Invasion, Impact and Control Techniques for Invasive Ipomoea hildebrandtii on Maasai Steppe Rangelands
Bibliographic record
Abstract
The ecosystem integrity of the Simanjiro Maasai steppe rangeland in Tanzania is threatened by the invasive plant Ipomoea hildebrandtii Vatke. However, its invasion status, impact and control techniques are unclear in the country. We conducted a study in Terrat and Sukuro villages in Simanjro District, Tanzania, to assess its invasion status and impact across grassland–woodland habitats using point sampling techniques. Key informant interviews and questionnaires were used to assess techniques used by the Maasai pastoralists to control I. hildebrandtii. A total of 10 plots (70 m2 each) with 9 quadrats (1 m2 each) in the invaded and non–invaded sites were established to study I. hildebrandtii invasions. The impact of I. hildebrandtii on rangelands was investigated by comparing herbage (herbaceous vegetation) species composition, richness, basal cover and biomass productivity between invaded and non–invaded plots. Results revealed that I. hildebrandtii invasion was higher in grass woodland habitats (90%) than in non-invaded plots. Non–invaded plots exhibited higher biomass productivity (0.289 ± 0.03 t DM/ha) than invaded plots (0.202 ± 0.02 t DM/ha). Furthermore, non–invaded plots had a higher basal cover (grasses: 54.71 ± 1.95%, forbs: 45.29 ± 1.95%) compared with invaded plots. We also recorded high native plants abundance in quadrats with low I. Hildebrandtii density (22.00 ± 1.36). Additionally, 81% of Maasai pastoralists reported to manually (uproot) control I. hildebrandtii. Based on the results of our study, we recommend further research and novel control techniques coupled with education to be implemented in the Simanjiro.
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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) | 0.001 | 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".