Soil quality indicators as influenced by 5-year diversified and monoculture cropping systems
Bibliographic record
Abstract
Abstract Increasing crop diversity has been highly recommended because of its environmental and economic benefits. However, the impacts of crop diversity on soil properties are not well documented. Thus, the present study was conducted to assess the impacts of crop diversity on selected soil quality indicators. The cropping systems investigated here included wheat (Triticum aestivumL.) grown continuously for 5 years as mono-cropping (MC), and a 5-year cropping sequence [(wheat–cover crop (CC)–corn (Zea maysL.)–pea (Pisum sativumL.) and barley (Hordeum vulgareL.)–sunflower (Helianthus annuus L.)]. Each crop was present every year. This study was conducted in the northern Great Plains of North America, and soil quality data were collected for 2016 and 2017. Selected soil quality indicators that include: soil pH, organic carbon (SOC), cold water-extractable C (CWC) and N (CWN), hot water-extractable C (HWC) and N (HWN), microbial biomass carbon (MBC), bulk density (BD), water retention (SWR), wet soil aggregate stability (WAS), and urease andβ-glucoside enzyme activity were measured after the completion of 5-year rotation cycle and the following year. Crop diversity did not affect soil pH, CWC, CWN, HWC, HWN and SWR. Cropping systems that contained CC increased SOC at shallow depths compared to the systems that did not have CC. Crop diversity increased WAS, MBC, and urease andβ-glucoside enzyme activity compared with the MC. Comparison of electrical conductivity (EC) measured in this study to the baseline values at the research site prior to the establishment of treatments revealed that crop rotation decreased EC over time. Results indicate that crop diversity can improve soil quality, thus promoting sustainable agriculture.
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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 | 0.001 |
| 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 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".