Impact of Agricultural Land Use Practices on Water Quality in Lubigi Wetland
Bibliographic record
Abstract
Wetland encroachment due to agricultural land use practices (LUPs) adversely affects water quality in wetlands mainly through agricultural runoff. This study was conducted in Lubigi wetland in Uganda to (1) determine the effect of the agricultural LUPs on water quality and (2) understand the relationships between different water quality indicators and different agricultural LUPs existing in the wetland. The eight LUPs included in this study were perennial crops i.e. banana farming and sugarcane farming, annual crops i.e. maize farming and tomato farming, livestock farming which included a mixture of cows, goats, and sheep, undisturbed dense natural vegetation, reference wetland and the river. Water samples collected from the different LUPs were analyzed in the laboratory for selected physicochemical and biological water quality parameters. The water samples were analyzed for a variety of properties, including pH, electrical conductivity, total hardness, Ca-hardness, Mg- hardness, and concentrations of dissolved oxygen, calcium, magnesium, fluoride, total nitrogen, nitrates, ammonium, phosphates, potassium, total coliform, and Escherichia coli (E. coli), chemical oxygen demand, biological oxygen demand, total organic carbon, total dissolved solids, chlorides, sulphates, total phosphorus, and turbidity levels. The results suggested that the different LUPs influenced several water quality parameters. Dissolved oxygen was lowest in banana fields and the river at 2.57 mg/L and 3.44 mg/L respectively than other LUPs which limits survival of aquatic organisms. Biological oxygen demand, chemical oxygen demand and total organic carbon were high in the river water with values = 69.67 mg/L, 351.67 mg/L, 117.33 mg/L respectively indicating high organic pollution. We detected nutrient pollution in water from tomato and sugarcane fields which showed high concentrations of nitrates. The concentration of ammonium in river water (10.40 mg/L) was high, indicating nitrogen contamination. In all LUPs, Escherichia coli and total coliforms had high counts exceeding 2000 CFU/100mL which indicated elevated microbial pollution from livestock waste and agricultural runoff. Cluster analysis revealed distinct clusters influenced by varying water quality parameters. These findings show that agricultural LUPs in Lubigi wetland are significantly contributing to water pollution through microbial contamination, nutrient, and organic matter accumulation, which increases the potential for eutrophication and renders the water unsafe for drinking and domestic use. Implementing best management practices (BMPs) is essential for the sustainable use and management of the wetland.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".