Azolla Fern in Mwea Irrigation Scheme and Its Potential Nitrogen Contribution in Paddy Rice Production
Bibliographic record
Abstract
Azolla fern is invasive in Mwea Irrigation Scheme in Kenya and its management in paddy rice fields is a challenge to farmers. A survey was undertaken to establish farmers’ knowledge and potential nitrogen contribution by Azolla in the paddies. The Scheme was stratified into seven sections and a questionnaire administered to 250 farmers. Data were collected on awareness levels, source, trend of infestation, abundance, fertilizer regimes and management practices. Five farms from each of the sections were also sampled for Azolla coverage and tissue N levels analyzed. Survey data were analyzed using SPSS software and interpreted using descriptive statistics. Biomass sampling data were analyzed using SAS software and means separated using the least significant differences at P ≤ 0.05. The results demonstrated that Azolla has infested nearly all the paddy farms in Mwea. Azolla invasion occurred more than 10 years ago and coverage per unit area was on a decline and stood at 25%. Water shortage and herbicide use were the main reasons associated with this trend. Azolla is conspicuously noticed at transplanting and weeding times. The presence of Azolla in Mwea is enhanced by widespread use of P and K fertilizers and continuous paddy cropping, thus providing a suitable environment for Azolla growth. Azolla was reported to enhance soil fertility, rice yield and yield components. The maximum Azolla biomass coverage was 14.92 t/ha, with a potential nitrogen contribution of 37.6 kg N/ha. Azolla is invasive in Mwea, widespread, beneficial to paddies and with a high potential N contribution.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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".