare L.) every year. Soil properties measured at the start of the experiment in 1973 were used to model yield in the control plots while soil properties measured at the end of annual manure applications in 2003 were used as predictors in partial least squares (PLS) regressions to model barley yield in subsequent years in plots in which manure application was discontinued. Soil total N, organic carbon, P, and NO 3 –N concentrations were the most important soil properties for modeling annual barley grain yield in manured soils using PLS regression. Our results indicate that initial measured soil properties can be a useful tool in modeling yearly crop yield. During the 7 yr following discontinuation of manure application, there was no evidence of convergence in grain yield among amendment treatments, reflecting that the soil nutrient levels were above agronomic thresholds for optimum yields. Long‐term monitoring of yield will assist in the development of models for predicting when nutrient application will become necessary.
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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 | 0.001 | 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".