Crop rotation enhances soybean yields and soil health indicators
Bibliographic record
Abstract
Abstract Soybean ( Glycine max L.) is known to contribute to soil N reserves when grown in rotation with other high‐value crops such as corn ( Zea mays L.) and winter wheat ( Triticum aestivum L.). However, continuous soybean and “short” soybean rotations (e.g., corn–soybean, wheat–soybean) may cause declining soybean yields and degrading soil health over time. In this long‐term field study, we determined crop rotation effects on soybean yield and soil health indicators for a cool, humid, clay loam soil in southwestern Ontario. The study used nine soybean rotations, which included continuous soybean (S), corn–soybean (C–S), corn–soybean–soybean (C–S–S), and six rotations where winter wheat (WW) was grown with red clover ( Trifolium pratense L.) (+RC) and without underseeded red clover (i.e., winter wheat–soybean [WW–S, WW+RC–S], corn–soybean–winter wheat [C–S–WW, C–S–WW+RC], and winter wheat–soybean–soybean [WW–S–S, WW+RC–S–S]). Ten soil health indicators during the first and second soybean phase of each rotation were measured in 2018, whereas soybean yield was measured from 2002 to 2018. Soybean yields in 2‐ and 3‐yr rotations were 39–44% and 48–52% greater, respectively, relative to continuous soybean excluding the rotations with 2 of 3 yr of soybean (C–S–S, WW–S–S, WW+RC–S–S), which were only 22–35% greater than continuous soybean. Partial least squares regression indicated that inorganic N, particulate organic matter N, particulate organic matter C, potentially mineralizable N, total C, soil respiration rate, and water extractable organic soil C were the most important soil health indicators, explaining 34% of the total variation in soybean yields. It was concluded that soybean grown in 3‐yr rotations with corn and winter wheat produced the largest soybean yields and the greatest positive impacts on soil health indicators likely owing to cereal crops enhancing C inputs into soil.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".