Relationships between soil cations and plant characteristics based on spatial variability in a forage field
Bibliographic record
Abstract
Spatial variability within a forage field can be used to assess how soil properties relate to forage characteristics of yield, mineral composition, and nutritive value. The relationship between the dietary cation-anion difference (DCAD) of the forage and soil cations is of particular interest to dairy farmers because of problems related to hypocalcemia. An experiment was conducted on a dairy farm in southern Québec (Canada) to determine the variability in soil cations (K, Mg, Ca) and forage characteristics (yield, mineral composition, and nutritive value) and to establish relationships between them, primarily with respect to DCAD. A 10-ha field with a tile-drained silty loam soil was seeded in 1998 with a mixture of timothy (Phleum pratense L.) and red clover (Trifolium pratense L.). Soil samples were collected on a systematic grid in spring (n = 151) and fall (n = 38) 1999. Forage samples were collected at these sampling points at the first harvest in June 1999 and 2000. Soil K and K-related properties (K/CEC, Mg/K, Ca/K) displayed the largest variability among selected soil cation properties in both samplings, with coefficients of variation (CV) ranging from 35 to 58%. Dry matter (DM) yield, plant mineral composition, and forage nutritive value had low to moderate variabilities (8 < CV < 38%). Dry matter yield was not closely related to soil cations. High correlation coefficients were found between plant K and soil K (0.62 < r < 0.67), and between DCAD and soil K (0.67 < r < 0.72). Other plant mineral composition (P, Mg, Ca, S, Cl, and Na), CP, K/(Ca + Mg) and relative feed value showed low correlations and inconsistent trends with soil cations. Considering the importance of DCAD in predisposing dry cows to milk fever, the spatial variability of soil K should be considered to reduce the incidence of this metabolic disorder. Key words: Dietary cation-anion difference, forage, milk fever, spatial variability, soil K
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".