Root Nodule Preparation as a Low-Cost Inoculant for Cowpea
Bibliographic record
Abstract
Widely grown in diverse regions of Brazil, cowpea has high nutritional value, is easily cultivated, and can fix nitrogen (N) symbiotically. Although there are commercial inoculants for cowpea, it is difficult for small producers to acquire them. A nodule preparation is an inexpensive and easily prepared option for small farmers. The aim of this study was to test a nodule preparation as a low-cost inoculant, increasing cowpea seed grain production. Thus, different cowpea cultivars were compared in two locations, the municipalities of Crato and Madalena, both in Ceará. Two field experiments were performed to evaluate gain derived from this inoculation method. The nodule preparation was created from nodules removed from roots of cowpea grown in the experimental locations. The nodules were macerated and added to water, obtaining a liquid (inoculant) that was applied to the seeds. The experiment was conducted following a randomized block design with four replicates and a 4 × 3 factorial arrangement (N sources × cultivars). Results indicate little interaction between the sources of N and the cultivars because only the shoot dry matter (Crato experiment) exhibited interaction between both. The differences provided by the nodule preparation were more notable among cultivars and between the environments, Crato and Madalena. The nodule preparation differed little from the commercial inoculant; they were comparable. However, gain in relation to the zero control also proved to be reduced, indicating little contribution of the nodule preparation to cowpea under the conditions tested.
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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) | 0.001 | 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 | 0.001 |
| 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".