Evaluating the Effectiveness of Rhizobium Inoculants and Micronutrients as Technologies for Nepalese Common Bean Smallholder Farmers in the Real-World Context of Highly Variable Hillside Environments and Indigenous Farming Practices
Bibliographic record
Abstract
Studies have shown the potential of rhizobia and associated micronutrients to enhance symbiotic nitrogen fixation in legumes. Tens of millions of smallholder farmers, however, farm on mountain hillsides in highly variable soil and microenvironments, with different crop rotations, inputs and cultural practices. Here, on the terraces of the Nepalese Himalayas, we evaluated rhizobium inoculants (local, exotic), micronutrients (molybdenum, boron) and their combinations as technologies for smallholder farmers under highly variable microenvironments and traditional practices. The study was conducted as a series of participatory on-farm trials with 39 terrace farmers in two mid-hill districts of Nepal (Dhading, Kaski) from 2015 to 2017. Plots were measured for relevant agronomic traits. As expected, when comparing treatment plots with adjacent control plots within each farm, the results demonstrated tremendous farm-to-farm variability for nodulation, vegetative biomass, shoot nitrogen content, grain yield, and grain N content. Despite the variation observed, the data showed that the number of farms that showed yield increases from the rhizobium interventions, compared to those that suffered yield losses, was generally 2:1. We discuss potential experimental and socio-agronomic reasons for the variable results, including rainfall, which appeared critical. The results demonstrate the promise of rhizobium interventions for hillside smallholder farmers, even in a highly variable context.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".