Baseline Soil and Water Quality for Sustainable Agriculture–Aquaculture Systems in Keerom, Papua, Indonesia
Bibliographic record
Abstract
Tropical frontier regions such as Keerom Regency in Papua, Indonesia, face increasing pressure to expand food production under the National Strategic Projects (PSN) for food security. However, the absence of baseline data on soil fertility and water quality constrains the design of sustainable management practices. This study evaluated the temporal variation in soil chemical properties and aquaculture water quality to establish scientific benchmarks for site-specific interventions. Soil samples from chilli pepper (Capsicum annuum) farms were collected across resting, early growth, pre-harvest, and intercropping stages, while water samples from catfish (Clarias spp.) ponds were obtained during larval, grow-out, and harvest phases. Soil pH declined from 6.5 to 4.4, accompanied by reductions in total N, P, and K and a gradual rise in EC, indicating nutrient depletion and increasing acidity. In aquaculture ponds, DO levels decreased while Ammonium and nitrite accumulated during intensive feeding, suggesting excessive organic loading and incomplete nitrification. These results reveal critical limitations in both systems that reduce productivity and environmental resilience. The findings provide essential baseline data for nutrient management, fertilizer optimization, and water-quality control, forming a scientific foundation for future integrated agriculture–aquaculture (IAA) development to strengthen regional and national food-security initiatives in Papua.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".