Temporal variability of soil fertility indicators and sampling periods in Québec
Bibliographic record
Abstract
An inadequate soil sampling time leads to difficulties in interpreting soil tests, to incorrect recommendations for soil amendments and fertilizers, and to inappropriate environmental protection restrictions. Soil samples may be collected from agricultural fields before, during, or after the crop growth period. Since the time of soil sample collection can affect soil tests results, the objective of this study was to evaluate the effect of sampling time on measurements representativity of 15 fertility indicators in two fields located in La Pocatière (Québec, Canada). The soils were of fine (G1) and medium (G2) textural groups and were sampled weekly for 33 weeks per year during four years. Data analyses included descriptive statistics, time-series decomposition, and time autocorrelation function (ACF). Since results of these analyses showed a clear seasonal effect only for Mehlich-3 extracted phosphorus (PM3), soil phosphorus saturation index (SPS) for both G1 and G2 soils, and for pHW for G1 only, we recommend that the sampling calendar should be restricted to the first five weeks of spring (until the end of May) and to the entire fall period (starting in early September). Also, the temporal autocorrelation was four weeks on average. This implies that, for an initial year, whichever date is chosen for the sampling, the following annual sampling should be done within a four-week time window (i.e., two weeks before until two weeks after the initial sampling date). Time series are an important element to consider in selecting a representative sampling period for soil fertility indicators.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".