Sugarcane and Pine Biochar as Amendments for Greenhouse Growing Media for the Production of Bean (Phaseolus vulgaris L.) Seedlings
Bibliographic record
Abstract
Louisiana sugarcane farmers in 2016 harvested 11.7 million Mg of millable sugarcane from 163,000 ha, producing 1.47 million Mg of raw sugar and an estimated 3.5 million Mg of bagasse. Even though Louisiana sugar mills use 80% to 90% of the bagasse for fuel production, another 350,000 to 700,000 Mg of bagasse accumulates each year. The conversion of the excess bagasse into biochar is one solution to reduce the excess supply. Research was conducted to determine the impact of sugarcane biochar as an amendment to soilless planting media for the production of green bean (Phaseolus vulgaris L.) seedlings. Sugarcane bagasse biochar (SBB) and pine biochar (PB) were each combined by volume with a commercial certified organic soilless growing media into 5 combinations (0%:100%, 25%:75%, 50%:50%, 75%:25%, and 100%:0%, biochars and growing media, respectively). Green bean variety ‘Bowie’ seeds were planted in each of the different planting mixtures. The particle size distribution for the two biochars are in stark contrast to each other with the PB particle median, mean, geometric mean, and mode much greater than those of the SBB. As amendments to the soilless greenhouse growing media, the biochars (SBB and PB) functioned very well, especially at the 25% and 75% levels. The 100% SBB performed as well as the 100% commercial soilless growing media and slightly better than the 100% PB when comparing seedling fresh and dry weights. The 100% PB is not recommended as a soilless growing media even with the supplemental fertilizer used in these experiments. These results indicate that the volume of a standard soilless greenhouse growing media can be successfully extended by adding 25% to 75% SBB and PB without reducing bean seedling growth. Future research is needed to evaluate these biochars for the production of additional plant species.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".