Testing of Commercial Inoculants to Enhance P Uptake and Grain Yield of Promiscuous Soybean in Kenya
Bibliographic record
Abstract
The aim of this study was to assess the potential of commercial mycorrhizal inoculants and a rhizobial inoculant to improve soybean yield in Kenya. A promiscuous soybean variety was grown in a greenhouse pot study with two representative soils amended with either water-soluble mineral P or rock P to assess product performance. The performance of selected mycorrhizal inoculants combined with a rhizobial inoculant (Legumefix) was then assessed with farmer groups in three agroecological zones using a small-plot, randomized complete block design to assess soybean root colonization by mycorrhiza, nodulation, and plant biomass production in comparison to rhizobial inoculant alone or with water-soluble mineral P. Greenhouse results showed highly significant root colonization by commercial mycorrhizal inoculant alone (p < 0.001) and in interaction with soil type (p < 0.0001) and P source (p < 0.0001). However, no significant effect was shown in plant P uptake, biomass production, or leaf chlorophyll index. In field conditions, the effects of mycorrhizal and rhizobial inoculants in combination or alone were highly context-specific and may induce either a significant increase or decrease in root mycorrhizal colonization and nodule formation. Mycorrhizal and rhizobial inoculants in combination or alone had limited effect on plant P uptake, biomass production, leaf chlorophyll index, and grain yield. Though some mycorrhizal inoculants induced significant root colonization by mycorrhizal inoculants, this did not lead to higher soybean yield, even in soils with limited P content. Our results are further evidence that inoculant type, soil type, and P source are critical factors to evaluate commercial inoculants on a context-specific basis. However, our results highlight the need for the identification of additional targeting criteria, as inoculant type, soil type, and P source alone were not enough to be predictive of the response. Without the identification of predictive criteria for improved targeting, the economic use of such inoculants will remain elusive.
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.
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 | 0.000 |
| 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) | 0.001 | 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".