The long-term effects of crop rotation and fertilizer applications on soil health and crop productivity in Alberta
Bibliographic record
Abstract
Long-term agricultural management practices affect soil health. Five long-term rotations at the University of Alberta Breton Plots were sampled as part of the Soil Heath Institute (SHI) North American Project to Evaluate Soil Health Measurements (NAPESHM) in 2019: (1) check (no fertilizer addition), NPKS and manure fertility treatments of a wheat–fallow (WF) rotation; (2) check, NPKS and manure fertility treatments of a 5 yr cereal–forage rotation (with and without lime); (3) continuous forage (CF) receiving NPKS fertilizer; (4) continuous grain (CG) receiving NPKS fertilizer; and (5) an 8-yr “agro-ecological” rotation of barley, faba beans and forages receiving manure. In addition to the >25 soil health indicators measured as part of NAPESHM, soil moisture retention curves (SMRC), phospholipid fatty acid (PLFAs) profile, size distribution of water-stable aggregates and total C, N, 13C and 15N within each class of water-stable aggregates were measured on additional samples taken in 2020. These soil health indicators were used to calculate a site-specific soil health index (SPSHI) using methods similar to those used to develop the Cornell comprehensive assessment of soil health (CASH). Multivariate permutational multivariate analysis of variance (PERMANOVA) and non-metric multidimensional scaling (NMDS) were used to assess the significance of long-term crop rotation, fertilization and their interactions on the soil health indicators used to develop the SPSHI. The indicators in the SPSHI equation included autoclave-citrate-extractable (ACE) protein, pH, available P, Na, available water holding capacity (AWHC), the proportion of total carbon in aggregates (PTCA) and Phosphomonoesterase. The higher the SPSHI value, the better the soil health. The SPSHI values of each rotation-fertilizer treatment from high to low are 8-yr with manure (0.802), 5-yr cereal-forage with manure and lime (0.79), WF manure (0.686), 5-yr with manure (0.674), 5-yr NPKS with lime (0.633), CG NPKS (0.507), 5-yr check with lime (0.477), 5-yr NPKS (0.432), 5-yr check (0.418), WF with NPKS (0.403), CF with NPKS (0.389), and WF with check (0.38). The PERMANOVA results indicated significant effects of fertilizer treatments (p-value =0.0064), rotation treatments (p-value =0.0482) and their interaction (p-value =0.0095) on the soil health indicators. The primary difference in SPSHI values was caused by the difference of C and N input to soils, PTCA and pH in response to fertilizer, manure and rotations. The positive correlation between SPSHI values and crop yield is only weak to moderate, mainly because manure has a greater improvement on soil health than crop yield, whereas NPKS fertilizers had the opposite effect.
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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 | 0.000 | 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 | 0.000 | 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".