Investigating the impacts of biochar on water fluxes in a rice experiment in the dry corridor of Central America, Costa Rica
Bibliographic record
Abstract
Abstract Amending soils with biochar, a pyrolyzed organic material, is an emerging practice to potentially increase plant available water and reduce the risks associated with climatic variability in traditionally‐rainfed tropical agricultural systems. To investigate the impacts of biochar amendment on soil water storage relative to non‐amended soils, we performed an upland rice field experiment in a tropical seasonally dry region of Costa Rica consisting of plots with two different biochar amendments and a control plot. Across all plots, we collected hydrometric and isotopic data (δ18O and δ2H of rain, mobile soil, ground and rice xylem water). We observed that the soil water retention curves for biochar treated soils shifted, indicating that rice plants had 2% to 7% more water available throughout the growing season relative to the control plots and thus could withstand dry spells up to seven extra days. Furthermore, the isotopic composition of plant water in biochar and control treatments were rather similar, indicating that rice plants in different treatments likely consumed similar water. Hence, we observed that biochar amendments can stabilize water supplies for the rice plants; however, still supplemental irrigation was required to facilitate plant growth during extended dry periods. Ultimately, our findings indicate, that biochar amendments can complement, but not necessarily replace, other water management strategies to help reduce the threat of rainfall variability to rainfed agriculture in tropical regions.
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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) | 0.000 | 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 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".