The potential of corn-soybean intercropping to improve the soil health status and biomass production in cool climate boreal ecosystems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Intercropping (IC) is a promising approach used to improve soil health and sustainable crop production. However, it is unknown whether IC improve the soil health status and biomass productivity of crops cultivated in podzols under cool climate in boreal ecosystems. Two silage corn and three forage soybean genotypes were cultivated either as inter or monocrop (MC) treatments in a randomized complete block design. IC resulted in 28% increase in total forage production (FP). A reduction in rhizosphere soil pH (RS-pH) was observed in the IC treatments. Conversely, the rhizosphere soil acid phosphatase (RS-APase) activity was significantly higher (26–46%) in the IC treatments and occurred concomitant with a significant increase in available phosphorus (RS-P available ) (26–74%) in the rhizosphere. Furthermore, IC enhanced the active microbial composition and strong positive correlations were observed between RS-P available , RS-APase, microbial biomass and FP; while RS-pH was negatively correlated with FP, RS-APase and RS-P available . These findings suggested silage corn intercropped with forage soybean could be a viable approach to enhance FP through improved active microbial community structure, RS-APase activity and RS-P available when cultivated on podzols in cool climate boreal ecosystem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 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 it