Field Evaluation of a Chitosan-Based Biostimulant: Yield Gains and Photosynthetic Priming in Upland Rice
Bibliographic record
Abstract
Chitosan-based biostimulants offer a promising strategy to enhance crop resilience under environmental stress, though their physiological effects under field conditions remain poorly understood. This study evaluated FF-BR—a foliar-applied chitosan-based formulation (U.S. Patent No. 9,868,677 B2), under development, for its effects on physiological traits and grain yield in sprinkler-irrigated upland rice (Oryza sativa L.) cultivated in the Brazilian Cerrado. Foliar applications at 1.0%, 1.5%, and 2.0% (v/v) were compared to untreated controls under 48-hour irrigation intervals. FF-BR enhanced net CO2 assimilation (A), stomatal conductance (gs), and intrinsic water use efficiency (iWUE), particularly during the early photoperiod when vapor pressure deficit increased—suggesting improved stomatal regulation and hydraulic integrity. Chlorophyll a fluorescence analysis revealed elevated Y(II) and ETR, moderated NPQ, and enhanced qP at 2.0%, indicating greater photochemical efficiency. Grain yield increased by 589 kg ha-1 (10.7%) and 620 kg ha-1 (11.3%) at 1.5% and 2.0%, respectively, due to higher grain number per panicle, reduced spikelet sterility, and increased 1000-grain weight. These enhancements at the physiological level were successfully translated into superior agronomic performance, supporting the priming hypothesis under fluctuating water availability. This study provides the first field-based evidence of FF-BR’s priming capacity in rice, demonstrating its ability to improve water use efficiency, photosynthetic performance, and yield components under realistic production conditions. FF-BR shows strong potential as a climate-resilient biostimulant that promotes yield stability through enhanced physiological plasticity in sprinkler-irrigated rice systems.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".